基于交叉视觉皮质模型的图像处理关键技术研究
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摘要
本文的主题是基于交叉视觉皮质模型的图像处理关键技术研究，交叉视觉皮质模型(Intersecting Cortical Model，ICM)为单层的神经网络，它是基于20世纪70年代Eckhorn对于家猫的视觉皮层的研究成果，在综合几种视觉皮质模型的基础上，利用了生物神经元所具有的延迟特性、非线性耦合调制特性。凶此，ICM具有传统的人工神经网络所不具备的无需学习大量样本即能够进行图像处理任务的特性、并具备生物神经元所特有的延迟特性、非线性耦合调制特性。这些特征在图像噪声抑制、图像形态学处理和图像分割中较之传统的图像处理方法而言具有处理效果更好、处理速度更快的优势。所以，ICM在图像处理研究领域更具有实际的研究价值和应用价值。
ICM具备了生物神经系统中具有的信息传递延迟性和非线性耦合调制特性。ICM由于其本身直接来自于对于哺乳动物的视觉神经系统的解剖研究，相对于传统的人工神经网络模型更加接近实际的生物视觉神经网络，也更加适合面向图像处理的工作。同时，ICM还模拟了哺乳动物视神经系统的视野受到适当的刺激的时候，相邻连接神经元会同步激发35～70Hz的振荡脉冲串特性。同时，ICM还具有能够将高维数据压缩为一维时间脉冲序列的能力，这与生物实际的神经网络很相似。但是，ICM本身会产生自动波效应，这种自动波效应表现为图像中的物体的边界会随着ICM运算中的迭代而产生伪边界不断扩散的问题，这种伪边界的不断扩散效应对于后期的目标分割和识别会带来严重的干扰。在本文的研究工作之前，ICM处理的数据领域仅面向二维平面数据，对于高维数据尚无能力处理。
本文的第一个研究成果是对于ICM本身会产生自动波效应的问题进行了研究，并提出了相应的解决方法。接着，本文主要针对传统人工神经网络在图像处理中存在的需要预先进行训练才能从事相应的图像处理的问题，基于ICM对于图像处理中的几项关键技术展开探索和研究。针对图像噪声抑制、图像形态学处理和图像分割等内容提出和演进了不同的ICM，并利用这些ICM和当前国际和国内比较优秀方法在图像处理的不同任务进行了相应的实验对比并通过实验数据验证了ICM在图像处理中的高效和准确性。
本文的第二个研究成果是提出了基于ICM的图像脉冲噪声抑制机制，并取得的良好的噪声抑制效果和处理效率。
本文的第二个研究成果是提出了基于ICM的图像形态学处理机制，该机制对于后续的图像信息度量和模式识别具有特定的应用价值。
本文的第四个研究成果是针对含有高背景噪声的X光脊柱图像中的脊柱难以有效分割的问题，以及利用ICM在面向灰度图像分割时ICM的神经元的初始阈值需要手动设定的问题，提出了基于被分割图像的信息熵的最大化原则来确定ICM神经元的初始阈值，并对含有高背景噪声的X光脊柱图像达到有效的分割。
本文的第五个研究成果是突破ICM仅能面向2D数据进行处理的束缚，提出了3D-ICM，并将3D-ICM应用于彩色自然图像的自动分割，该自动分割的判定标准亦是基于被分割图像的信息熵最大原则，从而避免了最佳分割的判定需要人为干预的问题。
The research topic of this dissertation is Key Technologies of Image Processing based on Intersecting Cortical Model. Intersecting Cortical Model (ICM) is a single layer neural network. ICM is based on the Eckhorn's anatomic research result of the cat vision cortex in the 1970s'. Based on the integration of attributes from some other visual cortex models, ICM utilized the attribute of latency in information transfer in biologic neuron system and the attribute of non-linear coupling modulation. ICM boasts the attribute of processing image without training a large amount of learning sample. ICM have the attributes of latency in information transfer in biologic neuron system and non-linear coupling modulation. These attributes have the processing speed and result advantages over the traditional image processing method in image noise suppression, image morphologic operation, and image segmentation. Therefore, ICM shows great research and application value in the scope of image processing.
ICM contains two attributes, namely, the attribute of latency in information transfer in biologic neuron system and the attribute of non-linear coupling modulation. Directly inheriting the anatomic research result of the mammal's visual cortex system, ICM is closer to the actual biologic neural network and is more suitable for the image processing work. Meanwhile, ICM simulates the phenomena of synchronous spike 35 to 75 Hz oscillatory impulse stream when field of vision in mammal's visual neural system receives relevant stimulus. Moreover, ICM has the ability of compressing high dimension data into one dimension time pulse sequence, which is similar to the actual biologic vision neural network. However, ICM itself will produce the effect of auto-wave, the phenomenon of which is that the border of the object inside the image will emit the fake border in a diffused way accompanying the iteration in the ICM. The diffusion of fake object border will cause the severe interference to the post-object segmentation and recognition. Before our research work, the data that ICM can process is still 2D data; ICM has not the ability to process high dimension data.
We analyzed the cause of autowave problem in ICM and proposed solutions to autowave problem. Our research was aimed at avoiding the pre-training process in the traditional neural network in processing the image, and we proposed some key image processing technologies based on the ICM. We proposed different ICM models for image noise suppression, image morphologic operation, and image segmentation. We implemented these proposed models and compared our models with national and international outstanding methods in different image processing tasks by experiments. The experiment results showed that our models were more efficient and accurate than these methods.
We analyzed the cause of impulse noise in the image and proposed noise suppression mechanism based on ICM. The mechanism showed excellent result in noise suppression in the image contaminated by impulse noise and high efficiency in de-noise processing.
We introduced ICM into the scope of image morphology, which demonstrated high application value in post image information measurement and pattern recognition.
We solved two problems of inefficacy segmentation of spine in the X-ray gray spine image: one problem is that the image itself contains high background noise information; another problem is that the threshold of neurons in ICM must be manuallu initialized. We solved these problems by automated determination of the initial threshold value of the neurons in ICM on the basis of the maximization principle of the image segments' information entropy.
We evolved ICM into 3D-ICM, which can handle high dimension data. We applied 3D-ICM into nature color image automatic segmentation, which is also based on the maximization principle of the image segments' information entropy.
ICM具备了生物神经系统中具有的信息传递延迟性和非线性耦合调制特性。ICM由于其本身直接来自于对于哺乳动物的视觉神经系统的解剖研究，相对于传统的人工神经网络模型更加接近实际的生物视觉神经网络，也更加适合面向图像处理的工作。同时，ICM还模拟了哺乳动物视神经系统的视野受到适当的刺激的时候，相邻连接神经元会同步激发35～70Hz的振荡脉冲串特性。同时，ICM还具有能够将高维数据压缩为一维时间脉冲序列的能力，这与生物实际的神经网络很相似。但是，ICM本身会产生自动波效应，这种自动波效应表现为图像中的物体的边界会随着ICM运算中的迭代而产生伪边界不断扩散的问题，这种伪边界的不断扩散效应对于后期的目标分割和识别会带来严重的干扰。在本文的研究工作之前，ICM处理的数据领域仅面向二维平面数据，对于高维数据尚无能力处理。
本文的第一个研究成果是对于ICM本身会产生自动波效应的问题进行了研究，并提出了相应的解决方法。接着，本文主要针对传统人工神经网络在图像处理中存在的需要预先进行训练才能从事相应的图像处理的问题，基于ICM对于图像处理中的几项关键技术展开探索和研究。针对图像噪声抑制、图像形态学处理和图像分割等内容提出和演进了不同的ICM，并利用这些ICM和当前国际和国内比较优秀方法在图像处理的不同任务进行了相应的实验对比并通过实验数据验证了ICM在图像处理中的高效和准确性。
本文的第二个研究成果是提出了基于ICM的图像脉冲噪声抑制机制，并取得的良好的噪声抑制效果和处理效率。
本文的第二个研究成果是提出了基于ICM的图像形态学处理机制，该机制对于后续的图像信息度量和模式识别具有特定的应用价值。
本文的第四个研究成果是针对含有高背景噪声的X光脊柱图像中的脊柱难以有效分割的问题，以及利用ICM在面向灰度图像分割时ICM的神经元的初始阈值需要手动设定的问题，提出了基于被分割图像的信息熵的最大化原则来确定ICM神经元的初始阈值，并对含有高背景噪声的X光脊柱图像达到有效的分割。
本文的第五个研究成果是突破ICM仅能面向2D数据进行处理的束缚，提出了3D-ICM，并将3D-ICM应用于彩色自然图像的自动分割，该自动分割的判定标准亦是基于被分割图像的信息熵最大原则，从而避免了最佳分割的判定需要人为干预的问题。
The research topic of this dissertation is Key Technologies of Image Processing based on Intersecting Cortical Model. Intersecting Cortical Model (ICM) is a single layer neural network. ICM is based on the Eckhorn's anatomic research result of the cat vision cortex in the 1970s'. Based on the integration of attributes from some other visual cortex models, ICM utilized the attribute of latency in information transfer in biologic neuron system and the attribute of non-linear coupling modulation. ICM boasts the attribute of processing image without training a large amount of learning sample. ICM have the attributes of latency in information transfer in biologic neuron system and non-linear coupling modulation. These attributes have the processing speed and result advantages over the traditional image processing method in image noise suppression, image morphologic operation, and image segmentation. Therefore, ICM shows great research and application value in the scope of image processing.
ICM contains two attributes, namely, the attribute of latency in information transfer in biologic neuron system and the attribute of non-linear coupling modulation. Directly inheriting the anatomic research result of the mammal's visual cortex system, ICM is closer to the actual biologic neural network and is more suitable for the image processing work. Meanwhile, ICM simulates the phenomena of synchronous spike 35 to 75 Hz oscillatory impulse stream when field of vision in mammal's visual neural system receives relevant stimulus. Moreover, ICM has the ability of compressing high dimension data into one dimension time pulse sequence, which is similar to the actual biologic vision neural network. However, ICM itself will produce the effect of auto-wave, the phenomenon of which is that the border of the object inside the image will emit the fake border in a diffused way accompanying the iteration in the ICM. The diffusion of fake object border will cause the severe interference to the post-object segmentation and recognition. Before our research work, the data that ICM can process is still 2D data; ICM has not the ability to process high dimension data.
We analyzed the cause of autowave problem in ICM and proposed solutions to autowave problem. Our research was aimed at avoiding the pre-training process in the traditional neural network in processing the image, and we proposed some key image processing technologies based on the ICM. We proposed different ICM models for image noise suppression, image morphologic operation, and image segmentation. We implemented these proposed models and compared our models with national and international outstanding methods in different image processing tasks by experiments. The experiment results showed that our models were more efficient and accurate than these methods.
We analyzed the cause of impulse noise in the image and proposed noise suppression mechanism based on ICM. The mechanism showed excellent result in noise suppression in the image contaminated by impulse noise and high efficiency in de-noise processing.
We introduced ICM into the scope of image morphology, which demonstrated high application value in post image information measurement and pattern recognition.
We solved two problems of inefficacy segmentation of spine in the X-ray gray spine image: one problem is that the image itself contains high background noise information; another problem is that the threshold of neurons in ICM must be manuallu initialized. We solved these problems by automated determination of the initial threshold value of the neurons in ICM on the basis of the maximization principle of the image segments' information entropy.
We evolved ICM into 3D-ICM, which can handle high dimension data. We applied 3D-ICM into nature color image automatic segmentation, which is also based on the maximization principle of the image segments' information entropy.
引文
[1] Sharon B, Wells R B. VLSI implementation of neuromime pulse generator for Eckhorn neurons [J]. Electronics Letters, 2004, 40(18): 1143-1144.
[2] Eckhorn R. Neural mechanisms of scene segmentation: recordings from the visual cortex suggest basic circuits for linking field models [J]. IEEE Transactions on Neural Networks, 1999, 10(3): 464-479.
[3] Eckhorn R, Bauer R, Jordan W. Coherent oscillations: A mechanism of feature linking in the visual cortex? [J]. Biological Cybernetics, 1988, 60 (2): 121-130.
[4] Eckhorn R, Frien A, Bauer R. High frequency (60-90 Hz) oscillations in primary visual cortex of awake monkey [J]. Neuroreport, 1993, 4(3): 243-246.
[5] Eckhorn R. Oscillatory and non-oscillatory synchronizations in the visual cortex and their possible roles in associations of visual features [J]. Progress in Brain Research, 1994, 102: 405-426.
[6] 余群明，王耀南．基于振荡型混沌神经网络的智能信息处理研究[J]．自动化学报，2002，28(3)：401-407．
[7] 焦贤发，王如彬．刺激下可变耦合神经振子群活动的非线性随机演化模型[J]．控制与决策，2005，20(8)：897-900．
[8] 汪云九，唐孝威，吴建永．意识的计算神经科学研究[J]．科学通报，2001，46(13)：1140-1144．
[9] 张建州，张宇．外积取等联想记忆神经网络部分同步收敛性[J]．系统工程与电子技术，2002，24(11)：19-21．
[10] 石美红，张军英，李永刚．基于差别特征的纹理图像识别研究[J]．计算机应用，2004，24(1)：66-69．
[11] Ekblad U, Kinserb J M, Atmera J. The intersecting cortical model in image processing [J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2004, 525(1): 392-396.
[12] Ekblad U, Kinser J M. Theoretical foundation of the intersecting cortical model and its use for change detection of aircraft, cars, and nuclear explosion tests[J]. Signal Processing, 2004, 84(7): 1131-1146.
[13] Ekblad U, Kinserb J M, Atmer J, et ai. Image information content and extraction techniques[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2004, 525(2): 397-401.
[14] 黄英，王成波．生物电及其在医学中的应用[J]．局解手术学杂志，2002，11(4)：389-399．
[15] 伍国锋，贵阳，张文渊．脑电波产生的神经生理机制[J]．临床脑电学杂志，2000，9(3)：188-190．
[16] Cai S P, Shen J, Doi T. Optical mapping of brainstem neuronal activity evoked by auditory electro-stimulation in rats [J]. Chinese Journal of Clinical Rehabilitation, 2005, 9(29): 219-222.
[17] McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity[J]. Bulletin of Mathematical Biophysics, 1943, 5: 115-133.
[18] Hebb D O. The Organization of Behavior [M]. New York: Wiley, 1949.
[19] Rosenblatt F. The perception: A probabilistic model for information storage and organization in the brain [J]. Psychological Review, 1958, 65: 386-408.
[20] Widrow B, Hoff M E. Adaptive switching circuits[A]. In: Neurocomputing: foundations of research [M]. Cambridge, MA, USA: MIT Press, 1988: 123-134.
[21] Minsky M, Papert S.Perceptions [M]. Cambridge MA: MIT Press, 1969.
[22] Kohonen T. Correlation matrix memories[A]. In: Neurocomputing: foundations of research[M]. Cambridge, MA, USA: MIT Press, 1988: 171-180.
[23] Anderson J A. A simple neural network generating an interactive memory[J]. Mathematical Biosciences, 1972, 14: 197-220.
[24] Grossberg S. Adaptive pattern classification and universal recoding: Ⅰ. Parallel development and coding of neural feature detectors [J]. Biological Cybernetics, 1976, 23: 121-134.
[25] Hopfleld J J. Neural networks and physical systems with emergent collective computational properties[A]. In: Proceedings of the National Academy of Sciences[C], 1982,79: 2554-2558.
[26] Rumelhart D E, McClelland J L. Parallel Distributed Processing: Explorations in the Microstructure of Cognition [M]. Cambridge, MA: MIT Press, 1986, 1.
[27] Pawlak Z. Rough Sets [J]. Communication of ACM, 1995, 38(11): 89-95.
[28] Eckhorn R, Reitboeck H J, Arndt M, et al. Feature linking via stimulus-evoked oscillations: experimental results from cat visual cortex and functional implications from a network model[A]. In: International Joint Conference on Neural Networks[C]. Washington DC, 1989: 723-730.
[29] Ekblad U, Kinser J M. Theoretical foundation of the intersecting cortical model and its use for change detection of aircraft, cars and nuclear explosion tests [J]. Signal Processing, 2004, 84(7): 1131-1146.
[30] Ekblad U, Kinser J M, Atmer J, et al. The intersecting cortical model in image processing [J]. Nuclear Instruments & Methods In Physics Research, Section A, 2004, 525(2): 392-396.
[31] 赵卫东, 李旗号. 粗集在数据开采中的应用[J]. 系统工程学报, 2002, 17(4): 575-579.
[32] Jelonek J. Rough Set Reducts of Attributes and Their Domains for Neural Networks[J]. Computational Intelligence, 1995, 11(2) :339-347.
[33] Szczuka M S. Rough Set Methods for Constructing Artifical Neural Network[J]. American Society of Mechanial Engineers, 1996,79(7): 1203-1206.
[34] Banerjee M. Rough Fuzzy MLP: Knowledge Encoding and Classification[J]. IEEE Transactions On Neural Networks, 1998,9(6): 1203-1206.
[35] Cornfield J. Statistical classification methods[A]. In: Proceedings of the Second Conference on the Diagnostic Process, Computer Diagnosis and Diagnostic Methods[C]. Chicago, 1972: 108-130.
[36] Devijver P A, Kittler J. Pattern Recognition, a Statistical Approach[M].Englewood Cliffs, London: Prentice Hall, 1982.
[37] Fukunaga K. Introduction to Statistical Pattern Recognition, 2nd Edition[M]. New York: Academic Press, 1990.
[38] Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation[A], In: Rumelhart D E. Parallel Distributed Processing: Explorations in the microstructure of Cognition Vol. I [C]. Cambridge: MIT Press, 1986:319-362.
[39] Pal N R, Pal S K. A review on image segmentation techniques[J]. Pattern Recognition, 1993, 26(9): 1277-1294.
[40] Adler A, Guardo R. A neural network image reconstruction technique for electrical impedance tomography[J]. IEEE Transaction on Medical Imaging, 1994, 13(4): 594-600.
[41] Srinivasan V, Han Y K, Ong S H. Image reconstruction by a Hopfield neural network[J]. Image Vision Computing, 1993, 11(5): 278-282.
[42] Meyer R R, Heindl E. Reconstruction of off-axis electron holograms using a neural net[J]. Journal of Microscopy, 1998, 191(1): 52-59.
[43] Wang Y M, Wahl F M. Vector-entropy optimization-based neural-network approach to image reconstruction from projections[J].IEEE Transaction on Neural Networks, 1997, 8(5): 1008-1014.
[44] Greenhil D, Davies E R. Relative effectiveness of neural networks for image noise suppression[A]. In: Proceedings of the Pattern Recognition in Practice IV[C]. Vlieland, 1994:367-378.
[45] Ridder D, Duin R P, Verbeek P W. The applicability of neural networks to nonlinear image processing[J]. Pattern Analysis & Applications, 1999, 2(2): 111 -128.
[46] Chua W, Yang L. Cellular networks: theory [J]. IEEE Trans. Circuits Systems, 1988, 35(10): 1257-1272.
[47] Hanek H, Ansari N. Speeding up the generalized adaptive neural filters[J]. IEEE Trans. Image Process, 1996, 5(5):705-712.
[48] Russo F. Hybrid neuro-fuzzy filter for impulse noise removal[J]. Pattern Recognition, 1999, 32(11): 1843-1855.
[49] Matsumoto T, Kobayashi H, Togawa Y. Spatial versus temporal stability issues in image processing neuro chips[J]. IEEE Trans. Neural Networks, 1992,3 (4): 540-569.
[50] Bedini L, Tonazzini A. Image restoration preserving discontinuities: the Bayesian approach and neural networks[J]. Image Vision Comput.,1992,10(2):108-118.
[51] Chandrasekaran V, Palaniswami M, Caelli T M. Range image segmentation by dynamic neural network architecture[J]. Pattern Recognition, 1996, 29(2):315-329.
[52] Pugmire R H, Hodgson R M, Chaplin R I. The properties and training of a neural network based universal window filter developed for image processing tasks[A]. In: S. Amari, N. Kasabov (Eds.), Brain-like computing and intelligent information systems[C], Springer-Verlag, Singapore, 1998:49-77.
[53] Shih F Y, Moh J, Chang F C. A new ART-based neural architecture for pattern classification and image enhancement without prior knowledge [J]. Pattern Recognition, 1992, 25(5): 533-542.
[54] Amerijckx C, Verleysen M, Thissen P. Image compression by self-organized Kohonen map[J]. IEEE Trans. Neural Networks, 1998,9(3):503-507.
[55] Daugman J G Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression[J]. IEEE Trans. Acoustics, Speech Signal Process, 1988, 36(7): 1169-1179.
[56] Brause R W, Rippl M. Noise suppressing sensor encoding and neural signal orthonormalization[J]. IEEE Trans. Neural Networks, 1998, 9(4):613-628.
[57] Amerijckx C, Verleysen M, Thissen P. Image compression by self-organized Kohonen map[J]. IEEE Trans. Neural Networks, 1998, 9 (3):503-507.
[58] Mitra S, Yang S Y. High fidelity adaptive vector quantization at very low bit rates for progressive transmission of radiographic images[J]. J. Electron. Imaging, 1999, 8(1):23-35.
[59] Morris R J T, Rubin L D, Tirri H. Neural network techniques for object orientation detection: solution by optimal feedforward network and learning vector quantization approaches[J]. IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12 (11): 1107-1115.
[60] Dony R D, Haykin S. Optimally adaptive transform coding[J]. IEEE Trans. Image Process., 1995, 4(10): 1358-1370.
[61] Oja E. A simplified neuron model as a principal component analyzer[J].J. Math. Biol., 1982,15(3):267-273.
[62] Baldi P, Hornik J. Neural networks and principal component analysis: learning from examples without local minima[J]. Neural Networks,1989,2(1):53-58.
[63] Kepuska V Z, Mason S O. A hierarchical neural network system for signalized point recognition in aerial photographs[J]. Photogrammetric Eng. Remote Sensing, 1995, 61(7):917-925.
[64] Shustorovich A. A subspace projection approach to feature extraction-the 2-D Gabor transform for character recognition[J]. Neural Networks, 1994,7(8): 1295-1301.
[65] Patel D, Davies E R, Hannah I. The use of convolution operators for detecting contaminants in food images[J]. Pattern Recognition, 1996, 29(6): 1019-1029.
[66] Glass J O, Reddick W E. Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma[J]. Magn. Resonance 2 Imaging. 1998,16 (9): 1075-1083.
[67] Fukumi M, Omatu S, Nishikawa Y. Rotation-invariant neural pattern recognition system estimating a rotation angle[J]. IEEE Trans. Neural Networks, 1997, 8(3):568-581.
[68] Williams CKI, Revow M, Hinton G E. Instantiating deformable models with a neural net[J]. Comput. Vision Image Understand,1997,68(1):120-126.
[69] Hall L O, Bensaid A M, Clarke L P. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain[J]. IEEE Trans. Neural Networks, 1992,3(5):672-682.
[70] Handels H, Busch C, Encarnacao J. KAMEDIN: A telemedicine system for computer supported cooperative work and remote image analysis in radiology [J]. Comput. Methods Programs Biomed., 1997,52(3):175-183.
[71] Manjunath B S, Simchony T, Chellappa R. Stochastic and deterministic networks for texture segmentation[J]. IEEE Trans. Acoustics, Speech Signal Process., 1990,38(6): 1039-1049.
[72] DeKruger D, Hunt B R. Image processing and neural networks for recognition of cartographic area features[J]. Pattern Recognition, 1994,27(4):461-483.
[73] Laine A, Fan J. Texture classification by wavelet packet signatures[J]. IEEE Trans.Pattern Anal. Mach. Intell., 1993, 15(11): 1186-1191.
[74] Raghu P P, Poongodi R, Yegnanarayana B. Unsupervised texture classification using vector quantization and deterministic relaxation neural network[J]. IEEE Trans. Image Process., 1997, 6(10):1376-1387.
[75] Haring S, Viergever M A, Kok J N. Kohonen networks for multiscale image segmentation[J]. Image Vision Comput, 1994, 12(6): 339-344.
[76] Petersen M E, Arts T. Recognition of radiopaque markers in X-ray images using a neural network as nonlinear filter[J]. Pattern Recognition Letter, 1999,20(5):521-533.
[77] Carpenter G A, Grossberg S, Lesher G W. The what-and-where filter-a spatial mapping neural network for object recognition and image understanding[J]. Comput. Vision Image Understand, 1998, 69(1): 1-22.
[78] Shen J Y, Zhang Y X, Mu G G. Optical pattern recognition system based on a winner-take-all model of a neural network[J]. Opt. Eng., 1992, 32(5): 1053-1056.
[79] Penedo M G, Carreira M J, Mosquera A. Computer-aided diagnosis: a neural-network-based approach to lung nodule detection[J]. IEEE Trans. Med. Imag., 1998, 17(6):872-880.
[80] Watanabe M. Reward expectancy in primate prefrontal neurons [J]. Nature, 1996, 382: 521-535.
[81] Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. Journal of Physiology, 1962, 160: 106-154.
[82] Schwartz E L, Rojer A S. Cortical hypercolumns and the topology of random orientation maps [J]. Pattern Recognition, 1994, 2: 150-155.
[83] Rodieck R W, Stone J. Analysis of receptive fields of cat retinal ganglion cells [J]. Journal of Neurophysiology, 1965, 28: 833-849.
[84] Kruizinga Peter, Nikolay Petkov. Nonlinear Operator for Oriented Texture [J]. IEEE Transactions on Image Processing, 1999,8(10): 1395-1407.
[85] Yuen P C, Feng G C, Zhou J P. A contour detection method: Initialization and contour model[J]. Pattern Recognition Letters, 1999,20(8): 141-148.
[86] 杨谦，齐翔林，汪云九．简单细胞方位选择性感受野组织形成的神经网络模型[J]．中国科学(C辑)，2000，30(4)：412-420．
[87] 杨谦，齐翔林．视觉皮层复杂细胞时空编码特性[J]．生物物理学报，2000，16(2)：280-287．
[88] 李速，齐翔林，胡宏，汪云九．功能柱结构神经网络模型中的同步振荡现象[J]．中国科学(C辑)，2004，34(4)：385-394．
[89] 杨谦，齐翔林，汪云九．视皮层V1区简单细胞的稀疏编码策略[J]．计算物理，2001，18(2)：143-146
[90] Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve[J]. Journal of Physiology, 1952, 117:500-544.
[91] FitzHugh R, Impulses and phsyiological states in theoretical models of nerve membrane[J]. Biophysics J. , 1961,1: 445-466.
[92] Eckhom R, Reitboeck H J, Amdt M. Feature linking via synchronization among distributed assemblies: Simulations of results from Cat Visual Cortex[J]. Neural Computing, 1990,2:293-307.
[93] Labbi A,Milanese R,Bosch H. A network of FitzHugh-Nagumo oscillators for object segmentation[A]. Proc. of International Symposium on Nonlinear Theory and Applications, NOLTA'97[C],Hawaii, 1997: 581-584.
[94] Rybak I A, Shevtsova N A, Sandler V A. The model of a Neural Network visual processor[J]. Neurocomputing, 1992 4:93-102.
[95] Young M P. The large scale organization of the primate cortical visual system[A]. SPIE[C], 1994, 2054: 185-193.
[96] Gray C M. Oscillatory Responses in Cat Visual Cortex Exhibit Inter-Columnar Synchronization Which Reflects Global Stimulus Properties[J]. Nature, 1989, 555:334-337.
[97] Neven H, Aertsen A. Rate coherence and event coherence in the visual cortex: a neuronal model of object recognition[J]. Biological Cybernetics, 1992, 57:309-322.
[98] Eckhorn R. Feature linking via stimulus-evoked oscillations: experimental results from cat visual cortex and functional implications from a network model[A]. IJCNN: International Joint Conference on Neural Networks[C],1989:723-730.
[99] 徐志平，钟亦平，张世永．用于脉冲噪声图像的交叉视觉皮质模型滤波[J]．计算机辅助设计与图形学学报，2007，19卷，第6期(国内权威，EI索引，已录用)
[100] 卡斯尔曼．数字图像处理[M]．北京：清华大学出版社，1998：414-416．
[101] 陈贺新．非线性滤波与数字图像处理[M]．北京：国防工业出版社，1997．
[102] 赵春晖，王伟．全方位多结构元形态滤波器．中国图像图形学报，1997．2(8)：629-633．
[103] 崔屹，图像处理与分析数学形态学方法及应用[M]．北京：科学出版社，2000：126-149．
[104] 王伟，赵春晖，孙圣和．最优全方位结构元约束层叠滤波[J]．中国图像图形学报，1999，4(6)：445-449．
[105] 王伟，赵春晖，孙圣和．基于层叠处理的多机自适应WOS滤波器[J]．中国图像图形学报，2000，5(8)：670-683．
[106] 张薇，沈允春，张曙．堆栈滤波的递归实现[J]．中国图像图形学报，2000，5(10)：851-856．
[107] 黄贤武，王加俊，李家华．数字图像处理与压缩编码技术[M]．成都：电子科技大学出版社，2000．12．
[108] Akopian D, Vainio O, Agaian S, et al. Processors for generalized stack filters[J]. IEEE Trans Signal Processing, 1995, 43(6): 1541-1546.
[109] Kuosmanen P, Astola J. Optimal stack filters under rank selection and structural constraints[J]. Signal Processing,1995,41:309-338
[110] Farina A. Linear and non-linear filters for clutter cancellation in radar systems.[J], SIGNAL Processing, 1997, 59(1): 101-112.
[111] Pasian E Crise A. Restoration of signals degraded by impulse noise by means of a low-distortion non-linear filter[J], Signal Processing, 1984, 6(1): 67-76.
[112] Henriksen R. The truncated 2nd-order non-linear filter revisited[J]. IEEE Transactions on automatic control, 1982, 27(1): 247-251.
[113] 梁雯，刘松林．改进的中心加权中值滤波[J]．中国图像图形学报，1997，2(8，9)：629-633．
[114] 冯星奎，肖兴明，尹洪君．方向加权中值滤波算法[J]．中国图像图形学报，2000，5(7)：609-611．
[115] 马义德，张在峰，张祥光．基于全方位结构元层叠滤波的混合滤波器[J]．甘肃科学学报，2002，14(2)：56-60．
[116] 马义德，张祥光，戴若兰，李廉．一种改进的最优全方位结构元约束层叠滤波设计方案[J]．兰州大学学报(自然科学版)，2001，37(3)：52-55．
[117] 黄煦涛．二维数字信号处理—变换与中值滤波[M]．北京：科学出版社，1985．
[118] 赵春晖，乔景洵，孙圣和．一类多结构元自适应广义形态滤波器[J]．中国图像图形学报，1997，2(11)：806-809．
[99] 徐志平，钟亦平，张世永．用于脉冲噪声图像的交叉视觉皮质模型滤波[J]．计算机辅助设计与图形学学报，2007，19卷，第6期(国内权威，EI索引，已录用)
[119] 徐志平，钟亦平，张世永．基于交叉视觉皮质模型的脉冲噪声红外图像滤波算法[J]．小型微型计算机系统(国内权威，已录用)
[120] Power W, Clist R. Comparison of supervised learning techniques applied to color segmentation of fruit image[A]. In: Proceeding of SPIE, Intelligent Roberts and Computer Vision. ⅩⅤ: Algorithms, Techniques, Active Vision, and Material Handing[C]. Boston, MA, USA, 1996: 370-381.
[121] Hanee G A, Umbaugh S E, Moss R H,et al.Unsupervised color image segmentation with application to skin tumor borders[J]. IEEE Engineering inMedicine and Biology, 1996,15(1): 104-111.
[122] Pal N R, Pal S K. A review on image segmentation techniques [J]. Pattern Recognition, 1993, 26(9): 1277-1294.
[123] Ohlander R, Price K, Reddy D R. Picture segmentation using a recursive region splittingmethod [J]. ComputerGraphics and Image Processing, 1978,8(3): 313-333.
[124] Guo G D,Yu S, Ma S D. Unsupervised segmentation of color images [A]. In: Proceeding of 1998 IEEE International Conference on Image Processing[C]. Chicago, IL, USA, 1998: 299-302.
[125] Underwood S A, Aggarwal J K. Interactive computer analysis of aerial color infrared photographs[J]. Computer Graphics and Image Processing, 1977,6(1): 1-24.
[126] Kurugollu F, Sankur B, Harmanci A E. Color image segmentation using histogram multithresholding and fusion [J]. Image and Vision Computing, 2001,19(13): 915-928.
[127] Celenk M. A color clustering technique for image segmentation[J]. ComputerVision, Graphics, and Image Processing, 1990, 52(2): 145-170.
[128] Ahuja N, Haralick R M, Rosenfeld A. Neighbor gray levels as features in pixel classification[J]. Pattern Recognition, 1980, 13(4): 251-260.
[129] 刘重庆，程华．分割彩色图像的一种有效聚类方法[J]．模式识别与人工智能，1995，8(A01)：133-138．
[130] Carevic D, Caelli T. Region-based coding of color image using Karhunen-Loeve Transform[J]. Graphics Models and Image Processing, 1997,59(1): 27-38.
[131] 王兴伟，沈兰荪，卫保国等．基于改进的K-均值聚类和数学形态学的彩色眼科图像病灶分割[J]．中国生物医学工程学报，2002，21(5)：443～448．
[132] Bezdek J C. Pattern recognition with fuzzy objective function algorithms [M]. New York: Plenum Press, 1981.
[133] Xie X L, BeniG. A validitymeasure for fuzzy clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(8): 841-847.
[134] Zahid N, LimouriM, Esseaid A. A new cluster-validity for fuzzy clustering [J]. Pattern Recognition, 1999,32(5): 1089-1097.
[135] Ferri F, Vidal E. Color image segmentation and labeling through multi-edit condensing[J]. PatternRecognition Letters, 1992,13(8): 561-568.
[136] Lim Y W, Lee S U. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques [J]. Pattern Recognition, 1990,23(9): 935-952.
[137] 刘健庄，谢维信．高效的彩色图像塔形模糊聚类分割方法[J]．西安电了科技大学学报，1993，20(1)：40-46．
[138] 林开颜，徐立鸿，吴军辉．快速模糊C均值聚类彩色图像分割方法[J]．中国图象图形学报， 2004,9(2): 159-163.
[139] Chen T Q, Lu Y. Color image segmentation—An innovative approach[J]. Pattern Recognition, 2002,35(2): 395-405.
[140] Michael T U, Arbib A. Color image segmentation using competitive learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994,16(12): 1197-1206.
[141] Ohta Y, Kanade T, Sakai T. Color information for region segmentation[J]. Computer Graphics and Image Processing, 1980, 13(3): 222-241.
[142] Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations[J]. IEEE Transactions on PatternAnalysis andMachine Intelligence, 1991,13(6): 583-598.
[143] Lezoray O, Cardot H. Cooperation of color pixel classification schemes and color watershed: a study for microscopic images[J]. IEEE Transactions on Image Processing, 2002,11(7): 783-789.
[144] Shafarenko L, Petrou M, Kittler J. Automatic watershed segmentation of randomly textured color images[J]. IEEE Transactions on Image Processing, 1997,6(11): 1530-1544.
[145] Shiji A, Hamada N. Color image segmentation method using watershed algorithm and contour information[A]. In: Proceeding of 1999 IEEE International Conference on Image Processing[C]. Kobe, Japan, 1999: 305-309.
[146] 马丽红，张宇，邓健平．基于形态开闭滤波二值标记和纹理特征合并的分水岭算法[J]．中国图象图形学报，2003，8(1)：77-83．
[147] Geman S, Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984,6(11): 721-741.
[148] Celeux G, Forbes F, Peyrard N. EM procedures using mean field-like approximations forMarkovmodel-based image segmentation [J]. Pattern Recognition 2003,36(1): 131-144.
[149] Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm [J]. IEEE Transactions on Medical Image, 2001,20(1): 45-57.
[150] Huang C L. Parallel image segmentation using modified Hopfield model [J]. Pattern Recognition Letters, 1993, 13(5): 345-353.
[151] Campadelli P, Medici D, Schettini R. Color image segmentation usingHopfield networks [J]. Image and Vision Computing, 1997, 15(3): 161-166.
[152] Sammouda M, Sammouda R, Niki N, et al.Segmentation and analysis of liver cancer pathological color image based on artificial neural networks [A]. In: Proceeding of IEEE 1999 International Conference on Image Processing [C]. Kobe, Japan, 1999: 392-396.
[153] Sammouda R, Sammouda M. Improving the performance of Hopfield neural network to segment pathological liver color images [J]. International Congress Series, 2003, 1256: 232-239.
[154] Ong S H, Yeo N C, Lee K H, et al.Segmentation of color images using a two-stage self-organizing network [J]. Image and Vision Computing, 2002,20(4): 279-289.
[155] Papamarkos N, Strouthopoulous C, Andreadis I. Multithresholding of color and gray-level images through a neural network technique[J]. Image and Vision Computing, 2000,18(3): 213-222.
[156] Lescure P, Yedid V M, DupoisotH,et al.Color segmentation on biological microscope images[A]. In: Proceeding of SPIE, Application of Artificial NeuralNetworks in Image Processing Ⅳ[C]. San Jose, California, USA, 1999: 182-193.
[157] Ozden M, Polat E. A color image segmentation approach for content-based image retrieval[J]. Pattern Recognition, 2007, 40: 1318-1325.
[158] Zhiping XU, Yiping ZHONG, Shiyong ZHANG, Fast Shape Index Framework based on Principle Component Analysis using Edge Co-occurrence Matrix,Lecture Notes AI, 2006,4253: 390-397. (SCI索引，EI索引)
[159] Zhiping XU, Shiyong ZHANG, Yiping ZHONG. A Novel Image Semantic Block Clustering Method based on Artificial Visual Cortical Responding Model[A]. In: 2007 IEEE Symposium on Computational Intelligence and Data Mining[C], April 1-5, 2007,Honolulu, Hawaii, USA(EI索引，已录用)
[160] Zhiping XU, Yiping ZHONG, Shiyong ZHANG, Artificial Visual Cortical Responding Model in Image Semantic Processing[A]. In: 2007 ACS/IEEE International Conference on Computer Systems and Applications[C]. (SCI索引，EI索引，已录用)
[161] Zhiping Xu, Jinghong Pan, Shiyong Zhang. A Novel Automatic Framework for Scoliosis X-Ray Image Retrieval[A]. In: 2007 International Joint Conderence on Neural Networks, IJCNN 2007[C]. (SCI索引，EI索引，已录用)
[162] Bovik A L. Handbook of Image And Video Processing [M]. New York: Academic Press, 2000.
[163] 杨波，汪同庆，吕永平，等．利用动态结构元素提取直线[J]．计算机辅助设计与图形学学报，2003，15(4)：421-424．
[164] 岳洪伟，李扬，蔡肯，等．数学形态学在图像处理中的应用与展望[J]．影像技术，2006．2：19-21．
[165] Kenneth.R.Castleman Digital Image Processing Printice-Hall,Inc[M].北京：清华大学出版社，1998，4．
[166] 颜七笙，王士同．调节形态学运算及其神经网络实现[J]．计算机工程与设计，2007，28(1)：115-117．
[167] 赵于前，桂卫华，陈真诚．基于自适应数学形态学的医学图像边缘连接[J]．计算机工程，2006，32(22)：17-19．
[168] 赵鹏，浦昭邦．基于形态学4子带分解金字塔的图像融合[J]．光学学报，2007，27(1)：39-44．
[169] 阚江明，李文彬．基于数学形态学的树木图像分割方法[J]．北京林业大学学报，2006，28(2)：151-156．
[170] 徐志平，张世永，钟亦平．基于交叉视觉皮质模型的图像形态学处理研究[J]．计算机辅助设计与图形学学报．2007，19卷，第8期(国内权威，EI索引，已录用)
[1] Sharon B, Wells R B. VLSI implementation of neuromime pulse generator for Eckhorn neurons [J]. Electronics Letters, 2004, 40(18): 1143-1144.
[2] Eckhorn R. Neural mechanisms of scene segmentation: recordings from the visual cortex suggest basic circuits for linking field models [J]. IEEE Transactions on Neural Networks, 1999, 10(3): 464-479.
[3] Eckhom R, Bauer R, Jordan W. Coherent oscillations: A mechanism of feature linking in the visual cortex? [J]. Biological Cybernetics, 1988, 60 (2): 121-130.
[4] Eckhorn R, Frien A, Bauer R. High frequency (60-90 Hz) oscillations in primary visual cortex of awake monkey [J]. Neurorepon, 1993, 4(3): 243-246.
[5] Eckhorn R. Oscillatory and non-oscillatory synchronizations in the visual cortex and their, possible roles in associations of visual features [J]. Progress in Brain Research, 1994, 102: 405-426.
[6] 余群明，王耀南．基于振荡型混沌神经网络的智能信息处理研究[J]．自动化学报，2002，28(3)：401-407．
[7] 焦贤发，王如彬．刺激下可变祸合神经振了群活动的非线性随机演化模型[J]．控制与决策，2005，20(8)：897-900．
[8] 汪云九，唐孝威，吴建永．意识的计算神经科学研究[J]．科学通报，2001，46(13)：1140-1144．
[9] 张建州，张宇．外积取等联想记忆神经网络部分同步收敛性[J]．系统工程与电子技术，2002，24(11)：19-21．
[10] 石美红，张军英，李永刚．基于差别特征的纹理图像识别研究[J]．计算机应用，2004，24(1)：66-69．
[11] Ekblad U, Kinserb J M, Atmera J. The intersecting cortical model in image processing [J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2004, 525(1): 392-396.
[12] Ekblad U, Kinser J M. Theoretical foundation of the intersecting conical model and its use for change detection of aircraft, cars, and nuclear explosion tests[J]. Signal Processing, 2004, 84(7): 1131-1146.
[13] Ekblad U, Kinserb J M, Atmer J, et al. Image information content and extraction techniques[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2004, 525(2): 397-401.
[14] 黄英，王成波．生物电及其在医学中的应用[J]．局解手术学杂志，2002，11(4)：389-399．
[15] 伍国锋，贵阳，张文渊．脑电波产生的神经生理机制[J]．临床脑电学杂志，2000，9(3)：188-190．
[16] Cai S P, Shen J, Doi T. Optical mapping of brainstem neuronal activity evoked by auditory electro-stimulation in rats [J]. Chinese Journal of Clinical Rehabilitation, 2005, 9(29): 219-222.
[17] McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity[J]. Bulletin of Mathematical Biophysics, 1943, 5: 115-133.
[18] Hebb D O. The Organization of Behavior [M]. New York: Wiley, 1949.
[19] Rosenblatt F. The perception: A probabilistic model for information storage and organization in the brain [J]. Psychological Review, 1958, 65: 386-408.
[20] Widrow B, Hoff M E. Adaptive switching circuits[A]. In: Neurocomputing: foundations of research [M]. Cambridge, MA, USA: MIT Press, 1988:123-134.
[21] Minsky M, Papert S.Perceptions [M]. Cambridge MA: MIT Press, 1969.
[22] Kohonen T. Correlation matrix memories[A]. In: Neurocomputing: foundations of research [M]. Cambridge, MA, USA: MIT Press, 1988: 171-180.
[23] Anderson J A. A simple neural network generating an interactive memory[J]. Mathematical Biosciences, 1972, 14: 197-220.
[24] Grossberg S. Adaptive pattern classification and universal recoding: Ⅰ. Parallel development and coding of neural feature detectors [J]. Biological Cybernetics, 1976, 23: 121-134.
[25] Hopfleld J J. Neural networks and physical systems with emergent collective computational properties[A]. In: Proceedings of the National Academy of Sciences[C], 1982,79: 2554-2558.
[26] Rumelhart D E, McClelland J L. Parallel Distributed Processing: Explorations in the Microstructure of Cognition [M]. Cambridge, MA: MIT Press, 1986, 1.
[27] Pawlak Z. Rough Sets [J]. Communication of ACM, 1995, 38(11): 89-95.
[28] Eckhorn R, Reitboeck H J, Arndt M, et al. Feature linking via stimulus-evoked oscillations: experimental results from cat visual cortex and functional implications from a network model[A]. In: International Joint Conference on Neural Networks[C]. Washington DC, 1989: 723-730.
[29] Ekblad U, Kinser J M. Theoretical foundation of the intersecting cortical model and its use for change detection of aircraft, cars and nuclear explosion tests [J]. Signal Processing , 2004, 84(7): 1131—1146.
[30] Ekblad U, Kinser J M, Atmer J, et al. The intersecting cortical model in image processing [J]. Nuclear Instruments & Methods In Physics Research, Section A, 2004, 525(2) : 392-396.
[31] 赵卫东, 李旗号. 粗集在数据开采中的应用[J].系统工程学报, 2002, 17(4): 575-579.
[32] Jelonek J. Rough Set Reducts of Attributes and Their Domains for Neural Networks[J]. Computational Intelligence, 1995, 11(2) :339-347.
[33] Szczuka M S. Rough Set Methods for Constructing Artifical Neural Network[J]. American Society of Mechanial Engineers, 1996,79(7): 1203-1206.
[34] Banerjee M. Rough Fuzzy MLP: Knowledge Encoding and Classification[J]. IEEE Transactions On Neural Networks, 1998,9(6): 1203-1206.
[35] Cornfield J. Statistical classification methods[A]. In: Proceedings of the Second Conference on the Diagnostic Process, Computer Diagnosis and Diagnostic Methods[C]. Chicago, 1972: 108-130.
[36] Devijver P A, Kittler J. Pattern Recognition, a Statistical Approach[M].Englewood Cliffs, London: Prentice Hall, 1982.
[37] Fukunaga K. Introduction to Statistical Pattern Recognition, 2nd Edition[M]. New York: Academic Press, 1990.
[38] Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation[A], In: Rumelhart D E. Parallel Distributed Processing: Explorations in the microstructure of Cognition Vol. I [C]. Cambridge: MIT Press, 1986:319-362.
[39] Pal N R, Pal S K. A review on image segmentation techniques[J]. Pattern Recognition, 1993,26(9): 1277-1294.
[40] Adler A, Guardo R. A neural network image reconstruction technique for electrical impedance tomography [J]. IEEE Transaction on Medical Imaging, 1994, 13(4): 594-600.
[41] Srinivasan V, Han Y K, Ong S H. Image reconstruction by a Hopfield neural network[J]. Image Vision Computing, 1993, 11(5): 278-282.
[42] Meyer R R, Heindl E. Reconstruction of off-axis electron holograms using a neural net[J]. Journal of Microscopy, 1998, 191(1): 52-59.
[43] Wang Y M, Wahl F M. Vector-entropy optimization-based neural-network approach to image reconstruction from projections[J].IEEE Transaction on Neural Networks, 1997, 8(5): 1008-1014.
[44] Greenhil D, Davies E R. Relative effectiveness of neural networks for image noise suppression[A]. In: Proceedings of the Pattern Recognition in Practice IV[C]. Vlieland, 1994:367-378.
[45] Ridder D, Duin R P, Verbeek P W. The applicability of neural networks to nonlinear image processing[J]. Pattern Analysis & Applications, 1999, 2(2): 111-128.
[46] Chua W, Yang L. Cellular networks: theory [J]. IEEE Trans. Circuits Systems, 1988, 35(10): 1257-1272.
[47] Hanek H, Ansari N. Speeding up the generalized adaptive neural filters[J]. IEEE Trans. Image Process, 1996, 5(5):705-712.
[48] Russo F. Hybrid neuro-fuzzy filter for impulse noise removal[J]. Pattern Recognition, 1999,32(11):1843-1855.
[49] Matsumoto T, Kobayashi H, Togawa Y. Spatial versus temporal stability issues in image processing neuro chips[J]. IEEE Trans. Neural Networks, 1992,3 (4): 540-569.
[50] Bedini L, Tonazzini A. Image restoration preserving discontinuities: the Bayesian approach and neural networks[J]. Image Vision Comput, 1992,10(2): 108-118.
[51] Chandrasekaran V, Palaniswami M, Caelli T M. Range image segmentation by dynamic neural network architecture[J]. Pattern Recognition, 1996, 29(2):315-329.
[52] Pugmire R H, Hodgson R M, Chaplin R I. The properties and training of a neural network based universal window filter developed for image processing tasks[A]. In: S. Amari, N. Kasabov (Eds.), Brain-like computing and intelligent information systems[C]. Springer-Verlag, Singapore, 1998: 49-77.
[53] Shih F Y, Moh J, Chang F C. A new ART-based neural architecture for pattern classification and image enhancement without prior knowledge [J]. Pattern Recognition, 1992,25(5): 533-542.
[54] Amerijckx C, Verleysen M, Thissen P. Image compression by self-organized Kohonen map[J]. IEEE Trans. Neural Networks, 1998,9(3):503-507.
[55] Daugman J G. Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression[J]. IEEE Trans. Acoustics, Speech Signal Process, 1988, 36(7): 1169-1179.
[56] Brause R W, Rippl M. Noise suppressing sensor encoding and neural signal orthonormalization[J]. IEEE Trans. Neural Networks, 1998, 9(4):613-628.
[57] Amerijckx C, Verleysen M, Thissen P. Image compression by self-organized Kohonen map[J]. IEEE Trans. Neural Networks, 1998, 9 (3):503-507.
[58] Mitra S, Yang S Y. High fidelity adaptive vector quantization at very low bit rates for progressive transmission of radiographic images[J]. J. Electron. Imaging, 1999, 8(1):23-35.
[59] Morris R J T, Rubin L D, Tirri H. Neural network techniques for object orientation detection: solution by optimal feedforward network and learning vector quantization approaches[J]. IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12 (11):1107-1115.
[60] Dony R D, Haykin S. Optimally adaptive transform coding[J]. IEEE Trans. Image Process., 1995, 4(10): 1358-1370.
[61] Oja E. A simplified neuron model as a principal component analyzer[J].J. Math. Biol., 1982,15(3):267-273.
[62] Baldi P, Hornik J. Neural networks and principal component analysis: learning from examples without local minima[J]. Neural Networks, 1989,2(1):53-58.
[63] Kepuska V Z, Mason S O. A hierarchical neural network system for signalized point recognition in aerial photographs[J]. Photogrammetric Eng. Remote Sensing, 1995, 61(7):917-925.
[64] Shustorovich A. A subspace projection approach to feature extraction-the 2-D Gabor transform for character recognition[J]. Neural Networks, 1994,7(8): 1295-1301.
[65] Patel D, Davies E R, Hannah I. The use of convolution operators for detecting contaminants in food images[J]. Pattern Recognition, 1996, 29(6): 1019-1029.
[66] Glass J O, Reddick W E. Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma[J]. Magn. Resonance 2 Imaging. 1998,16 (9): 1075-1083.
[67] Fukumi M, Omatu S, Nishikawa Y. Rotation-invariant neural pattern recognition system estimating a rotation angie[J]. IEEE Trans. Neural Networks, 1997, 8(3):568-581.
[68] Williams CKI, Revow M, Hinton G E. Instantiating deformable models with a neural net[J]. Comput. Vision Image Understand, 1997,68(1): 120-126.
[69] Hall L O, Bensaid A M, Clarke L P. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain[J]. IEEE Trans. Neural Networks, 1992,3(5):672-682.
[70] Handels H, Busch C, Encarnacao J. KAMEDIN: A telemedicine system for computer supported cooperative work and remote image analysis in radiology[J]. Comput. Methods Programs Biomed., 1997,52(3): 175-183.
[71] Manjunath B S, Simchony T, Chellappa R. Stochastic and deterministic networks for texture segmentation[J]. IEEE Trans. Acoustics, Speech Signal Process., 1990,38(6): 1039-1049.
[72] DeKruger D, Hunt B R. Image processing and neural networks for recognition of cartographic area features[J]. Pattern Recognition, 1994,27(4):461-483.
[73] Laine A, Fan J. Texture classification by wavelet packet signatures[J]. IEEE Trans.Pattern Anal. Mach. Intell.,1993, 15(11): 1186-1191.
[74] Raghu P P, Poongodi R, Yegnanarayana B. Unsupervised texture classification using vector quantization and deterministic relaxation neural network[J]. IEEE Trans. Image Process., 1997, 6(10): 1376-1387.
[75] Haring S, Viergever M A, Kok J N. Kohonen networks for multiscale image segmentation[J]. Image Vision Comput, 1994, 12(6): 339-344.
[76] Petersen M E, Arts T. Recognition of radiopaque markers in X-ray images using a neural network as nonlinear filter[J]. Pattern Recognition Letter, 1999,20(5):521-533.
[77] Carpenter G A, Grossberg S, Lesher G W. The what-and-where filter-a spatial mapping neural network for object recognition and image understanding[J]. Comput. Vision Image Understand, 1998, 69(1): 1-22.
[78] Shen J Y, Zhang Y X, Mu G G. Optical pattern recognition system based on a winner-take-all model of a neural network[J]. Opt. Eng., 1992, 32(5): 1053-1056.
[79] Penedo M G, Carreira M J, Mosquera A. Computer-aided diagnosis: a neural-network-based approach to lung nodule detection[J]. IEEE Trans. Med. Imag., 1998, 17(6):872-880.
[80] Watanabe M. Reward expectancy in primate prefrontal neurons [J]. Nature, 1996, 382: 521-535.
[81] Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. Journal of Physiology, 1962, 160:106-154.
[82] Schwartz E L, Rojer A S. Cortical hypercolumns and the topology of random orientation maps [J]. Pattern Recognition, 1994, 2:150-155.
[83] Rodieck R W, Stone J. Analysis of receptive fields of cat retinal ganglion cells [J]. Journal of Neurophysiology, 1965, 28: 833-849.
[84] Kruizinga Peter, Nikolay Petkov. Nonlinear Operator for Oriented Texture [J]. IEEE Transactions on Image Processing, 1999,8(10): 1395-1407.
[85] Yuen P C, Feng G C, Zhou J P. A contour detection method: Initialization and contour model [J]. Pattern Recognition Letters, 1999,20(8): 141-148.
[86] 杨谦，齐翔林，汪云九．简单细胞方位选择性感受野组织形成的神经网络模型[J]．中国科学(C辑)，2000，30(4)：412-420．
[87] 杨谦，齐翔林．视觉皮层复杂细胞时空编码特性[J]．生物物理学报，2000，16(2)：280-287．
[88] 李速，齐翔林，胡宏，汪云九．功能柱结构神经网络模型中的同步振荡现象[J]．中国科学(C辑)，2004，34(4)：385-394．
[89] 杨谦，齐翔林，汪云九．视皮层V1区简单细胞的稀疏编码策略[J]．计算物理，2001， 18(2): 143-146.
[90] Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve[J]. Journal of Physiology,1952, 117:500-544.
[91] FitzHugh R. Impulses and phsyiological states in theoretical models of nerve membrane[J]. Biophysics J., 1961,1: 445-466.
[92] Eckhorn R, Reitboeck H J, Arndt M. Feature linking via synchronization among distributed assemblies: Simulations of results from Cat Visual Cortex[J]. Neural Computing, 1990,2:293-307.
[93] Labbi A,Milanese R,Bosch H. A network of FitzHugh-Nagumo oscillators for object segmentation[A]. Proc. of International Symposium on Nonlinear Theory and Applications, NOLTA'97[C],Hawaii, 1997: 581-584.
[94] Rybak I A, Shevtsova N A, Sandier V A. The model of a Neural Network visual processor[J]. Neurocomputing, 1992 4:93-102.
[95] Young M P. The large scale organization of the primate cortical visual system[A]. SPIE[C], 1994, 2054:185-193.
[96] Gray Charles M. Oscillatory Responses in Cat Visual Cortex Exhibit Inter-Columnar Synchronization Which Reflects Global Stimulus Properties[J]. Nature, 1989, 555: 334-337.
[97] Neven H, Aertsen A. Rate coherence and event coherence in the visual cortex: a neuronal model of object recognition[J]. Biological Cybernetics, 1992, 57:309-322.
[98] Eckhom R. Feature linking via stimulus-evoked oscillations: experimental results from cat visual cortex and functional implications from a network model[A]. IJCNN: International Joint Conference on Neural Networks[C], 1989:723-730.
[100] 卡斯尔曼．数字图像处理[M]．北京：清华大学出版社，1998：414-416．
[101] 陈贺新．非线性滤波与数字图像处理[M]．北京：国防工业出版社，1997．
[102] 赵春晖，王伟．全方位多结构元形态滤波器．中国图像图形学报，1997．2(8)：629-633．
[103] 崔屹，图像处理与分析数学形态学方法及应用[M]．北京：科学出版社，2000：126-149．
[104] 王伟，赵春晖，孙圣和．最优全方位结构元约束层叠滤波[J]．中国图像图形学报，1999，4(6)：445-449．
[105] 王伟，赵春晖，孙圣和．基于层叠处理的多机自适应WOS滤波器[J]．中国图像图形学报，2000，5(8)：670-683．
[106] 张薇，沈允春，张曙．堆栈滤波的递归实现[J]．中国图像图形学报，2000，5(10)：851-856．
[107] 黄贤武，王加俊，李家华．数字图像处理与压缩编码技术[M]．成都：电子科技大学出版社，2000．12．
[108] Akopian D, Vainio O, Agaian S, et al. Processors for generalized stack filters[J]. IEEE Trans Signal Processing, 1995, 43(6): 1541-1546.
[109] Kuosmanen P, Astola J. Optimal stack filters under rank selection and structural constraints[J]. Signal Processing,1995,41:309-338
[110] Farina A. Linear and non-linear filters for clutter cancellation in radar systems.[J], SIGNAL Processing, 1997, 59(1): 101-112.
[111] Pasian F, Crise A. Restoration of signals degraded by impulse noise by means of a low-distortion non-linear filter[J], Signal Processing, 1984, 6(1): 67-76.
[112] Henriksen R. The truncated 2nd-order non-linear filter revisited[J]. IEEE Transactions on automatic control, 1982, 27(1): 247-251.
[113] 梁雯，刘松林．改进的中心加权中值滤波[J]．中国图像图形学报，1997，2(8，9)：629-633．
[114] 冯星奎，肖兴明，尹洪君．方向加权中值滤波算法[J]．中围图像图形学报，2000，5(7)：609-611．
[115] 马义德，张在峰，张祥光．基于全方位结构元层叠滤波的混合滤波器[J]．甘肃科学学报，2002，14(2)：56-60．
[116] 马义德，张祥光，戴若兰，李廉．一种改进的最优全方位结构元约束层叠滤波设计方案[J]．兰州大学学报(自然科学版)，2001，37(3)：52-55．
[117] 黄煦涛．二维数字信号处理—变换与中值滤波[M]．北京：科学出版社，1985．
[118] 赵春晖，乔景洵，孙圣和．一类多结构元自适应广义形态滤波器[J]．中国图像图形学报，1997，2(11)：806-809．
[120] Power W, Clist R. Comparison of supervised learning techniques applied to color segmentation of fruit image[A]. In: Proceeding of SPIE, Intelligent Roberts and Computer Vision. ⅩⅤ: Algorithms, Techniques, Active Vision, and Material Handing[C]. Boston, MA, USA, 1996: 370-381.
[121] Hance G A, Umbaugh S E, Moss R H,et al.Unsupervised color image segmentation with application to skin tumor borders[J]. IEEE Engineering inMedicine and Biology, 1996,15(1): 104-111.
[122] Pal N R, Pal S K. A review on image segmentation techniques [J]. Pattern Recognition, 1993, 26(9): 1277-1294.
[123] Ohlander R, Price K, Reddy D R. Picture segmentation using a recursive region splittingmethod [J]. ComputerGraphics and Image Processing, 1978,8(3): 313-333.
[124] Guo G D,Yu S, Ma S D. Unsupervised segmentation of color images [A]. In: Proceeding of 1998 IEEE International Conference on Image Processing[C]. Chicago, IL, USA, 1998: 299-302.
[125] Underwood S A, Aggarwal J K. Interactive computer analysis of aerial color infrared photographs[J]. Computer Graphics and Image Processing, 1977,6(1): 1-24.
[126] Kurugollu F, Sankur B, Harmanci A E. Color image segmentation using histogram multithresholding and fusion [J]. Image and Vision Computing, 2001, 19(13): 915-928.
[127] Celenk M. A color clustering technique for image segmentation[J]. ComputerVision, Graphics, and Image Processing, 1990, 52(2): 145-170.
[128] Ahuja N, Haralick R M, Rosenfeld A. Neighbor gray levels as features in pixel classification [J]. Pattern Recognition, 1980, 13(4): 251-260.
[129] 刘重庆，程华．分割彩色图像的一种有效聚类方法[J]．模式识别与人工智能，1995，8(A01)：133-138．
[130] Carevic D, Caelli. T. Region-based coding of color image using Karhunen-Loeve Transform [J]. Graphics Models and Image Processing, 1997,59(1): 27-38.
[131] 王兴伟，沈兰荪，卫保国等．基于改进的K-均值聚类和数学形态学的彩色眼科图像病灶分割[J]．中国生物医学工程学报，2002，21(5)：443～448．
[132] Bezdek J C. Pattern recognition with fuzzy objective function algorithms [M]. New York: Plenum Press, 1981.
[133] Xie X L, BeniG. A validitymeasure for fuzzy clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(8): 841-847.
[134] Zahid N, LimouriM, Esseaid A. A new cluster-validity for fuzzy clustering [J]. Pattern Recognition, 1999,32(5): 1089-1097.
[135] Ferri F, Vidal E. Color image segmentation and labeling through multi-edit condensing[J]. PatternRecognition Letters, 1992,13(8): 561-568.
[136] Lim Y W, Lee S U. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques [J]. Pattern Recognition, 1990,23(9): 935-952.
[137] 刘健庄，谢维信．高效的彩色图像塔形模糊聚类分割方法[J]．西安电子科技大学学报，1993，20(1)：40-46．
[138] 林开颜，徐立鸿，吴军辉．快速模糊C均值聚类彩色图像分割方法[J]．中国图象图形学报，2004，9(2)：159-163．
[139] Chen T Q, Lu Y. Color image segmentation—An innovative approach[J]. Pattern Recognition, 2002,35(2): 395-405.
[140] Michael T U, Arbib A. Color image segmentation using competitive learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994,16(12): 1197-1206.
[141] Ohta Y, Kanade T, Sakai T. Color information for region segmentation[J]. Computer Graphics and Image Processing, 1980, 13(3): 222-241.
[142] Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations[J]. IEEE Transactions on PatternAnalysis andMachine Intelligence, 1991, 13(6): 583-598.
[143] Lezoray O, Cardot H. Cooperation of color pixel classification schemes and color watershed: a study for microscopic images[J]. IEEE Transactions on Image Processing, 2002, 11(7): 783-789.
[144] Shafarenko L, Petrou M, Kittler J. Automatic watershed segmentation of randomly textured color images[J]. IEEE Transactions on Image Processing, 1997,6(11): 1530-1544.
[145] Shiji A, Hamada N. Color image segmentation method using watershed algorithm and contour information[A]. In: Proceeding of 1999 IEEE International Conference on Image Processing[C]. Kobe, Japan, 1999: 305-309.
[146] 马丽红，张宇，邓健平．基于形态开闭滤波二值标记和纹理特征合并的分水岭算法[J]．中国图象图形学报，2003，8(1)：77-83．
[147] Geman S, Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(11): 721-741.
[148] Celeux G, Forbes F, Peyrard N. EM procedures using mean field-like approximations forMarkovmodel-based image segmentation [J]. Pattern Recognition 2003,36(1): 131-144.
[149] Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm [J]. IEEE Transactions on Medical Image, 2001, 20(1): 45-57.
[150] Huang C L. Parallel image segmentation using modified Hopfield model [J]. Pattern Recognition Letters, 1993, 13(5): 345-353.
[151] Campadelli P, Medici D, Schettini R. Color image segmentation usingHopfield networks[J]. Image and Vision Computing, 1997, 15(3): 161-166.
[152] Sammouda M, Sammouda R, Niki N, et al.Segmentation and analysis of liver cancer pathological color image based on artificial neural networks [A]. In: Proceeding of IEEE 1999 International Conference on Image Processing [C]. Kobe, Japan, 1999: 392-396.
[153] Sammouda R, Sammouda M. Improving the performance of Hopfield neural network to segment pathological liver color images [J]. International Congress Series, 2003, 1256: 232-239.
[154] Ong S H, Yeo N C, Lee K H, et al. Segmentation of color images using a two-stage self-organizing network[J]. Image and Vision Computing, 2002,20(4): 279-289.
[155] Papamarkos N, Strouthopoulous C, Andreadis I. Multithresholding of color and gray-level images through a neural network technique[J]. Image and Vision Computing, 2000, 18(3): 213-222.
[156] Lescure P, Yedid V M, DupoisotH,et al.Color segmentation on biological microscope images [A]. In: Proceeding of SPIE, Application of Artificial NeuralNetworks in Image Processing Ⅳ[C]. San Jose, California, USA, 1999: 182-193.
[157] Ozden M, Polat E. A color image segmentation approach for content-based image retrieval[J]. Pattern Recognition, 2007, 40: 1318-1325.
[162] Bovik A L. Handbook of Image And Video Processing [M]. New York: Academic Press, 2000.
[163] 杨波，汪同庆，吕永平，等．利用动态结构元素提取直线[J]．计算机辅助设计与图形学学报，2003，15(4)：421-424．
[164] 岳洪伟，李扬，蔡肯，等．数学形态学在图像处理中的应用与展望[J]．影像技术，2006．2：19-21．
[165] Kenneth．R．Castleman Digital Image Processing Printice-Hall，Inc[M]．北京：清华大学出版社，1998,4．
[166] 颜七笙，王士同．调节形态学运算及其神经网络实现[J]．计算机工程与设计，2007，28(1)：115-117．
[167] 赵于前，桂卫华，陈真诚．基于自适应数学形态学的医学图像边缘连接[J]．计算机工程，2006，32(22)：17-19．
[168] 赵鹏，浦昭邦．基于形态学4子带分解金字塔的图像融合[J]．光学学报，2007，27(1)：39-44．
[169] 阚江明，李文彬．基于数学形态学的树木图像分割方法[J]．北京林业大学学报，2006，28(2)：151-156．
[99] 徐志平，钟亦平，张世永．用于脉冲噪声图像的交叉视觉皮质模型滤波[J]．计算机辅助设计与图形学学报，2007，19卷，第6期(国内权威，EI索引，已录用)
[119] 徐志平，钟亦平，张世永．丛于交叉视觉皮质模型的脉冲噪声红外图像滤波算法[J]．小型微型计算机系统(国内权威，已录用)
[158] Zhiping XU, Yiping ZHONG, Shiyong ZHANG, Fast Shape Index Framework based on Principle Component Analysis using Edge Co-occurrence Matrix,Lecture Notes AI, 2006,4253: 390-397. (SCI索引，EI索引)
[159] Zhiping XU, Shiyong ZHANG, Yiping ZHONG. A Novel Image Semantic Block Clustering Method based on Artificial Visual Cortical Responding Model[A]. In: 2007 IEEE Symposium on Computational Intelligence and Data Mining[C], April 1-5, 2007,Honolulu, Hawaii, USA(EI索引, 已录用)
[160] Zhiping XU, Yiping ZHONG, Shiyong ZHANG, Artificial Visual Cortical Responding Model in Image Semantic Processing[A]. In: 2007 ACS/IEEE International Conference on Computer Systems and Applications[C]. (SCI索引，EI索引，已录用)
[161] Zhiping Xu, Jinghong Pan, Shiyong Zhang. A Novel Automatic Framework for Scoliosis X-Ray Image Retrieval[A]. In: 2007 International Joint Conderence on Neural Networks, IJCNN 2007[C].(SCI索引，EI索引，已录用)
[170] 徐志平，张世永，钟亦平．基于交叉视觉皮质模型的图像形态学处理研究[J]．计算机辅助设计与图形学学报．2007，19卷，第8期(国内权威，EI索引，已录用)
[2] Eckhorn R. Neural mechanisms of scene segmentation: recordings from the visual cortex suggest basic circuits for linking field models [J]. IEEE Transactions on Neural Networks, 1999, 10(3): 464-479.
[3] Eckhorn R, Bauer R, Jordan W. Coherent oscillations: A mechanism of feature linking in the visual cortex? [J]. Biological Cybernetics, 1988, 60 (2): 121-130.
[4] Eckhorn R, Frien A, Bauer R. High frequency (60-90 Hz) oscillations in primary visual cortex of awake monkey [J]. Neuroreport, 1993, 4(3): 243-246.
[5] Eckhorn R. Oscillatory and non-oscillatory synchronizations in the visual cortex and their possible roles in associations of visual features [J]. Progress in Brain Research, 1994, 102: 405-426.
[6] 余群明，王耀南．基于振荡型混沌神经网络的智能信息处理研究[J]．自动化学报，2002，28(3)：401-407．
[7] 焦贤发，王如彬．刺激下可变耦合神经振子群活动的非线性随机演化模型[J]．控制与决策，2005，20(8)：897-900．
[8] 汪云九，唐孝威，吴建永．意识的计算神经科学研究[J]．科学通报，2001，46(13)：1140-1144．
[9] 张建州，张宇．外积取等联想记忆神经网络部分同步收敛性[J]．系统工程与电子技术，2002，24(11)：19-21．
[10] 石美红，张军英，李永刚．基于差别特征的纹理图像识别研究[J]．计算机应用，2004，24(1)：66-69．
[11] Ekblad U, Kinserb J M, Atmera J. The intersecting cortical model in image processing [J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2004, 525(1): 392-396.
[12] Ekblad U, Kinser J M. Theoretical foundation of the intersecting cortical model and its use for change detection of aircraft, cars, and nuclear explosion tests[J]. Signal Processing, 2004, 84(7): 1131-1146.
[13] Ekblad U, Kinserb J M, Atmer J, et ai. Image information content and extraction techniques[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2004, 525(2): 397-401.
[14] 黄英，王成波．生物电及其在医学中的应用[J]．局解手术学杂志，2002，11(4)：389-399．
[15] 伍国锋，贵阳，张文渊．脑电波产生的神经生理机制[J]．临床脑电学杂志，2000，9(3)：188-190．
[16] Cai S P, Shen J, Doi T. Optical mapping of brainstem neuronal activity evoked by auditory electro-stimulation in rats [J]. Chinese Journal of Clinical Rehabilitation, 2005, 9(29): 219-222.
[17] McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity[J]. Bulletin of Mathematical Biophysics, 1943, 5: 115-133.
[18] Hebb D O. The Organization of Behavior [M]. New York: Wiley, 1949.
[19] Rosenblatt F. The perception: A probabilistic model for information storage and organization in the brain [J]. Psychological Review, 1958, 65: 386-408.
[20] Widrow B, Hoff M E. Adaptive switching circuits[A]. In: Neurocomputing: foundations of research [M]. Cambridge, MA, USA: MIT Press, 1988: 123-134.
[21] Minsky M, Papert S.Perceptions [M]. Cambridge MA: MIT Press, 1969.
[22] Kohonen T. Correlation matrix memories[A]. In: Neurocomputing: foundations of research[M]. Cambridge, MA, USA: MIT Press, 1988: 171-180.
[23] Anderson J A. A simple neural network generating an interactive memory[J]. Mathematical Biosciences, 1972, 14: 197-220.
[24] Grossberg S. Adaptive pattern classification and universal recoding: Ⅰ. Parallel development and coding of neural feature detectors [J]. Biological Cybernetics, 1976, 23: 121-134.
[25] Hopfleld J J. Neural networks and physical systems with emergent collective computational properties[A]. In: Proceedings of the National Academy of Sciences[C], 1982,79: 2554-2558.
[26] Rumelhart D E, McClelland J L. Parallel Distributed Processing: Explorations in the Microstructure of Cognition [M]. Cambridge, MA: MIT Press, 1986, 1.
[27] Pawlak Z. Rough Sets [J]. Communication of ACM, 1995, 38(11): 89-95.
[28] Eckhorn R, Reitboeck H J, Arndt M, et al. Feature linking via stimulus-evoked oscillations: experimental results from cat visual cortex and functional implications from a network model[A]. In: International Joint Conference on Neural Networks[C]. Washington DC, 1989: 723-730.
[29] Ekblad U, Kinser J M. Theoretical foundation of the intersecting cortical model and its use for change detection of aircraft, cars and nuclear explosion tests [J]. Signal Processing, 2004, 84(7): 1131-1146.
[30] Ekblad U, Kinser J M, Atmer J, et al. The intersecting cortical model in image processing [J]. Nuclear Instruments & Methods In Physics Research, Section A, 2004, 525(2): 392-396.
[31] 赵卫东, 李旗号. 粗集在数据开采中的应用[J]. 系统工程学报, 2002, 17(4): 575-579.
[32] Jelonek J. Rough Set Reducts of Attributes and Their Domains for Neural Networks[J]. Computational Intelligence, 1995, 11(2) :339-347.
[33] Szczuka M S. Rough Set Methods for Constructing Artifical Neural Network[J]. American Society of Mechanial Engineers, 1996,79(7): 1203-1206.
[34] Banerjee M. Rough Fuzzy MLP: Knowledge Encoding and Classification[J]. IEEE Transactions On Neural Networks, 1998,9(6): 1203-1206.
[35] Cornfield J. Statistical classification methods[A]. In: Proceedings of the Second Conference on the Diagnostic Process, Computer Diagnosis and Diagnostic Methods[C]. Chicago, 1972: 108-130.
[36] Devijver P A, Kittler J. Pattern Recognition, a Statistical Approach[M].Englewood Cliffs, London: Prentice Hall, 1982.
[37] Fukunaga K. Introduction to Statistical Pattern Recognition, 2nd Edition[M]. New York: Academic Press, 1990.
[38] Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation[A], In: Rumelhart D E. Parallel Distributed Processing: Explorations in the microstructure of Cognition Vol. I [C]. Cambridge: MIT Press, 1986:319-362.
[39] Pal N R, Pal S K. A review on image segmentation techniques[J]. Pattern Recognition, 1993, 26(9): 1277-1294.
[40] Adler A, Guardo R. A neural network image reconstruction technique for electrical impedance tomography[J]. IEEE Transaction on Medical Imaging, 1994, 13(4): 594-600.
[41] Srinivasan V, Han Y K, Ong S H. Image reconstruction by a Hopfield neural network[J]. Image Vision Computing, 1993, 11(5): 278-282.
[42] Meyer R R, Heindl E. Reconstruction of off-axis electron holograms using a neural net[J]. Journal of Microscopy, 1998, 191(1): 52-59.
[43] Wang Y M, Wahl F M. Vector-entropy optimization-based neural-network approach to image reconstruction from projections[J].IEEE Transaction on Neural Networks, 1997, 8(5): 1008-1014.
[44] Greenhil D, Davies E R. Relative effectiveness of neural networks for image noise suppression[A]. In: Proceedings of the Pattern Recognition in Practice IV[C]. Vlieland, 1994:367-378.
[45] Ridder D, Duin R P, Verbeek P W. The applicability of neural networks to nonlinear image processing[J]. Pattern Analysis & Applications, 1999, 2(2): 111 -128.
[46] Chua W, Yang L. Cellular networks: theory [J]. IEEE Trans. Circuits Systems, 1988, 35(10): 1257-1272.
[47] Hanek H, Ansari N. Speeding up the generalized adaptive neural filters[J]. IEEE Trans. Image Process, 1996, 5(5):705-712.
[48] Russo F. Hybrid neuro-fuzzy filter for impulse noise removal[J]. Pattern Recognition, 1999, 32(11): 1843-1855.
[49] Matsumoto T, Kobayashi H, Togawa Y. Spatial versus temporal stability issues in image processing neuro chips[J]. IEEE Trans. Neural Networks, 1992,3 (4): 540-569.
[50] Bedini L, Tonazzini A. Image restoration preserving discontinuities: the Bayesian approach and neural networks[J]. Image Vision Comput.,1992,10(2):108-118.
[51] Chandrasekaran V, Palaniswami M, Caelli T M. Range image segmentation by dynamic neural network architecture[J]. Pattern Recognition, 1996, 29(2):315-329.
[52] Pugmire R H, Hodgson R M, Chaplin R I. The properties and training of a neural network based universal window filter developed for image processing tasks[A]. In: S. Amari, N. Kasabov (Eds.), Brain-like computing and intelligent information systems[C], Springer-Verlag, Singapore, 1998:49-77.
[53] Shih F Y, Moh J, Chang F C. A new ART-based neural architecture for pattern classification and image enhancement without prior knowledge [J]. Pattern Recognition, 1992, 25(5): 533-542.
[54] Amerijckx C, Verleysen M, Thissen P. Image compression by self-organized Kohonen map[J]. IEEE Trans. Neural Networks, 1998,9(3):503-507.
[55] Daugman J G Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression[J]. IEEE Trans. Acoustics, Speech Signal Process, 1988, 36(7): 1169-1179.
[56] Brause R W, Rippl M. Noise suppressing sensor encoding and neural signal orthonormalization[J]. IEEE Trans. Neural Networks, 1998, 9(4):613-628.
[57] Amerijckx C, Verleysen M, Thissen P. Image compression by self-organized Kohonen map[J]. IEEE Trans. Neural Networks, 1998, 9 (3):503-507.
[58] Mitra S, Yang S Y. High fidelity adaptive vector quantization at very low bit rates for progressive transmission of radiographic images[J]. J. Electron. Imaging, 1999, 8(1):23-35.
[59] Morris R J T, Rubin L D, Tirri H. Neural network techniques for object orientation detection: solution by optimal feedforward network and learning vector quantization approaches[J]. IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12 (11): 1107-1115.
[60] Dony R D, Haykin S. Optimally adaptive transform coding[J]. IEEE Trans. Image Process., 1995, 4(10): 1358-1370.
[61] Oja E. A simplified neuron model as a principal component analyzer[J].J. Math. Biol., 1982,15(3):267-273.
[62] Baldi P, Hornik J. Neural networks and principal component analysis: learning from examples without local minima[J]. Neural Networks,1989,2(1):53-58.
[63] Kepuska V Z, Mason S O. A hierarchical neural network system for signalized point recognition in aerial photographs[J]. Photogrammetric Eng. Remote Sensing, 1995, 61(7):917-925.
[64] Shustorovich A. A subspace projection approach to feature extraction-the 2-D Gabor transform for character recognition[J]. Neural Networks, 1994,7(8): 1295-1301.
[65] Patel D, Davies E R, Hannah I. The use of convolution operators for detecting contaminants in food images[J]. Pattern Recognition, 1996, 29(6): 1019-1029.
[66] Glass J O, Reddick W E. Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma[J]. Magn. Resonance 2 Imaging. 1998,16 (9): 1075-1083.
[67] Fukumi M, Omatu S, Nishikawa Y. Rotation-invariant neural pattern recognition system estimating a rotation angle[J]. IEEE Trans. Neural Networks, 1997, 8(3):568-581.
[68] Williams CKI, Revow M, Hinton G E. Instantiating deformable models with a neural net[J]. Comput. Vision Image Understand,1997,68(1):120-126.
[69] Hall L O, Bensaid A M, Clarke L P. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain[J]. IEEE Trans. Neural Networks, 1992,3(5):672-682.
[70] Handels H, Busch C, Encarnacao J. KAMEDIN: A telemedicine system for computer supported cooperative work and remote image analysis in radiology [J]. Comput. Methods Programs Biomed., 1997,52(3):175-183.
[71] Manjunath B S, Simchony T, Chellappa R. Stochastic and deterministic networks for texture segmentation[J]. IEEE Trans. Acoustics, Speech Signal Process., 1990,38(6): 1039-1049.
[72] DeKruger D, Hunt B R. Image processing and neural networks for recognition of cartographic area features[J]. Pattern Recognition, 1994,27(4):461-483.
[73] Laine A, Fan J. Texture classification by wavelet packet signatures[J]. IEEE Trans.Pattern Anal. Mach. Intell., 1993, 15(11): 1186-1191.
[74] Raghu P P, Poongodi R, Yegnanarayana B. Unsupervised texture classification using vector quantization and deterministic relaxation neural network[J]. IEEE Trans. Image Process., 1997, 6(10):1376-1387.
[75] Haring S, Viergever M A, Kok J N. Kohonen networks for multiscale image segmentation[J]. Image Vision Comput, 1994, 12(6): 339-344.
[76] Petersen M E, Arts T. Recognition of radiopaque markers in X-ray images using a neural network as nonlinear filter[J]. Pattern Recognition Letter, 1999,20(5):521-533.
[77] Carpenter G A, Grossberg S, Lesher G W. The what-and-where filter-a spatial mapping neural network for object recognition and image understanding[J]. Comput. Vision Image Understand, 1998, 69(1): 1-22.
[78] Shen J Y, Zhang Y X, Mu G G. Optical pattern recognition system based on a winner-take-all model of a neural network[J]. Opt. Eng., 1992, 32(5): 1053-1056.
[79] Penedo M G, Carreira M J, Mosquera A. Computer-aided diagnosis: a neural-network-based approach to lung nodule detection[J]. IEEE Trans. Med. Imag., 1998, 17(6):872-880.
[80] Watanabe M. Reward expectancy in primate prefrontal neurons [J]. Nature, 1996, 382: 521-535.
[81] Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. Journal of Physiology, 1962, 160: 106-154.
[82] Schwartz E L, Rojer A S. Cortical hypercolumns and the topology of random orientation maps [J]. Pattern Recognition, 1994, 2: 150-155.
[83] Rodieck R W, Stone J. Analysis of receptive fields of cat retinal ganglion cells [J]. Journal of Neurophysiology, 1965, 28: 833-849.
[84] Kruizinga Peter, Nikolay Petkov. Nonlinear Operator for Oriented Texture [J]. IEEE Transactions on Image Processing, 1999,8(10): 1395-1407.
[85] Yuen P C, Feng G C, Zhou J P. A contour detection method: Initialization and contour model[J]. Pattern Recognition Letters, 1999,20(8): 141-148.
[86] 杨谦，齐翔林，汪云九．简单细胞方位选择性感受野组织形成的神经网络模型[J]．中国科学(C辑)，2000，30(4)：412-420．
[87] 杨谦，齐翔林．视觉皮层复杂细胞时空编码特性[J]．生物物理学报，2000，16(2)：280-287．
[88] 李速，齐翔林，胡宏，汪云九．功能柱结构神经网络模型中的同步振荡现象[J]．中国科学(C辑)，2004，34(4)：385-394．
[89] 杨谦，齐翔林，汪云九．视皮层V1区简单细胞的稀疏编码策略[J]．计算物理，2001，18(2)：143-146
[90] Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve[J]. Journal of Physiology, 1952, 117:500-544.
[91] FitzHugh R, Impulses and phsyiological states in theoretical models of nerve membrane[J]. Biophysics J. , 1961,1: 445-466.
[92] Eckhom R, Reitboeck H J, Amdt M. Feature linking via synchronization among distributed assemblies: Simulations of results from Cat Visual Cortex[J]. Neural Computing, 1990,2:293-307.
[93] Labbi A,Milanese R,Bosch H. A network of FitzHugh-Nagumo oscillators for object segmentation[A]. Proc. of International Symposium on Nonlinear Theory and Applications, NOLTA'97[C],Hawaii, 1997: 581-584.
[94] Rybak I A, Shevtsova N A, Sandler V A. The model of a Neural Network visual processor[J]. Neurocomputing, 1992 4:93-102.
[95] Young M P. The large scale organization of the primate cortical visual system[A]. SPIE[C], 1994, 2054: 185-193.
[96] Gray C M. Oscillatory Responses in Cat Visual Cortex Exhibit Inter-Columnar Synchronization Which Reflects Global Stimulus Properties[J]. Nature, 1989, 555:334-337.
[97] Neven H, Aertsen A. Rate coherence and event coherence in the visual cortex: a neuronal model of object recognition[J]. Biological Cybernetics, 1992, 57:309-322.
[98] Eckhorn R. Feature linking via stimulus-evoked oscillations: experimental results from cat visual cortex and functional implications from a network model[A]. IJCNN: International Joint Conference on Neural Networks[C],1989:723-730.
[99] 徐志平，钟亦平，张世永．用于脉冲噪声图像的交叉视觉皮质模型滤波[J]．计算机辅助设计与图形学学报，2007，19卷，第6期(国内权威，EI索引，已录用)
[100] 卡斯尔曼．数字图像处理[M]．北京：清华大学出版社，1998：414-416．
[101] 陈贺新．非线性滤波与数字图像处理[M]．北京：国防工业出版社，1997．
[102] 赵春晖，王伟．全方位多结构元形态滤波器．中国图像图形学报，1997．2(8)：629-633．
[103] 崔屹，图像处理与分析数学形态学方法及应用[M]．北京：科学出版社，2000：126-149．
[104] 王伟，赵春晖，孙圣和．最优全方位结构元约束层叠滤波[J]．中国图像图形学报，1999，4(6)：445-449．
[105] 王伟，赵春晖，孙圣和．基于层叠处理的多机自适应WOS滤波器[J]．中国图像图形学报，2000，5(8)：670-683．
[106] 张薇，沈允春，张曙．堆栈滤波的递归实现[J]．中国图像图形学报，2000，5(10)：851-856．
[107] 黄贤武，王加俊，李家华．数字图像处理与压缩编码技术[M]．成都：电子科技大学出版社，2000．12．
[108] Akopian D, Vainio O, Agaian S, et al. Processors for generalized stack filters[J]. IEEE Trans Signal Processing, 1995, 43(6): 1541-1546.
[109] Kuosmanen P, Astola J. Optimal stack filters under rank selection and structural constraints[J]. Signal Processing,1995,41:309-338
[110] Farina A. Linear and non-linear filters for clutter cancellation in radar systems.[J], SIGNAL Processing, 1997, 59(1): 101-112.
[111] Pasian E Crise A. Restoration of signals degraded by impulse noise by means of a low-distortion non-linear filter[J], Signal Processing, 1984, 6(1): 67-76.
[112] Henriksen R. The truncated 2nd-order non-linear filter revisited[J]. IEEE Transactions on automatic control, 1982, 27(1): 247-251.
[113] 梁雯，刘松林．改进的中心加权中值滤波[J]．中国图像图形学报，1997，2(8，9)：629-633．
[114] 冯星奎，肖兴明，尹洪君．方向加权中值滤波算法[J]．中国图像图形学报，2000，5(7)：609-611．
[115] 马义德，张在峰，张祥光．基于全方位结构元层叠滤波的混合滤波器[J]．甘肃科学学报，2002，14(2)：56-60．
[116] 马义德，张祥光，戴若兰，李廉．一种改进的最优全方位结构元约束层叠滤波设计方案[J]．兰州大学学报(自然科学版)，2001，37(3)：52-55．
[117] 黄煦涛．二维数字信号处理—变换与中值滤波[M]．北京：科学出版社，1985．
[118] 赵春晖，乔景洵，孙圣和．一类多结构元自适应广义形态滤波器[J]．中国图像图形学报，1997，2(11)：806-809．
[99] 徐志平，钟亦平，张世永．用于脉冲噪声图像的交叉视觉皮质模型滤波[J]．计算机辅助设计与图形学学报，2007，19卷，第6期(国内权威，EI索引，已录用)
[119] 徐志平，钟亦平，张世永．基于交叉视觉皮质模型的脉冲噪声红外图像滤波算法[J]．小型微型计算机系统(国内权威，已录用)
[120] Power W, Clist R. Comparison of supervised learning techniques applied to color segmentation of fruit image[A]. In: Proceeding of SPIE, Intelligent Roberts and Computer Vision. ⅩⅤ: Algorithms, Techniques, Active Vision, and Material Handing[C]. Boston, MA, USA, 1996: 370-381.
[121] Hanee G A, Umbaugh S E, Moss R H,et al.Unsupervised color image segmentation with application to skin tumor borders[J]. IEEE Engineering inMedicine and Biology, 1996,15(1): 104-111.
[122] Pal N R, Pal S K. A review on image segmentation techniques [J]. Pattern Recognition, 1993, 26(9): 1277-1294.
[123] Ohlander R, Price K, Reddy D R. Picture segmentation using a recursive region splittingmethod [J]. ComputerGraphics and Image Processing, 1978,8(3): 313-333.
[124] Guo G D,Yu S, Ma S D. Unsupervised segmentation of color images [A]. In: Proceeding of 1998 IEEE International Conference on Image Processing[C]. Chicago, IL, USA, 1998: 299-302.
[125] Underwood S A, Aggarwal J K. Interactive computer analysis of aerial color infrared photographs[J]. Computer Graphics and Image Processing, 1977,6(1): 1-24.
[126] Kurugollu F, Sankur B, Harmanci A E. Color image segmentation using histogram multithresholding and fusion [J]. Image and Vision Computing, 2001,19(13): 915-928.
[127] Celenk M. A color clustering technique for image segmentation[J]. ComputerVision, Graphics, and Image Processing, 1990, 52(2): 145-170.
[128] Ahuja N, Haralick R M, Rosenfeld A. Neighbor gray levels as features in pixel classification[J]. Pattern Recognition, 1980, 13(4): 251-260.
[129] 刘重庆，程华．分割彩色图像的一种有效聚类方法[J]．模式识别与人工智能，1995，8(A01)：133-138．
[130] Carevic D, Caelli T. Region-based coding of color image using Karhunen-Loeve Transform[J]. Graphics Models and Image Processing, 1997,59(1): 27-38.
[131] 王兴伟，沈兰荪，卫保国等．基于改进的K-均值聚类和数学形态学的彩色眼科图像病灶分割[J]．中国生物医学工程学报，2002，21(5)：443～448．
[132] Bezdek J C. Pattern recognition with fuzzy objective function algorithms [M]. New York: Plenum Press, 1981.
[133] Xie X L, BeniG. A validitymeasure for fuzzy clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(8): 841-847.
[134] Zahid N, LimouriM, Esseaid A. A new cluster-validity for fuzzy clustering [J]. Pattern Recognition, 1999,32(5): 1089-1097.
[135] Ferri F, Vidal E. Color image segmentation and labeling through multi-edit condensing[J]. PatternRecognition Letters, 1992,13(8): 561-568.
[136] Lim Y W, Lee S U. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques [J]. Pattern Recognition, 1990,23(9): 935-952.
[137] 刘健庄，谢维信．高效的彩色图像塔形模糊聚类分割方法[J]．西安电了科技大学学报，1993，20(1)：40-46．
[138] 林开颜，徐立鸿，吴军辉．快速模糊C均值聚类彩色图像分割方法[J]．中国图象图形学报， 2004,9(2): 159-163.
[139] Chen T Q, Lu Y. Color image segmentation—An innovative approach[J]. Pattern Recognition, 2002,35(2): 395-405.
[140] Michael T U, Arbib A. Color image segmentation using competitive learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994,16(12): 1197-1206.
[141] Ohta Y, Kanade T, Sakai T. Color information for region segmentation[J]. Computer Graphics and Image Processing, 1980, 13(3): 222-241.
[142] Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations[J]. IEEE Transactions on PatternAnalysis andMachine Intelligence, 1991,13(6): 583-598.
[143] Lezoray O, Cardot H. Cooperation of color pixel classification schemes and color watershed: a study for microscopic images[J]. IEEE Transactions on Image Processing, 2002,11(7): 783-789.
[144] Shafarenko L, Petrou M, Kittler J. Automatic watershed segmentation of randomly textured color images[J]. IEEE Transactions on Image Processing, 1997,6(11): 1530-1544.
[145] Shiji A, Hamada N. Color image segmentation method using watershed algorithm and contour information[A]. In: Proceeding of 1999 IEEE International Conference on Image Processing[C]. Kobe, Japan, 1999: 305-309.
[146] 马丽红，张宇，邓健平．基于形态开闭滤波二值标记和纹理特征合并的分水岭算法[J]．中国图象图形学报，2003，8(1)：77-83．
[147] Geman S, Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984,6(11): 721-741.
[148] Celeux G, Forbes F, Peyrard N. EM procedures using mean field-like approximations forMarkovmodel-based image segmentation [J]. Pattern Recognition 2003,36(1): 131-144.
[149] Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm [J]. IEEE Transactions on Medical Image, 2001,20(1): 45-57.
[150] Huang C L. Parallel image segmentation using modified Hopfield model [J]. Pattern Recognition Letters, 1993, 13(5): 345-353.
[151] Campadelli P, Medici D, Schettini R. Color image segmentation usingHopfield networks [J]. Image and Vision Computing, 1997, 15(3): 161-166.
[152] Sammouda M, Sammouda R, Niki N, et al.Segmentation and analysis of liver cancer pathological color image based on artificial neural networks [A]. In: Proceeding of IEEE 1999 International Conference on Image Processing [C]. Kobe, Japan, 1999: 392-396.
[153] Sammouda R, Sammouda M. Improving the performance of Hopfield neural network to segment pathological liver color images [J]. International Congress Series, 2003, 1256: 232-239.
[154] Ong S H, Yeo N C, Lee K H, et al.Segmentation of color images using a two-stage self-organizing network [J]. Image and Vision Computing, 2002,20(4): 279-289.
[155] Papamarkos N, Strouthopoulous C, Andreadis I. Multithresholding of color and gray-level images through a neural network technique[J]. Image and Vision Computing, 2000,18(3): 213-222.
[156] Lescure P, Yedid V M, DupoisotH,et al.Color segmentation on biological microscope images[A]. In: Proceeding of SPIE, Application of Artificial NeuralNetworks in Image Processing Ⅳ[C]. San Jose, California, USA, 1999: 182-193.
[157] Ozden M, Polat E. A color image segmentation approach for content-based image retrieval[J]. Pattern Recognition, 2007, 40: 1318-1325.
[158] Zhiping XU, Yiping ZHONG, Shiyong ZHANG, Fast Shape Index Framework based on Principle Component Analysis using Edge Co-occurrence Matrix,Lecture Notes AI, 2006,4253: 390-397. (SCI索引，EI索引)
[159] Zhiping XU, Shiyong ZHANG, Yiping ZHONG. A Novel Image Semantic Block Clustering Method based on Artificial Visual Cortical Responding Model[A]. In: 2007 IEEE Symposium on Computational Intelligence and Data Mining[C], April 1-5, 2007,Honolulu, Hawaii, USA(EI索引，已录用)
[160] Zhiping XU, Yiping ZHONG, Shiyong ZHANG, Artificial Visual Cortical Responding Model in Image Semantic Processing[A]. In: 2007 ACS/IEEE International Conference on Computer Systems and Applications[C]. (SCI索引，EI索引，已录用)
[161] Zhiping Xu, Jinghong Pan, Shiyong Zhang. A Novel Automatic Framework for Scoliosis X-Ray Image Retrieval[A]. In: 2007 International Joint Conderence on Neural Networks, IJCNN 2007[C]. (SCI索引，EI索引，已录用)
[162] Bovik A L. Handbook of Image And Video Processing [M]. New York: Academic Press, 2000.
[163] 杨波，汪同庆，吕永平，等．利用动态结构元素提取直线[J]．计算机辅助设计与图形学学报，2003，15(4)：421-424．
[164] 岳洪伟，李扬，蔡肯，等．数学形态学在图像处理中的应用与展望[J]．影像技术，2006．2：19-21．
[165] Kenneth.R.Castleman Digital Image Processing Printice-Hall,Inc[M].北京：清华大学出版社，1998，4．
[166] 颜七笙，王士同．调节形态学运算及其神经网络实现[J]．计算机工程与设计，2007，28(1)：115-117．
[167] 赵于前，桂卫华，陈真诚．基于自适应数学形态学的医学图像边缘连接[J]．计算机工程，2006，32(22)：17-19．
[168] 赵鹏，浦昭邦．基于形态学4子带分解金字塔的图像融合[J]．光学学报，2007，27(1)：39-44．
[169] 阚江明，李文彬．基于数学形态学的树木图像分割方法[J]．北京林业大学学报，2006，28(2)：151-156．
[170] 徐志平，张世永，钟亦平．基于交叉视觉皮质模型的图像形态学处理研究[J]．计算机辅助设计与图形学学报．2007，19卷，第8期(国内权威，EI索引，已录用)
[1] Sharon B, Wells R B. VLSI implementation of neuromime pulse generator for Eckhorn neurons [J]. Electronics Letters, 2004, 40(18): 1143-1144.
[2] Eckhorn R. Neural mechanisms of scene segmentation: recordings from the visual cortex suggest basic circuits for linking field models [J]. IEEE Transactions on Neural Networks, 1999, 10(3): 464-479.
[3] Eckhom R, Bauer R, Jordan W. Coherent oscillations: A mechanism of feature linking in the visual cortex? [J]. Biological Cybernetics, 1988, 60 (2): 121-130.
[4] Eckhorn R, Frien A, Bauer R. High frequency (60-90 Hz) oscillations in primary visual cortex of awake monkey [J]. Neurorepon, 1993, 4(3): 243-246.
[5] Eckhorn R. Oscillatory and non-oscillatory synchronizations in the visual cortex and their, possible roles in associations of visual features [J]. Progress in Brain Research, 1994, 102: 405-426.
[6] 余群明，王耀南．基于振荡型混沌神经网络的智能信息处理研究[J]．自动化学报，2002，28(3)：401-407．
[7] 焦贤发，王如彬．刺激下可变祸合神经振了群活动的非线性随机演化模型[J]．控制与决策，2005，20(8)：897-900．
[8] 汪云九，唐孝威，吴建永．意识的计算神经科学研究[J]．科学通报，2001，46(13)：1140-1144．
[9] 张建州，张宇．外积取等联想记忆神经网络部分同步收敛性[J]．系统工程与电子技术，2002，24(11)：19-21．
[10] 石美红，张军英，李永刚．基于差别特征的纹理图像识别研究[J]．计算机应用，2004，24(1)：66-69．
[11] Ekblad U, Kinserb J M, Atmera J. The intersecting cortical model in image processing [J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2004, 525(1): 392-396.
[12] Ekblad U, Kinser J M. Theoretical foundation of the intersecting conical model and its use for change detection of aircraft, cars, and nuclear explosion tests[J]. Signal Processing, 2004, 84(7): 1131-1146.
[13] Ekblad U, Kinserb J M, Atmer J, et al. Image information content and extraction techniques[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2004, 525(2): 397-401.
[14] 黄英，王成波．生物电及其在医学中的应用[J]．局解手术学杂志，2002，11(4)：389-399．
[15] 伍国锋，贵阳，张文渊．脑电波产生的神经生理机制[J]．临床脑电学杂志，2000，9(3)：188-190．
[16] Cai S P, Shen J, Doi T. Optical mapping of brainstem neuronal activity evoked by auditory electro-stimulation in rats [J]. Chinese Journal of Clinical Rehabilitation, 2005, 9(29): 219-222.
[17] McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity[J]. Bulletin of Mathematical Biophysics, 1943, 5: 115-133.
[18] Hebb D O. The Organization of Behavior [M]. New York: Wiley, 1949.
[19] Rosenblatt F. The perception: A probabilistic model for information storage and organization in the brain [J]. Psychological Review, 1958, 65: 386-408.
[20] Widrow B, Hoff M E. Adaptive switching circuits[A]. In: Neurocomputing: foundations of research [M]. Cambridge, MA, USA: MIT Press, 1988:123-134.
[21] Minsky M, Papert S.Perceptions [M]. Cambridge MA: MIT Press, 1969.
[22] Kohonen T. Correlation matrix memories[A]. In: Neurocomputing: foundations of research [M]. Cambridge, MA, USA: MIT Press, 1988: 171-180.
[23] Anderson J A. A simple neural network generating an interactive memory[J]. Mathematical Biosciences, 1972, 14: 197-220.
[24] Grossberg S. Adaptive pattern classification and universal recoding: Ⅰ. Parallel development and coding of neural feature detectors [J]. Biological Cybernetics, 1976, 23: 121-134.
[25] Hopfleld J J. Neural networks and physical systems with emergent collective computational properties[A]. In: Proceedings of the National Academy of Sciences[C], 1982,79: 2554-2558.
[26] Rumelhart D E, McClelland J L. Parallel Distributed Processing: Explorations in the Microstructure of Cognition [M]. Cambridge, MA: MIT Press, 1986, 1.
[27] Pawlak Z. Rough Sets [J]. Communication of ACM, 1995, 38(11): 89-95.
[28] Eckhorn R, Reitboeck H J, Arndt M, et al. Feature linking via stimulus-evoked oscillations: experimental results from cat visual cortex and functional implications from a network model[A]. In: International Joint Conference on Neural Networks[C]. Washington DC, 1989: 723-730.
[29] Ekblad U, Kinser J M. Theoretical foundation of the intersecting cortical model and its use for change detection of aircraft, cars and nuclear explosion tests [J]. Signal Processing , 2004, 84(7): 1131—1146.
[30] Ekblad U, Kinser J M, Atmer J, et al. The intersecting cortical model in image processing [J]. Nuclear Instruments & Methods In Physics Research, Section A, 2004, 525(2) : 392-396.
[31] 赵卫东, 李旗号. 粗集在数据开采中的应用[J].系统工程学报, 2002, 17(4): 575-579.
[32] Jelonek J. Rough Set Reducts of Attributes and Their Domains for Neural Networks[J]. Computational Intelligence, 1995, 11(2) :339-347.
[33] Szczuka M S. Rough Set Methods for Constructing Artifical Neural Network[J]. American Society of Mechanial Engineers, 1996,79(7): 1203-1206.
[34] Banerjee M. Rough Fuzzy MLP: Knowledge Encoding and Classification[J]. IEEE Transactions On Neural Networks, 1998,9(6): 1203-1206.
[35] Cornfield J. Statistical classification methods[A]. In: Proceedings of the Second Conference on the Diagnostic Process, Computer Diagnosis and Diagnostic Methods[C]. Chicago, 1972: 108-130.
[36] Devijver P A, Kittler J. Pattern Recognition, a Statistical Approach[M].Englewood Cliffs, London: Prentice Hall, 1982.
[37] Fukunaga K. Introduction to Statistical Pattern Recognition, 2nd Edition[M]. New York: Academic Press, 1990.
[38] Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation[A], In: Rumelhart D E. Parallel Distributed Processing: Explorations in the microstructure of Cognition Vol. I [C]. Cambridge: MIT Press, 1986:319-362.
[39] Pal N R, Pal S K. A review on image segmentation techniques[J]. Pattern Recognition, 1993,26(9): 1277-1294.
[40] Adler A, Guardo R. A neural network image reconstruction technique for electrical impedance tomography [J]. IEEE Transaction on Medical Imaging, 1994, 13(4): 594-600.
[41] Srinivasan V, Han Y K, Ong S H. Image reconstruction by a Hopfield neural network[J]. Image Vision Computing, 1993, 11(5): 278-282.
[42] Meyer R R, Heindl E. Reconstruction of off-axis electron holograms using a neural net[J]. Journal of Microscopy, 1998, 191(1): 52-59.
[43] Wang Y M, Wahl F M. Vector-entropy optimization-based neural-network approach to image reconstruction from projections[J].IEEE Transaction on Neural Networks, 1997, 8(5): 1008-1014.
[44] Greenhil D, Davies E R. Relative effectiveness of neural networks for image noise suppression[A]. In: Proceedings of the Pattern Recognition in Practice IV[C]. Vlieland, 1994:367-378.
[45] Ridder D, Duin R P, Verbeek P W. The applicability of neural networks to nonlinear image processing[J]. Pattern Analysis & Applications, 1999, 2(2): 111-128.
[46] Chua W, Yang L. Cellular networks: theory [J]. IEEE Trans. Circuits Systems, 1988, 35(10): 1257-1272.
[47] Hanek H, Ansari N. Speeding up the generalized adaptive neural filters[J]. IEEE Trans. Image Process, 1996, 5(5):705-712.
[48] Russo F. Hybrid neuro-fuzzy filter for impulse noise removal[J]. Pattern Recognition, 1999,32(11):1843-1855.
[49] Matsumoto T, Kobayashi H, Togawa Y. Spatial versus temporal stability issues in image processing neuro chips[J]. IEEE Trans. Neural Networks, 1992,3 (4): 540-569.
[50] Bedini L, Tonazzini A. Image restoration preserving discontinuities: the Bayesian approach and neural networks[J]. Image Vision Comput, 1992,10(2): 108-118.
[51] Chandrasekaran V, Palaniswami M, Caelli T M. Range image segmentation by dynamic neural network architecture[J]. Pattern Recognition, 1996, 29(2):315-329.
[52] Pugmire R H, Hodgson R M, Chaplin R I. The properties and training of a neural network based universal window filter developed for image processing tasks[A]. In: S. Amari, N. Kasabov (Eds.), Brain-like computing and intelligent information systems[C]. Springer-Verlag, Singapore, 1998: 49-77.
[53] Shih F Y, Moh J, Chang F C. A new ART-based neural architecture for pattern classification and image enhancement without prior knowledge [J]. Pattern Recognition, 1992,25(5): 533-542.
[54] Amerijckx C, Verleysen M, Thissen P. Image compression by self-organized Kohonen map[J]. IEEE Trans. Neural Networks, 1998,9(3):503-507.
[55] Daugman J G. Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression[J]. IEEE Trans. Acoustics, Speech Signal Process, 1988, 36(7): 1169-1179.
[56] Brause R W, Rippl M. Noise suppressing sensor encoding and neural signal orthonormalization[J]. IEEE Trans. Neural Networks, 1998, 9(4):613-628.
[57] Amerijckx C, Verleysen M, Thissen P. Image compression by self-organized Kohonen map[J]. IEEE Trans. Neural Networks, 1998, 9 (3):503-507.
[58] Mitra S, Yang S Y. High fidelity adaptive vector quantization at very low bit rates for progressive transmission of radiographic images[J]. J. Electron. Imaging, 1999, 8(1):23-35.
[59] Morris R J T, Rubin L D, Tirri H. Neural network techniques for object orientation detection: solution by optimal feedforward network and learning vector quantization approaches[J]. IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12 (11):1107-1115.
[60] Dony R D, Haykin S. Optimally adaptive transform coding[J]. IEEE Trans. Image Process., 1995, 4(10): 1358-1370.
[61] Oja E. A simplified neuron model as a principal component analyzer[J].J. Math. Biol., 1982,15(3):267-273.
[62] Baldi P, Hornik J. Neural networks and principal component analysis: learning from examples without local minima[J]. Neural Networks, 1989,2(1):53-58.
[63] Kepuska V Z, Mason S O. A hierarchical neural network system for signalized point recognition in aerial photographs[J]. Photogrammetric Eng. Remote Sensing, 1995, 61(7):917-925.
[64] Shustorovich A. A subspace projection approach to feature extraction-the 2-D Gabor transform for character recognition[J]. Neural Networks, 1994,7(8): 1295-1301.
[65] Patel D, Davies E R, Hannah I. The use of convolution operators for detecting contaminants in food images[J]. Pattern Recognition, 1996, 29(6): 1019-1029.
[66] Glass J O, Reddick W E. Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma[J]. Magn. Resonance 2 Imaging. 1998,16 (9): 1075-1083.
[67] Fukumi M, Omatu S, Nishikawa Y. Rotation-invariant neural pattern recognition system estimating a rotation angie[J]. IEEE Trans. Neural Networks, 1997, 8(3):568-581.
[68] Williams CKI, Revow M, Hinton G E. Instantiating deformable models with a neural net[J]. Comput. Vision Image Understand, 1997,68(1): 120-126.
[69] Hall L O, Bensaid A M, Clarke L P. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain[J]. IEEE Trans. Neural Networks, 1992,3(5):672-682.
[70] Handels H, Busch C, Encarnacao J. KAMEDIN: A telemedicine system for computer supported cooperative work and remote image analysis in radiology[J]. Comput. Methods Programs Biomed., 1997,52(3): 175-183.
[71] Manjunath B S, Simchony T, Chellappa R. Stochastic and deterministic networks for texture segmentation[J]. IEEE Trans. Acoustics, Speech Signal Process., 1990,38(6): 1039-1049.
[72] DeKruger D, Hunt B R. Image processing and neural networks for recognition of cartographic area features[J]. Pattern Recognition, 1994,27(4):461-483.
[73] Laine A, Fan J. Texture classification by wavelet packet signatures[J]. IEEE Trans.Pattern Anal. Mach. Intell.,1993, 15(11): 1186-1191.
[74] Raghu P P, Poongodi R, Yegnanarayana B. Unsupervised texture classification using vector quantization and deterministic relaxation neural network[J]. IEEE Trans. Image Process., 1997, 6(10): 1376-1387.
[75] Haring S, Viergever M A, Kok J N. Kohonen networks for multiscale image segmentation[J]. Image Vision Comput, 1994, 12(6): 339-344.
[76] Petersen M E, Arts T. Recognition of radiopaque markers in X-ray images using a neural network as nonlinear filter[J]. Pattern Recognition Letter, 1999,20(5):521-533.
[77] Carpenter G A, Grossberg S, Lesher G W. The what-and-where filter-a spatial mapping neural network for object recognition and image understanding[J]. Comput. Vision Image Understand, 1998, 69(1): 1-22.
[78] Shen J Y, Zhang Y X, Mu G G. Optical pattern recognition system based on a winner-take-all model of a neural network[J]. Opt. Eng., 1992, 32(5): 1053-1056.
[79] Penedo M G, Carreira M J, Mosquera A. Computer-aided diagnosis: a neural-network-based approach to lung nodule detection[J]. IEEE Trans. Med. Imag., 1998, 17(6):872-880.
[80] Watanabe M. Reward expectancy in primate prefrontal neurons [J]. Nature, 1996, 382: 521-535.
[81] Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. Journal of Physiology, 1962, 160:106-154.
[82] Schwartz E L, Rojer A S. Cortical hypercolumns and the topology of random orientation maps [J]. Pattern Recognition, 1994, 2:150-155.
[83] Rodieck R W, Stone J. Analysis of receptive fields of cat retinal ganglion cells [J]. Journal of Neurophysiology, 1965, 28: 833-849.
[84] Kruizinga Peter, Nikolay Petkov. Nonlinear Operator for Oriented Texture [J]. IEEE Transactions on Image Processing, 1999,8(10): 1395-1407.
[85] Yuen P C, Feng G C, Zhou J P. A contour detection method: Initialization and contour model [J]. Pattern Recognition Letters, 1999,20(8): 141-148.
[86] 杨谦，齐翔林，汪云九．简单细胞方位选择性感受野组织形成的神经网络模型[J]．中国科学(C辑)，2000，30(4)：412-420．
[87] 杨谦，齐翔林．视觉皮层复杂细胞时空编码特性[J]．生物物理学报，2000，16(2)：280-287．
[88] 李速，齐翔林，胡宏，汪云九．功能柱结构神经网络模型中的同步振荡现象[J]．中国科学(C辑)，2004，34(4)：385-394．
[89] 杨谦，齐翔林，汪云九．视皮层V1区简单细胞的稀疏编码策略[J]．计算物理，2001， 18(2): 143-146.
[90] Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve[J]. Journal of Physiology,1952, 117:500-544.
[91] FitzHugh R. Impulses and phsyiological states in theoretical models of nerve membrane[J]. Biophysics J., 1961,1: 445-466.
[92] Eckhorn R, Reitboeck H J, Arndt M. Feature linking via synchronization among distributed assemblies: Simulations of results from Cat Visual Cortex[J]. Neural Computing, 1990,2:293-307.
[93] Labbi A,Milanese R,Bosch H. A network of FitzHugh-Nagumo oscillators for object segmentation[A]. Proc. of International Symposium on Nonlinear Theory and Applications, NOLTA'97[C],Hawaii, 1997: 581-584.
[94] Rybak I A, Shevtsova N A, Sandier V A. The model of a Neural Network visual processor[J]. Neurocomputing, 1992 4:93-102.
[95] Young M P. The large scale organization of the primate cortical visual system[A]. SPIE[C], 1994, 2054:185-193.
[96] Gray Charles M. Oscillatory Responses in Cat Visual Cortex Exhibit Inter-Columnar Synchronization Which Reflects Global Stimulus Properties[J]. Nature, 1989, 555: 334-337.
[97] Neven H, Aertsen A. Rate coherence and event coherence in the visual cortex: a neuronal model of object recognition[J]. Biological Cybernetics, 1992, 57:309-322.
[98] Eckhom R. Feature linking via stimulus-evoked oscillations: experimental results from cat visual cortex and functional implications from a network model[A]. IJCNN: International Joint Conference on Neural Networks[C], 1989:723-730.
[100] 卡斯尔曼．数字图像处理[M]．北京：清华大学出版社，1998：414-416．
[101] 陈贺新．非线性滤波与数字图像处理[M]．北京：国防工业出版社，1997．
[102] 赵春晖，王伟．全方位多结构元形态滤波器．中国图像图形学报，1997．2(8)：629-633．
[103] 崔屹，图像处理与分析数学形态学方法及应用[M]．北京：科学出版社，2000：126-149．
[104] 王伟，赵春晖，孙圣和．最优全方位结构元约束层叠滤波[J]．中国图像图形学报，1999，4(6)：445-449．
[105] 王伟，赵春晖，孙圣和．基于层叠处理的多机自适应WOS滤波器[J]．中国图像图形学报，2000，5(8)：670-683．
[106] 张薇，沈允春，张曙．堆栈滤波的递归实现[J]．中国图像图形学报，2000，5(10)：851-856．
[107] 黄贤武，王加俊，李家华．数字图像处理与压缩编码技术[M]．成都：电子科技大学出版社，2000．12．
[108] Akopian D, Vainio O, Agaian S, et al. Processors for generalized stack filters[J]. IEEE Trans Signal Processing, 1995, 43(6): 1541-1546.
[109] Kuosmanen P, Astola J. Optimal stack filters under rank selection and structural constraints[J]. Signal Processing,1995,41:309-338
[110] Farina A. Linear and non-linear filters for clutter cancellation in radar systems.[J], SIGNAL Processing, 1997, 59(1): 101-112.
[111] Pasian F, Crise A. Restoration of signals degraded by impulse noise by means of a low-distortion non-linear filter[J], Signal Processing, 1984, 6(1): 67-76.
[112] Henriksen R. The truncated 2nd-order non-linear filter revisited[J]. IEEE Transactions on automatic control, 1982, 27(1): 247-251.
[113] 梁雯，刘松林．改进的中心加权中值滤波[J]．中国图像图形学报，1997，2(8，9)：629-633．
[114] 冯星奎，肖兴明，尹洪君．方向加权中值滤波算法[J]．中围图像图形学报，2000，5(7)：609-611．
[115] 马义德，张在峰，张祥光．基于全方位结构元层叠滤波的混合滤波器[J]．甘肃科学学报，2002，14(2)：56-60．
[116] 马义德，张祥光，戴若兰，李廉．一种改进的最优全方位结构元约束层叠滤波设计方案[J]．兰州大学学报(自然科学版)，2001，37(3)：52-55．
[117] 黄煦涛．二维数字信号处理—变换与中值滤波[M]．北京：科学出版社，1985．
[118] 赵春晖，乔景洵，孙圣和．一类多结构元自适应广义形态滤波器[J]．中国图像图形学报，1997，2(11)：806-809．
[120] Power W, Clist R. Comparison of supervised learning techniques applied to color segmentation of fruit image[A]. In: Proceeding of SPIE, Intelligent Roberts and Computer Vision. ⅩⅤ: Algorithms, Techniques, Active Vision, and Material Handing[C]. Boston, MA, USA, 1996: 370-381.
[121] Hance G A, Umbaugh S E, Moss R H,et al.Unsupervised color image segmentation with application to skin tumor borders[J]. IEEE Engineering inMedicine and Biology, 1996,15(1): 104-111.
[122] Pal N R, Pal S K. A review on image segmentation techniques [J]. Pattern Recognition, 1993, 26(9): 1277-1294.
[123] Ohlander R, Price K, Reddy D R. Picture segmentation using a recursive region splittingmethod [J]. ComputerGraphics and Image Processing, 1978,8(3): 313-333.
[124] Guo G D,Yu S, Ma S D. Unsupervised segmentation of color images [A]. In: Proceeding of 1998 IEEE International Conference on Image Processing[C]. Chicago, IL, USA, 1998: 299-302.
[125] Underwood S A, Aggarwal J K. Interactive computer analysis of aerial color infrared photographs[J]. Computer Graphics and Image Processing, 1977,6(1): 1-24.
[126] Kurugollu F, Sankur B, Harmanci A E. Color image segmentation using histogram multithresholding and fusion [J]. Image and Vision Computing, 2001, 19(13): 915-928.
[127] Celenk M. A color clustering technique for image segmentation[J]. ComputerVision, Graphics, and Image Processing, 1990, 52(2): 145-170.
[128] Ahuja N, Haralick R M, Rosenfeld A. Neighbor gray levels as features in pixel classification [J]. Pattern Recognition, 1980, 13(4): 251-260.
[129] 刘重庆，程华．分割彩色图像的一种有效聚类方法[J]．模式识别与人工智能，1995，8(A01)：133-138．
[130] Carevic D, Caelli. T. Region-based coding of color image using Karhunen-Loeve Transform [J]. Graphics Models and Image Processing, 1997,59(1): 27-38.
[131] 王兴伟，沈兰荪，卫保国等．基于改进的K-均值聚类和数学形态学的彩色眼科图像病灶分割[J]．中国生物医学工程学报，2002，21(5)：443～448．
[132] Bezdek J C. Pattern recognition with fuzzy objective function algorithms [M]. New York: Plenum Press, 1981.
[133] Xie X L, BeniG. A validitymeasure for fuzzy clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(8): 841-847.
[134] Zahid N, LimouriM, Esseaid A. A new cluster-validity for fuzzy clustering [J]. Pattern Recognition, 1999,32(5): 1089-1097.
[135] Ferri F, Vidal E. Color image segmentation and labeling through multi-edit condensing[J]. PatternRecognition Letters, 1992,13(8): 561-568.
[136] Lim Y W, Lee S U. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques [J]. Pattern Recognition, 1990,23(9): 935-952.
[137] 刘健庄，谢维信．高效的彩色图像塔形模糊聚类分割方法[J]．西安电子科技大学学报，1993，20(1)：40-46．
[138] 林开颜，徐立鸿，吴军辉．快速模糊C均值聚类彩色图像分割方法[J]．中国图象图形学报，2004，9(2)：159-163．
[139] Chen T Q, Lu Y. Color image segmentation—An innovative approach[J]. Pattern Recognition, 2002,35(2): 395-405.
[140] Michael T U, Arbib A. Color image segmentation using competitive learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994,16(12): 1197-1206.
[141] Ohta Y, Kanade T, Sakai T. Color information for region segmentation[J]. Computer Graphics and Image Processing, 1980, 13(3): 222-241.
[142] Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations[J]. IEEE Transactions on PatternAnalysis andMachine Intelligence, 1991, 13(6): 583-598.
[143] Lezoray O, Cardot H. Cooperation of color pixel classification schemes and color watershed: a study for microscopic images[J]. IEEE Transactions on Image Processing, 2002, 11(7): 783-789.
[144] Shafarenko L, Petrou M, Kittler J. Automatic watershed segmentation of randomly textured color images[J]. IEEE Transactions on Image Processing, 1997,6(11): 1530-1544.
[145] Shiji A, Hamada N. Color image segmentation method using watershed algorithm and contour information[A]. In: Proceeding of 1999 IEEE International Conference on Image Processing[C]. Kobe, Japan, 1999: 305-309.
[146] 马丽红，张宇，邓健平．基于形态开闭滤波二值标记和纹理特征合并的分水岭算法[J]．中国图象图形学报，2003，8(1)：77-83．
[147] Geman S, Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(11): 721-741.
[148] Celeux G, Forbes F, Peyrard N. EM procedures using mean field-like approximations forMarkovmodel-based image segmentation [J]. Pattern Recognition 2003,36(1): 131-144.
[149] Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm [J]. IEEE Transactions on Medical Image, 2001, 20(1): 45-57.
[150] Huang C L. Parallel image segmentation using modified Hopfield model [J]. Pattern Recognition Letters, 1993, 13(5): 345-353.
[151] Campadelli P, Medici D, Schettini R. Color image segmentation usingHopfield networks[J]. Image and Vision Computing, 1997, 15(3): 161-166.
[152] Sammouda M, Sammouda R, Niki N, et al.Segmentation and analysis of liver cancer pathological color image based on artificial neural networks [A]. In: Proceeding of IEEE 1999 International Conference on Image Processing [C]. Kobe, Japan, 1999: 392-396.
[153] Sammouda R, Sammouda M. Improving the performance of Hopfield neural network to segment pathological liver color images [J]. International Congress Series, 2003, 1256: 232-239.
[154] Ong S H, Yeo N C, Lee K H, et al. Segmentation of color images using a two-stage self-organizing network[J]. Image and Vision Computing, 2002,20(4): 279-289.
[155] Papamarkos N, Strouthopoulous C, Andreadis I. Multithresholding of color and gray-level images through a neural network technique[J]. Image and Vision Computing, 2000, 18(3): 213-222.
[156] Lescure P, Yedid V M, DupoisotH,et al.Color segmentation on biological microscope images [A]. In: Proceeding of SPIE, Application of Artificial NeuralNetworks in Image Processing Ⅳ[C]. San Jose, California, USA, 1999: 182-193.
[157] Ozden M, Polat E. A color image segmentation approach for content-based image retrieval[J]. Pattern Recognition, 2007, 40: 1318-1325.
[162] Bovik A L. Handbook of Image And Video Processing [M]. New York: Academic Press, 2000.
[163] 杨波，汪同庆，吕永平，等．利用动态结构元素提取直线[J]．计算机辅助设计与图形学学报，2003，15(4)：421-424．
[164] 岳洪伟，李扬，蔡肯，等．数学形态学在图像处理中的应用与展望[J]．影像技术，2006．2：19-21．
[165] Kenneth．R．Castleman Digital Image Processing Printice-Hall，Inc[M]．北京：清华大学出版社，1998,4．
[166] 颜七笙，王士同．调节形态学运算及其神经网络实现[J]．计算机工程与设计，2007，28(1)：115-117．
[167] 赵于前，桂卫华，陈真诚．基于自适应数学形态学的医学图像边缘连接[J]．计算机工程，2006，32(22)：17-19．
[168] 赵鹏，浦昭邦．基于形态学4子带分解金字塔的图像融合[J]．光学学报，2007，27(1)：39-44．
[169] 阚江明，李文彬．基于数学形态学的树木图像分割方法[J]．北京林业大学学报，2006，28(2)：151-156．
[99] 徐志平，钟亦平，张世永．用于脉冲噪声图像的交叉视觉皮质模型滤波[J]．计算机辅助设计与图形学学报，2007，19卷，第6期(国内权威，EI索引，已录用)
[119] 徐志平，钟亦平，张世永．丛于交叉视觉皮质模型的脉冲噪声红外图像滤波算法[J]．小型微型计算机系统(国内权威，已录用)
[158] Zhiping XU, Yiping ZHONG, Shiyong ZHANG, Fast Shape Index Framework based on Principle Component Analysis using Edge Co-occurrence Matrix,Lecture Notes AI, 2006,4253: 390-397. (SCI索引，EI索引)
[159] Zhiping XU, Shiyong ZHANG, Yiping ZHONG. A Novel Image Semantic Block Clustering Method based on Artificial Visual Cortical Responding Model[A]. In: 2007 IEEE Symposium on Computational Intelligence and Data Mining[C], April 1-5, 2007,Honolulu, Hawaii, USA(EI索引, 已录用)
[160] Zhiping XU, Yiping ZHONG, Shiyong ZHANG, Artificial Visual Cortical Responding Model in Image Semantic Processing[A]. In: 2007 ACS/IEEE International Conference on Computer Systems and Applications[C]. (SCI索引，EI索引，已录用)
[161] Zhiping Xu, Jinghong Pan, Shiyong Zhang. A Novel Automatic Framework for Scoliosis X-Ray Image Retrieval[A]. In: 2007 International Joint Conderence on Neural Networks, IJCNN 2007[C].(SCI索引，EI索引，已录用)
[170] 徐志平，张世永，钟亦平．基于交叉视觉皮质模型的图像形态学处理研究[J]．计算机辅助设计与图形学学报．2007，19卷，第8期(国内权威，EI索引，已录用)