机器视觉水中图像特征提取与对象辨识研究
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摘要
机器视觉在海洋工程中用于对被观测物体进行视觉监控、精密定位和非接触测量。随着海洋开发和研究的深入,以及国防安全的需要,作为海洋工程高新技术研究的重要组成部分的智能水下探测器得到了广泛的应用。智能水下探测器在环境恶劣且复杂多变的海洋里作业时,常常要进行定位、识别、避障与路径规划等动作行为,因而使得其视觉系统显得尤为重要。对水下机器视觉系统的研究是一项具有挑战性的课题,具有重要的理论意义和实用价值。
特征检测是图像处理中的一项基本技术。根据图像匹配、合成及对象辨识的要求,特征检测方法需要较精确的特征定位,并能同时检测出角点、边缘等图像中不同的显著性特征。本文对机器视觉水中图像(包括水下图像和水面图像)特征提取与匹配技术中下述重点内容进行了研究。
(1)基于蚁群优化技术的水中图像分割算法。图像分割是图像理解与图像识别的基础,其分割质量的好坏对后续图像处理的效果会有直接的影响。就水中图像的特点来说,其图像具有模糊性与信息的不可加性,传统的图像分割方法不能满足要求。因此,本文基于蚁群算法,设计了一种智能化的水中图像分割方法,该方法利用图像分割技术的本质,将水中图像里各个像素进行自动分类,最终达到分割图像的目的。在像素分类的过程中,通过引入信息熵和均值聚类等概念,对基本蚁群算法进行了改进,从而使得本方法在水中图像分割中具有自适应性、鲁棒性、并行性和快速收敛性等特点。
(2)经验模式分解算法和相位信息相结合的水中图像检测分析技术。二维经验模式分解算法可以实现图像的多尺度结构分析,能够对图像进行融合、降噪、边界特征提取和图像压缩等方面的处理；相位信息是图像中最稳定、最重要的特性之一。因而本文在对经验模式分解算法和相位信息进行分析与综合的基础上,提出了一种用于水中图像特征检测分析的EP模型。该模型充分继承了上述两种方法的优点,可以用于水中图像的处理与分析,实现了水中图像的多尺度、多像素边缘特征提取,提高了图像中目标的匹配定位精度,还可以完成图像的多尺度分割。
(3)基于尺度不变特征检测的水中图像匹配技术。针对特征点匹配对于尺度变化比较敏感的问题,本文基于SIFT特征对图像的旋转和尺度的不变性,以及对于噪声、光照变化和视角改变等具有良好鲁棒性等优点,并考虑到水中弱光环境特征和不同的实验场合,提出了一种改进的基于SIFT特征的水中图像配准策略。该方法有效地提高了水中图像匹配的精度和速度,较好地解决了尺度变化给图像配准带来的影响,使得在水中进行较大尺度变化的图像匹配拼接成为可能。
(4)基于纹理特征的图像型船舶尾流分类辨识技术。通常来说,船舶尾流的诸多特性与船舶的船体线型、主机和螺旋桨的布置及参数等因素有关,并与船舶航行时的浮态与速度、所航行海域的海况、温度、盐度和海水密度分布等信息也有关。本文以图像型船舶尾流为研究对象,采用局部二进制模式和灰度共生矩阵方法,检测海面尾流的自然纹理形貌和共生统计特征,并将提取出来的特征用作BP神经网络的分类输入向量,建立了图像型船舶尾流自动分类辨识系统,经过对五种航速下的尾流图像的识别测试,实验结果表明用该方法进行的尾流目标检测平均正确识别率可达到80%以上。
Machine vision is used for visual monitoring, precision positioning and non-contact measurement of the observed objects in ocean engineering. With the further advance of ocean research and the national security needs, the intelligent underwater detector has been widely used as an important part of ocean engineering high-tech research. The actions such as object location, identification, barrier-avoiding and path planning are often taken by the smart underwater detector in the underwater surroundings, so complexities and uncertainties of working environments make the vision system of the detector stand out especially, further upgrading the performance of machine vision system for underwater research is not only a challenging task, but also has important theoretical significance and practical value.
The extraction of the features is the basic technique of the image processing. According to the need of the matching and object recognition and image synthesis, the method of feature detection can detect the different features of corners and edges and precision of the characteristics location are relatively good. This dissertation deals with the following key issues in machine vision underwater and surface image feature extraction and matching techniques research.
(1) Underwater image segmentation based on an ant colony optimization algorithm. Image segmentation is the basis of the image understanding and recognition, and the segmentation quality can directly affect on the results of the subsequent image processing. However, as far as the underwater image is concerned, the fuzzy and non-additive information of the image make the traditional method of image segmentation hardly meet the requirements. So a method of image segmentation based on the intelligent ant colony algorithm is designed in this paper, each pixel of an image is classified by analyzing the nature of image segmentation, and ultimately achieving the purpose of image segmentation. In the classification process, the basic ant colony algorithm has been improved by adopting the concepts of the entropy and clustering method, which making the underwater image segmentation program with self-adaptability, robustness, parallelism and fast convergence and so on.
(2) The detection of underwater image based on the empirical mode decomposition algorithm and the phase information. Two-dimensional empirical mode decomposition algorithm can achieve multi-scale image structure analysis, and deal with some problems of image such as the image fusion and noise reduction and feature extraction and the image compression and so on; in addition, the phase information is one of the most stable and important features of an image. Therefore, the EP model is proposed based on the feature detection for underwater image analysis by analyzing and synthesizing the empirical mode decomposition method and the phase information. This model is fully inherited the advantages of the two methods, and can be used for underwater image processing and analysis. The multi-scale and multi-pixel edge detection is achieved for an underwater image. And the positioning accuracy of the matching target is also improved. The multi-scale image segmentation can be completed based on this model.
(3) The underwater image matching technology based on the scale-invariant feature detection. Feature points matching algorithm is sensitive to the image scale change. In order to overcome this problem, an improved SIFT-based image registration scheme is proposed. The improved registration strategy can solve the above problem by using the rotation and scaling invariant property of the SIFT feature points as well as its robustness to added noise, illumination change and viewpoint change, and taking into account underwater environment with the low-light characteristic and different experimental situations. The proposed algorithm is effective in improving the accuracy and the speed of the image matching, as well as solves the scale changes to the influence of image registration, which makes it possible to achieve successful underwater image registration and mosaic when large scale change occurs.
(4) The problem of ship wakes identify based on the image texture feature. In general, many features of the ship wakes relate to the information of the ship's hull, geometric scale, the host's position in the boat, the propeller geometry parameter and its working conditions and other factors. In addition, the ship's speed and the navigation direction, as well as the information of the salinity, temperature, and density of sea water are also relevant with the ship wakes. In a word, the ship wake is widely studied in the ship design, marine environment monitoring and remote sensing areas, as well as in naval air force reconnaissance. It plays important roles in actual practice and military. This paper, taking a ship wakes image as an object of research, using the local binary patterns and the GLCM method to detect the natural texture and the statistical characteristics of the symbiotic as the input vectors of the BP neural network, establishes an automatic identification system for the ship wakes image. This method is applied to sort and recognize the ship wakes of five different speeds images, the result shows that the detection accuracy is satisfied as expected, the average correctness rates of wakes target recognition at the five speeds may be achieved over80%.
特征检测是图像处理中的一项基本技术。根据图像匹配、合成及对象辨识的要求,特征检测方法需要较精确的特征定位,并能同时检测出角点、边缘等图像中不同的显著性特征。本文对机器视觉水中图像(包括水下图像和水面图像)特征提取与匹配技术中下述重点内容进行了研究。
(1)基于蚁群优化技术的水中图像分割算法。图像分割是图像理解与图像识别的基础,其分割质量的好坏对后续图像处理的效果会有直接的影响。就水中图像的特点来说,其图像具有模糊性与信息的不可加性,传统的图像分割方法不能满足要求。因此,本文基于蚁群算法,设计了一种智能化的水中图像分割方法,该方法利用图像分割技术的本质,将水中图像里各个像素进行自动分类,最终达到分割图像的目的。在像素分类的过程中,通过引入信息熵和均值聚类等概念,对基本蚁群算法进行了改进,从而使得本方法在水中图像分割中具有自适应性、鲁棒性、并行性和快速收敛性等特点。
(2)经验模式分解算法和相位信息相结合的水中图像检测分析技术。二维经验模式分解算法可以实现图像的多尺度结构分析,能够对图像进行融合、降噪、边界特征提取和图像压缩等方面的处理；相位信息是图像中最稳定、最重要的特性之一。因而本文在对经验模式分解算法和相位信息进行分析与综合的基础上,提出了一种用于水中图像特征检测分析的EP模型。该模型充分继承了上述两种方法的优点,可以用于水中图像的处理与分析,实现了水中图像的多尺度、多像素边缘特征提取,提高了图像中目标的匹配定位精度,还可以完成图像的多尺度分割。
(3)基于尺度不变特征检测的水中图像匹配技术。针对特征点匹配对于尺度变化比较敏感的问题,本文基于SIFT特征对图像的旋转和尺度的不变性,以及对于噪声、光照变化和视角改变等具有良好鲁棒性等优点,并考虑到水中弱光环境特征和不同的实验场合,提出了一种改进的基于SIFT特征的水中图像配准策略。该方法有效地提高了水中图像匹配的精度和速度,较好地解决了尺度变化给图像配准带来的影响,使得在水中进行较大尺度变化的图像匹配拼接成为可能。
(4)基于纹理特征的图像型船舶尾流分类辨识技术。通常来说,船舶尾流的诸多特性与船舶的船体线型、主机和螺旋桨的布置及参数等因素有关,并与船舶航行时的浮态与速度、所航行海域的海况、温度、盐度和海水密度分布等信息也有关。本文以图像型船舶尾流为研究对象,采用局部二进制模式和灰度共生矩阵方法,检测海面尾流的自然纹理形貌和共生统计特征,并将提取出来的特征用作BP神经网络的分类输入向量,建立了图像型船舶尾流自动分类辨识系统,经过对五种航速下的尾流图像的识别测试,实验结果表明用该方法进行的尾流目标检测平均正确识别率可达到80%以上。
Machine vision is used for visual monitoring, precision positioning and non-contact measurement of the observed objects in ocean engineering. With the further advance of ocean research and the national security needs, the intelligent underwater detector has been widely used as an important part of ocean engineering high-tech research. The actions such as object location, identification, barrier-avoiding and path planning are often taken by the smart underwater detector in the underwater surroundings, so complexities and uncertainties of working environments make the vision system of the detector stand out especially, further upgrading the performance of machine vision system for underwater research is not only a challenging task, but also has important theoretical significance and practical value.
The extraction of the features is the basic technique of the image processing. According to the need of the matching and object recognition and image synthesis, the method of feature detection can detect the different features of corners and edges and precision of the characteristics location are relatively good. This dissertation deals with the following key issues in machine vision underwater and surface image feature extraction and matching techniques research.
(1) Underwater image segmentation based on an ant colony optimization algorithm. Image segmentation is the basis of the image understanding and recognition, and the segmentation quality can directly affect on the results of the subsequent image processing. However, as far as the underwater image is concerned, the fuzzy and non-additive information of the image make the traditional method of image segmentation hardly meet the requirements. So a method of image segmentation based on the intelligent ant colony algorithm is designed in this paper, each pixel of an image is classified by analyzing the nature of image segmentation, and ultimately achieving the purpose of image segmentation. In the classification process, the basic ant colony algorithm has been improved by adopting the concepts of the entropy and clustering method, which making the underwater image segmentation program with self-adaptability, robustness, parallelism and fast convergence and so on.
(2) The detection of underwater image based on the empirical mode decomposition algorithm and the phase information. Two-dimensional empirical mode decomposition algorithm can achieve multi-scale image structure analysis, and deal with some problems of image such as the image fusion and noise reduction and feature extraction and the image compression and so on; in addition, the phase information is one of the most stable and important features of an image. Therefore, the EP model is proposed based on the feature detection for underwater image analysis by analyzing and synthesizing the empirical mode decomposition method and the phase information. This model is fully inherited the advantages of the two methods, and can be used for underwater image processing and analysis. The multi-scale and multi-pixel edge detection is achieved for an underwater image. And the positioning accuracy of the matching target is also improved. The multi-scale image segmentation can be completed based on this model.
(3) The underwater image matching technology based on the scale-invariant feature detection. Feature points matching algorithm is sensitive to the image scale change. In order to overcome this problem, an improved SIFT-based image registration scheme is proposed. The improved registration strategy can solve the above problem by using the rotation and scaling invariant property of the SIFT feature points as well as its robustness to added noise, illumination change and viewpoint change, and taking into account underwater environment with the low-light characteristic and different experimental situations. The proposed algorithm is effective in improving the accuracy and the speed of the image matching, as well as solves the scale changes to the influence of image registration, which makes it possible to achieve successful underwater image registration and mosaic when large scale change occurs.
(4) The problem of ship wakes identify based on the image texture feature. In general, many features of the ship wakes relate to the information of the ship's hull, geometric scale, the host's position in the boat, the propeller geometry parameter and its working conditions and other factors. In addition, the ship's speed and the navigation direction, as well as the information of the salinity, temperature, and density of sea water are also relevant with the ship wakes. In a word, the ship wake is widely studied in the ship design, marine environment monitoring and remote sensing areas, as well as in naval air force reconnaissance. It plays important roles in actual practice and military. This paper, taking a ship wakes image as an object of research, using the local binary patterns and the GLCM method to detect the natural texture and the statistical characteristics of the symbiotic as the input vectors of the BP neural network, establishes an automatic identification system for the ship wakes image. This method is applied to sort and recognize the ship wakes of five different speeds images, the result shows that the detection accuracy is satisfied as expected, the average correctness rates of wakes target recognition at the five speeds may be achieved over80%.
引文
[1]黄明哲.海洋奥秘——高技术与海洋[M].北京：中国科学技术出版社,2011.
[2]陈亮.基于图像处理的水下目标识别方法研究[D].哈尔滨：哈尔滨工程大学,2006.
[3]张魏.水下图像的目标检测与定位研究[D].武汉：华中科技大学,2007.
[4]唐旭东.智能水下机器人水下管道检测与跟踪技术研究[D].哈尔滨：哈尔滨工程大学,2010.
[5]吕春望.海底管道的自主探测与识别技术研究[D].哈尔滨：哈尔滨工程大学,2007.
[6]王卫华,陈卫东,席裕庚.基于视觉的水下管线识别与定位系统[J].计算机工程,2000,26(2)：17-18.
[7]陈荣盛,袁小海,胡震等.基于水下三维目标识别的视觉系统[J].中国造船,1997,4(139)：88-94.
[8]陈荣盛,袁小海,胡震等.水下机器人光视觉信息获取及处理算法[J].船舶力学,1999,3(2)：63-69.
[9]陈荣盛,袁小海,胡震等.用于目标检测和精确定位的水下机器人视觉系统[J].中国造船,2000,41(2)：89-94.
[10]林焰,陈明,于雁云.水下侧扫声纳搭载装置：中国,ZL201220554171.5[P].2012,10,26.
[11]Balasuriya A, Ura T. Vision-based underwater cable detection and following using AUVs [C]. Proceedings of the Oceans 2002 Conference and Exhibition. Piscataway, NJ, USA:IEEE,2002: 1582-1587.
[12]Passball的专栏.关于图像特征提取(CSDN博客)[EB/OL].2010-01-17 [2011-08-20].http: //blog. csdn.net/passball/article/details/5204132.
[13]夏定元.基于内容的图像检索通用技术研究及应用[D].武汉：华中科技大学,2004.
[14]王申.敏感图像关键部位识别研究[D].兰州：兰州大学,2009.
[15]John R. Smith, Shih-Fu Chang. VisualSEEk:a fully automated content-based image query system [C]. Proceeding of MULTIMEDIA'96 Proceedings of the fourth ACM international conference on Multimedia, NY, USA:ACM,1996:87-98.
[16]M. Striker, M. Orengo. Similarity of Color Images [C]. Proceeding of Storage and Retrieval for Image and Video Databases,1995:381-392.
[17]Greg Pass. Comparing images using color coherence vectors [C]. Proceeding of MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia, NY, USA:ACM,1996:65-73.
[18]Calvin C. Gotlieb, Herbert E. Kreyszig. Texture descriptors based on co-occurrence matrices [J]. Computer Vision, Graphics, and Image Processing,1990,51(1):70-86.
[19]Benoit B. Mandelbrot. The fractal geometry of nature [M]. Times Books,1982.
[20]Garcia R, Cufi X, Carreras M. Estimating the motion of an underwater robot from a monocular image sequence [C]. Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent, Maui, Hawaii:Robots and Systems,2001,3:1682-1687.
[21]Cufi X, Garcia R, Ridao P. An approach to vision-based station keeping for an unmanned underwater vehicle [C]. Intelligent Robots and Systems,2002. IEEE/RSJ International Conference,2002,1:799-804.
[22]Lim D L H. Design of a vision system for an autonomous underwater vehicle [D]. Australia: University of Western Australia,2004.
[23]Chua K, Arshad M. Robotics Vision-based Heuristic Reasoning for Underwater Target Tracking and Navigation [J]. International Journal of Advanced Robotic Systems,2005,2(3): 245-250.
[24]Johnson H. Probing for life in the ocean crust with the LEXEN program [J]. Transactions of the American Geophysical Union,2003,84(12):109-116.
[25]Whitcomb L, Howland J, Smallwood D, et al. A new control system for the next generation of US and UK deep submergence oceanographic ROVs [C]. Proceedings of the 1st IFAC Workshop on Guidance and Control of Underwater Vehicles,2003:137-142.
[26]Woods Hole Oceanographic Institution. Remotely Operated Vehicle Jason/Medea [EB/OL]. [2011-08-21]. http://www.whoi.edu/ndsfVehicles/Jason.
[27]张铁栋.潜水器设计原理[M].哈尔滨：哈尔滨工程大学出版社,2010.
[28]AUV System Spec Sheet. Tantan configuration [EB/OL]. [2011-08-21]. http://auvac. Org/configurations/view/147.
[29]中华网.“蛟龙”号载人潜水器坐底最终下潜深度6908米[EB/OL]. [2011-08-21].http:// news.china.com/focus/kejin/11104958/20120619/17267755.html.
[30]王献孚,熊鳌魁.高等流体力学[M].华中科技大学出版社,2003.
[31]岳升好,吕欣荣,张存有,周伟东.螺旋桨尾流流场的数值计算[J].大连海事大学学报,2004,30(1)：29-30.
[32]K. Oumansour, Y. Wang and J. Saillard. Multifrequency SAR observation of a ship wake [J]. IEEE Proc., Radar Sonar Navig.1996,143(4):275-280.
[33]N.R. Stapleton. Ship wakes in radar imagery [J]. International Journal of Remote Sensing,1997, 18(6):1381-1386.
[34]Jin Min Kuo, Kun Shan Chen. Ship wake detection in synthetic aperture radar images using a combination of a wavelet correlator and Radon transform [J]. Optical Engineering,2002,41(3): 686-696.
[35]Hennings I, et al. Radar imaging of Kelvin arms of ship wakes [J]. Int. J. Remote Sensing,1999, 20(13):2519-2543.
[36]Munk W H. Ships from space [J]. Proceedings of the Royal Society of London,1987, A412(8): 231-254.
[37]Copeland A C. Localized radon transform-based detection of shipwakes in SAR images [J]. IEEETrans,1995, GRS-33(1):35-45.
[38]李恪,王江安,郭谊.细化算法在舰船热尾流红外图像处理中的应用[J].红外技术,2007,29(11)：648-650.
[39]Andrea Gelmetti, Tommaso Bellini, Paolo Lago et al. An optical interferometer for gas bubble measurements [J]. Rev. Sci. Instrum, October 1996,67(10):3564-3566.
[40]张建生,吕青,孙传东等.高速摄影术对水中气泡运动规律的研究[J].光子学报,2000,29(10)：952-955.
[41]M. D. Stokes, G. B. Deane. A new optical instrument for the study of bubbles at high void fractions within breaking waves [J]. IEEE Journal of Oceanic Engineering.1999,24(3): 300-311.
[42]Marta E. Bailey. Analysis of bubble size distributions using the McGill bubble size analyzer [D]. Canada:McGill University,2004.
[43]Claudio Abraham Acuna Perez. Measurement techniques to characterize bubble motion in swarms [D]. Canada:McGill University,2007.
[44]张蓉生,郑源,程云山.微小气泡粒径的测量研究[J].实验流体力学,2005,19(2)：91-95.
[45]赵晓飞,何俊华,韦明智等.一种基于图像处理技术获取尾流特性的新方法[J].光子学报.2006,35(3)：443-446.
[46]张建生,康筱锋,李玉清,刘长安.船舶尾流目标识别的随机梯度遗传算法研究[J].西安工业大学学报,2007,27(1)：78-82.
[47]D.G.Lowe. Object recognition from local scale-invariant features [C]. Proceedings of the Seventh IEEE International Conference on Computer Vision. Kerkyra, Greece:IEEE Xplore, 1999:1150-1157.
[48]H.Bay. From Wide-baseline Point and Line Correspondences to 3D [D]. Zurich:ETH,2006.
[49]邓燕萍.水下图像的特征提取和神经网络识别技术研究[D].哈尔滨：哈尔滨工程大学,2005.
[50]朱炜.基于粒子群的水下图像分割与识别技术研究[D].哈尔滨：哈尔滨工程大学,2008.
[51]章毓晋.图像工程(上册、中册)——图像处理(第2版)[M].北京：清华大学出版社,2007.
[52]胡志萍.图像特征提取、匹配和新视点图像生成技术研究[D].大连：大连理工大学,2005.
[53]Hartley R, Zisserman A. Mutliple view geometry [C]. In CVRP,1999:44-106.
[54]徐华.基于三维重构的人脸识别[D].成都：电子科技大学,2009.
[55]刘勇.基于局部灰度信息的图像匹配[D].上海：复旦大学,2000.
[56]R. Szeliski. Image alignment and stitching:A tutorial [R]. Preliminary draft, Technical Report, 2005.
[57]A. Peng, B. Dubuisson, M. Benjelloun. A study on the forms of smoothing filters for step and ramp edge detection [J]. Pattern Recognition,1992:741-744.
[58]K. H. Liang, T. Tjahjadi, Y. H. Yang. Roof edge detection using regularized cubic B-spline fitting [J]. Pattern Recognition,1997,30(5):719-728.
[59]张汗灵MATLAB在图像处理中的应用[M].北京：清华大学出版社,2008.
[60]L. G. Roberts. Machine percepetion of three-dimensional solids [D]. MIT:Department of Electrical Engineering,1963.
[61]I. Sobel. An isotropic 33 image gradient operator [M]. Machine Vision for Three-Dimensional Scenes. Stanford:Academic Press,1990. pp.376-379.
[62]J. M. S. Prewitt. Object enhancement and extraction [M]. Picture Processing and Psychopictorics. Philadelphia:Academic Press,1970.
[63]D. Marr, E. C. Hildreth. Theory of edge detection [J]. Proceedings of the Royal Society of London, Series B,1980,207:187-217.
[64]J. Canny. A computational approach to edge detection [J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1986,8(4):679-714.
[65]S. M. Smith, M. Brady. SUSAN—a new approach to low level image processing [J]. International Journal of Computer Vision,1997,23(1):45-78.
[66]罗忠亮.基于改进SUSAN算子的图像边缘检测算法[J].重庆工学院学报(自然科学),2009,23(5)：102-106.
[67]M. C. Morrone, R. A. Owens. Feature Detection from Local Energy [J]. Pattern Recog. Lett. 1987,6:303-313.
[68]M. C. Morrone, D. C. Burr. Feature Detection in Human Vision:A Phase-Dependent Energy Model [J]. Proc.R.Soc.Lond. B, Biol.Sci.1988,235(1280):221-245.
[69]H. P. Moravec. Towards Automatic Visual Obstacle Avoidance [C]. Proc.5th Internati- onal Joint Conference on Artificial Intelligence.1977:584-590.
[70]C. Harris, M. Stephens. A combined comer and edge detector [C]. Fourth Alvey Vision Conference.1988:189-192.
[71]K. Mikolajczyk, C. Schmid. Scale and affine invariant interest point detectors [J]. International Journal of Computer Vision,2004,60(1):63-86.
[72]T. Lindeberg. Feature Detection with Automatic Scale Selection [J]. IEEE Transactions Pattern Analysis Machine Intelligence,1998,30(2):77-116.
[73]D. G. Lowe. Local feature view clustering for 3D object recognition [C]. IEEE Conference on Computer Vision and Pattern Recognition. Kauai, Hawaii:IEEE,2001:682-688.
[74]D. G. Lowe. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision,2004,60(2):91-110.
[75]H. Bay, B. Fasel, L. V. Gool. Interactive museum guide:fast and robust recognition of museum ob-jects [C]. Proceedings of the First International Workshop on Mobile Vision.2006.
[76]H. Bay, T. Tuytelaars, L. V. Gool. SURF:Speeded up robust features [J]. Computer Vision and Image Understanding (CVIU),2008,110(3):346-359.
[77]李铁军,陈哲,王任享.基于尺度不变特征变换的图像快速拼接算法[J].微计算机信息,2008,4(3)：282-283.
[78]Dai Y. C.基于尺度不变特征变换的图像匹配研究(CSDN博客)[EB/OL].2007-05-07 [2011-10-20].http://www.8100.cc/link.php?url=http://blog. csdn.net%2 fdaiyuchao % 2farchive%2 f2007%2f05%2f.
[79]J. J. Koenderink. The structure of images [J]. Biological Cybernetics,1984,50:363-396.
[80]T. Lindeberg. Detecting salient blob-like image structures and their scales with a scale-space primal sketch:a method for focus-of-attention [J]. International Journal of Computer Vision, 1993,11(3):283-318.
[81]T. Lindeberg. Scale-space theory:A basic tool for analyzing structures at different scales [J]. Journal of Applied Statistics,1994,21(2):224-270.
[82]郑海珍.图像拼接技术的研究与应用[D].杭州：杭州电子科技大学,2010.
[83]J. Bauer, N. Sunderhauf, P. Protzel. Comparing Several Implementations of Two Recently Published Feature Detectors [C]. In Proc. of the International Conference on Intelligent and Autonomous Systems, IAV. Toulouse, France,2007.
[84]吕行军,韩宪忠,王克俭等.基于最大方差闽值法的火车票图像二值化处理[J].计算机应用与软件,2012,29(7)：249-253.
[85]吴成茂.基于核空间的Otsu阈值法[J].数据采集与处理,2010,25(6)：761-765.
[86]Peng W, Wenlong S. An image edge detection algorithm of plant roots based on support vector machine [J]. Acta Agriculturae Zhejiangensis,2012,24(4):721-726.
[87]姜红军,冯立异,平子良.数学形态学早期思想的形成[J].西北大学学报(自然科学版),2011,41(6)：1111-1116.
[88]李梦亮,翁正新.基于改进区域生长法和霍夫变换的车道分割法[J].计算机应用与软件,2011,28(12)：721-726.
[89]M. Dorigo, T. Stutzle. Ant Colony Optimization [M]. Cambridge:Massachusetts:The MIT Press,2004.
[90]M. Dorigo, M. Birattari, T. Stutzle. Ant Colony Optimization-Artificial Ants as a Computational Intelli-gence Technique [J]. IEEE Computation Intelligence Magazine,2006,1: 28-39.
[91]田沛,范瑾,李亮.基于类间方差和形态学的一类生物特征识别[J].清华大学学报(自然科学版),2007,47(2)：1747-1750.
[92]谢江,汪同庆.基于最大类间方差法的快速交通视频运动目标分割方法[J].计算机系统应用,2010,19(9)：151-154.
[93]Shannon C. E. A Mathematical Theory of Communication [J]. Bell System Technical Journal, 1948,27:379-429.
[94]马良,朱刚,宁爱兵.蚁群优化算法[M].北京：科学出版社,2008.
[95]段海滨.蚁群算法原理及其应用[M].北京：科学出版社,2005.
[96]刘德平,刘晓宇,陈建军.蚁群算法数据点排序技术研究[J].塑性工程学报,2008,15(6)：157-161.
[97]Malisia A. R., Tizhoosh H. R. Image Thresholding Using Ant Colony Optimization [C]. Third Cana-dian Conference on Computer and Robot Vision, CRV2006. Proceedings of Thirteenth International Symposium on Temporal Representation and Reasoning, TIME,2006.
[98]Zhao X, Lee M, Kim S. Improved Image Thresholding using Ant Colony Optimization Algo-rithm [C]. Proceedings of ALPIT 2008,7th International Conference on Advanced Language Processing and Web Information Technology.2008:210-215.
[99]Piatri T, Izquierdo E. Image Classification Using an Ant Colony Optimization Approach [J]. Springer-Verlag Berlin Heidelberg, Semantic Multimedia,2006,4306:159-168.
[100]Zhang W, Mao L, Xu W. Automatic Image Classification Using the Classification Ant Colony Algorithm [C]. Proceedings 2009 International Conference on Environmental Science and Information Application Technology, ESIAT.2009,3:325-329.
[101]Mullen J, Monekosso D, Barman S, et al. Artificial Ants to Extract Leaf Outlines and Primary Venation Patterns [C]. Lecture Notes In Computer Science, Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence.2008,5217:251-258.
[102]Guo Y., Wei Z., Zhang G. An Application of Ant Colony Algorithm for Edge Feature Extraction in City Aerial Image for Buiding Recognition [C]. The Eighth International Conference on Electronic Measurement and Instruments, ICEMI.2007,2:944-947.
[103]Wu Y., Hu Y., Lei W., et al. Edge Detection of Laser Range Image Based on a Fast Adaptive Ant Colony Algorithm [C]. Advances in Swarm Intelligence. Springer Verlag Berlin Heidelberg,2010,6145:265-272.
[104]Prakash V. O., Madasu H., Puneet K., et al. A Novel Approach for Edge Detection using Ant colony Optimization and Fuzzy Derivative Technique [C]. IEEE International Advance Computing Conference, IACC.2009:1206-1212.
[105]杨瑞臣,云庆夏.改进的蚁群算法在矿山物流配送路径优化中的研究[J].中国钼业,2004,28(6)：16-18.
[106]Pilat M. L., White T. Using genetic algorithms to optimize ACS-TSP [C]. Proceedings of Ant Algorithms:Third International Workshop. Brussels, Belgium.2002:282-287.
[107]丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法的融合[J].计算机研究与发展,2003,40(9)：1351-1356.
[108]王慧.改进的蚁群聚类分析算法的研究[D].河南：河南大学数学与信息科学学院,2009.
[109]苗京,黄红星,程卫生.基于蚁群模糊聚类算法的图像边缘检测[J].武汉大学学报(工学版),2005,38(5)：124-127.
[110]Shelokar P. S., Jayaraman V. K., Kulkarni B. D. An ant colony approach for clustering [J]. Elsevier, Ams-terdam, Analytica Chimica Acta,2004,509:187-195.
[111]韩彦芳,施鹏飞.基于蚁群算法的图像分割方法[J].计算机工程与应用,2004,40(18)：5-7.
[112]欧先锋,贾振红,郝军.多结构抗噪膨胀——腐蚀型数字灰度图像边缘检测的研究[J].激光杂志学报,2009,30(1)：40-41.
[113]张羽,徐端全.OpenCV分水岭算法的改进及其在细胞分割中的应用[J].计算机应用,2012,32(1)：134-136.
[114]祝儒德.风机齿轮箱监测诊断系统的研究与实现[D].哈尔滨：哈尔滨工业大学,2009.
[115]张海勇,马孝江,盖强.一种新的时频分析方法[J].火力与指挥控制,2000,25(3)：39-42.
[116]Huang E., Shen Z., Long S. R. The Empirical Mode Decomposition Method and the Hilbert Spectrum for Non-Stationary Time Series Analysis [J]. Proc. Royal. Soc. London A,1998, 454(A):903-995.
[117]徐争光.经验模式分解的数学理论研究[D].武汉：华中科技大学,2009.
[118]Nunes J. C., Niang O., Bouaoune Y., et al. Texture analysis based on the bidimensional empirical mode decomposition with gray-level co-occurrence models [C]. Signal Processing and Its Applications, Seventh International Symposium.2003,2:633-635.
[119]Nunes J. C., Guyot S., Delechelle E. Texture analysis based on local analysis of the Bidimensional Empirical Mode Decomposition [J]. Machine Vision and Applications,2005, 16(3):177-188.
[120]Nunes J. C., Bouaoune Y., Delechelle E., et al. Image analysis by bidimensional empirical mode decomposition [J]. Image and Vision Computing,2003,21(12):1019-1026.
[121]Rilling G., Flandrin P., Goncalves P., et al. Bivariate Empirical Mode Decomposition [J]. Signal Processing Letters, IEEE,2007,14(12):936-939.
[122]何佳英,张珣.基于多信号瞬时频率捕捉血压信号特定点的研究[J].机电工程,2009,26(12)：17-19.
[123]田岩,彭复员.数字图像处理与分析[M].武汉：华中科技大学出版社,2009.
[124]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,2002.
[125]Jean Serra. Image analysis and mathematical morphology [M]. London:Academic Press, Inc. Orlando, FL, USA.1983.
[126]周培德.平面散乱点线集三角剖分的算法[J].计算机辅助设计与图形学学报,2003,9(15)：1141-1144.
[127]Xu Y., Liu B., Liu J., et al. Two-dimensional empirical mode decomposition by finite elements [J]. Proceedings of the Royal Society A,2006,462(2074):3081-3096.
[128]吴宗敏.径向基函数、散乱数据拟合与无网格偏微分方程数值解[J].工程数学学报,2002,19(2)：1-12.
[129]Nur Arad, Daniel Reisfeld. Image Warping Using Few Anchor Points and Radial Functions [J]. Computer Graphics Forum,1995,14(1):35-46.
[130]聂煊,赵荣椿,等.一种改进的基于径向基函数图像变形方法[J].计算机科学,2005,32(4)：102-103.
[131]Antonio Criminisi, Patrick Perez, Kentaro Toyama Object Removal by Exemplar-based Inpainting [C]. Proc. of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2003,2:721-728.
[132]David J. Fleet, Allan D. Jepson. Stability of phase information [J]. IEEE Trans PAMI,1993, 15(12):1253-1268.
[133]M. Concetta Morrone, John Ross, David C. Burr, et al. Mach bands are phase dependent [J]. Nature,1986,324:250-253.
[134]Kovesi P. Image Features from Phase Congruency [J]. Videre:A Journal of Computer Vision Research. MIT Press,1999,1(3):1-27.
[135]黄令允.基于自适应阈值的SIFT算法研究及应用[D].大连：大连理工大学,2010.
[136]Martin A. Fischler, Robert C. Bolles. Random sample consensus:A paradigm for model fitting with application to image analysis and automated cartography [J]. Communications of the ACM CACM Homepage archive,1981,24(6):381-395.
[137]林焰.“新世纪一号”小水线面油田交通船船体线型优化设计与水动力性能试验研究[R].大连理工大学/中国海洋石油总公司,2004年12月.
[138]刘彬,叶丽娜.一种基于SIFT特征的序列图像拼接算法[J].兵工自动化,2009,28(6)：76-78.
[139]杨立,刘慧开.舰船尾流的光特性[J].光学技术,2005,31：253-257.
[140]钱东,韩啸.法国尾流自导仿真和对抗研究评述[J].鱼雷技术,2007,15(1)：8-11.
[141]朱邦元.水面舰艇对抗尾流自导鱼雷的措施及尾流自导鱼雷的对策[J].鱼雷技术,2007,15(5)：11-14.
[142]冯士笮,李凤岐,李少箐.海洋科学导论[M].北京：高等教育出版社,1999.
[143]张晓晖等.舰船尾流激光制导方法的研究[J].激光技术,2005,29(5)：494-496.
[144]Eldhuset K. An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions [J]. Transactions on Geoscience and Remote Sensing,1996,34(4):1010-1019.
[145]Jin M, Chen K. S. The application of wavelets correlator for ship wake detection in SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(6):1506-1511.
[146]Courmontagne Ph. An improvement of ship wake detection based on the radon transforms [J]. Signal Processing,2005,85(8):1634-1654.
[147]Zilman G., Zapolski A., Marom M. SAR imaging of ship wakes and inverse ship wake problem [C]. International Workshop on Water Waves and Floating Bodies,28-31 March, Cortona, Italy, 2004.
[148]林焰.船舶尾流试验方案及测试大纲[R].大连理工大学,2012年6月.
[149]Ojala T., Pietikainen M., et al. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions [C]. Proceedings of the 12th IAPR International Conference on Pattern Recognition.1994,1:582-585.
[150]Baraldi A., Parmiggiani F. An Investigation of the Texture Characteristics Associated with Gray Level Co-occurrence Matrix Statistical Parameters [J]. IEEE Transaction on Geoscience and Remote Sensing,1995,33(2):293-304.
[151]Leen-kiat Soh, Costas Tsatsoulis. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices [J]. IEEE Transactions on Geoscience and Remote Sensing,1999,37(2): 780-795.
[152]Robert M. Haralick, K. Shanmugam, It's Hak Dinstein. Textual Features for Image Classification [J]. Systems, Man and Cybernetics, IEEE Transactions on,1973,3(6):610-621.
[153]张骏.Boosting方法及其在图像理解中的应用研究[D].安徽：合肥工业大学,2009.
[154]王旭.基于BP网络手写数字识别系统的VC++实现[D].长春：吉林大学,2008.
[155]韩力群.人工神经网络教程[M].北京：北京邮电大学出版社,2006.
[156]郑丽霞.复数神经网络及其在说话人识别中的应用[D].上海：东华大学,2011.
[157]唐誉兴.基于人工神经网络和统计理论的混凝土碳化深度预测[D].南宁：广西大学,2004.
[2]陈亮.基于图像处理的水下目标识别方法研究[D].哈尔滨：哈尔滨工程大学,2006.
[3]张魏.水下图像的目标检测与定位研究[D].武汉：华中科技大学,2007.
[4]唐旭东.智能水下机器人水下管道检测与跟踪技术研究[D].哈尔滨：哈尔滨工程大学,2010.
[5]吕春望.海底管道的自主探测与识别技术研究[D].哈尔滨：哈尔滨工程大学,2007.
[6]王卫华,陈卫东,席裕庚.基于视觉的水下管线识别与定位系统[J].计算机工程,2000,26(2)：17-18.
[7]陈荣盛,袁小海,胡震等.基于水下三维目标识别的视觉系统[J].中国造船,1997,4(139)：88-94.
[8]陈荣盛,袁小海,胡震等.水下机器人光视觉信息获取及处理算法[J].船舶力学,1999,3(2)：63-69.
[9]陈荣盛,袁小海,胡震等.用于目标检测和精确定位的水下机器人视觉系统[J].中国造船,2000,41(2)：89-94.
[10]林焰,陈明,于雁云.水下侧扫声纳搭载装置：中国,ZL201220554171.5[P].2012,10,26.
[11]Balasuriya A, Ura T. Vision-based underwater cable detection and following using AUVs [C]. Proceedings of the Oceans 2002 Conference and Exhibition. Piscataway, NJ, USA:IEEE,2002: 1582-1587.
[12]Passball的专栏.关于图像特征提取(CSDN博客)[EB/OL].2010-01-17 [2011-08-20].http: //blog. csdn.net/passball/article/details/5204132.
[13]夏定元.基于内容的图像检索通用技术研究及应用[D].武汉：华中科技大学,2004.
[14]王申.敏感图像关键部位识别研究[D].兰州：兰州大学,2009.
[15]John R. Smith, Shih-Fu Chang. VisualSEEk:a fully automated content-based image query system [C]. Proceeding of MULTIMEDIA'96 Proceedings of the fourth ACM international conference on Multimedia, NY, USA:ACM,1996:87-98.
[16]M. Striker, M. Orengo. Similarity of Color Images [C]. Proceeding of Storage and Retrieval for Image and Video Databases,1995:381-392.
[17]Greg Pass. Comparing images using color coherence vectors [C]. Proceeding of MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia, NY, USA:ACM,1996:65-73.
[18]Calvin C. Gotlieb, Herbert E. Kreyszig. Texture descriptors based on co-occurrence matrices [J]. Computer Vision, Graphics, and Image Processing,1990,51(1):70-86.
[19]Benoit B. Mandelbrot. The fractal geometry of nature [M]. Times Books,1982.
[20]Garcia R, Cufi X, Carreras M. Estimating the motion of an underwater robot from a monocular image sequence [C]. Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent, Maui, Hawaii:Robots and Systems,2001,3:1682-1687.
[21]Cufi X, Garcia R, Ridao P. An approach to vision-based station keeping for an unmanned underwater vehicle [C]. Intelligent Robots and Systems,2002. IEEE/RSJ International Conference,2002,1:799-804.
[22]Lim D L H. Design of a vision system for an autonomous underwater vehicle [D]. Australia: University of Western Australia,2004.
[23]Chua K, Arshad M. Robotics Vision-based Heuristic Reasoning for Underwater Target Tracking and Navigation [J]. International Journal of Advanced Robotic Systems,2005,2(3): 245-250.
[24]Johnson H. Probing for life in the ocean crust with the LEXEN program [J]. Transactions of the American Geophysical Union,2003,84(12):109-116.
[25]Whitcomb L, Howland J, Smallwood D, et al. A new control system for the next generation of US and UK deep submergence oceanographic ROVs [C]. Proceedings of the 1st IFAC Workshop on Guidance and Control of Underwater Vehicles,2003:137-142.
[26]Woods Hole Oceanographic Institution. Remotely Operated Vehicle Jason/Medea [EB/OL]. [2011-08-21]. http://www.whoi.edu/ndsfVehicles/Jason.
[27]张铁栋.潜水器设计原理[M].哈尔滨：哈尔滨工程大学出版社,2010.
[28]AUV System Spec Sheet. Tantan configuration [EB/OL]. [2011-08-21]. http://auvac. Org/configurations/view/147.
[29]中华网.“蛟龙”号载人潜水器坐底最终下潜深度6908米[EB/OL]. [2011-08-21].http:// news.china.com/focus/kejin/11104958/20120619/17267755.html.
[30]王献孚,熊鳌魁.高等流体力学[M].华中科技大学出版社,2003.
[31]岳升好,吕欣荣,张存有,周伟东.螺旋桨尾流流场的数值计算[J].大连海事大学学报,2004,30(1)：29-30.
[32]K. Oumansour, Y. Wang and J. Saillard. Multifrequency SAR observation of a ship wake [J]. IEEE Proc., Radar Sonar Navig.1996,143(4):275-280.
[33]N.R. Stapleton. Ship wakes in radar imagery [J]. International Journal of Remote Sensing,1997, 18(6):1381-1386.
[34]Jin Min Kuo, Kun Shan Chen. Ship wake detection in synthetic aperture radar images using a combination of a wavelet correlator and Radon transform [J]. Optical Engineering,2002,41(3): 686-696.
[35]Hennings I, et al. Radar imaging of Kelvin arms of ship wakes [J]. Int. J. Remote Sensing,1999, 20(13):2519-2543.
[36]Munk W H. Ships from space [J]. Proceedings of the Royal Society of London,1987, A412(8): 231-254.
[37]Copeland A C. Localized radon transform-based detection of shipwakes in SAR images [J]. IEEETrans,1995, GRS-33(1):35-45.
[38]李恪,王江安,郭谊.细化算法在舰船热尾流红外图像处理中的应用[J].红外技术,2007,29(11)：648-650.
[39]Andrea Gelmetti, Tommaso Bellini, Paolo Lago et al. An optical interferometer for gas bubble measurements [J]. Rev. Sci. Instrum, October 1996,67(10):3564-3566.
[40]张建生,吕青,孙传东等.高速摄影术对水中气泡运动规律的研究[J].光子学报,2000,29(10)：952-955.
[41]M. D. Stokes, G. B. Deane. A new optical instrument for the study of bubbles at high void fractions within breaking waves [J]. IEEE Journal of Oceanic Engineering.1999,24(3): 300-311.
[42]Marta E. Bailey. Analysis of bubble size distributions using the McGill bubble size analyzer [D]. Canada:McGill University,2004.
[43]Claudio Abraham Acuna Perez. Measurement techniques to characterize bubble motion in swarms [D]. Canada:McGill University,2007.
[44]张蓉生,郑源,程云山.微小气泡粒径的测量研究[J].实验流体力学,2005,19(2)：91-95.
[45]赵晓飞,何俊华,韦明智等.一种基于图像处理技术获取尾流特性的新方法[J].光子学报.2006,35(3)：443-446.
[46]张建生,康筱锋,李玉清,刘长安.船舶尾流目标识别的随机梯度遗传算法研究[J].西安工业大学学报,2007,27(1)：78-82.
[47]D.G.Lowe. Object recognition from local scale-invariant features [C]. Proceedings of the Seventh IEEE International Conference on Computer Vision. Kerkyra, Greece:IEEE Xplore, 1999:1150-1157.
[48]H.Bay. From Wide-baseline Point and Line Correspondences to 3D [D]. Zurich:ETH,2006.
[49]邓燕萍.水下图像的特征提取和神经网络识别技术研究[D].哈尔滨：哈尔滨工程大学,2005.
[50]朱炜.基于粒子群的水下图像分割与识别技术研究[D].哈尔滨：哈尔滨工程大学,2008.
[51]章毓晋.图像工程(上册、中册)——图像处理(第2版)[M].北京：清华大学出版社,2007.
[52]胡志萍.图像特征提取、匹配和新视点图像生成技术研究[D].大连：大连理工大学,2005.
[53]Hartley R, Zisserman A. Mutliple view geometry [C]. In CVRP,1999:44-106.
[54]徐华.基于三维重构的人脸识别[D].成都：电子科技大学,2009.
[55]刘勇.基于局部灰度信息的图像匹配[D].上海：复旦大学,2000.
[56]R. Szeliski. Image alignment and stitching:A tutorial [R]. Preliminary draft, Technical Report, 2005.
[57]A. Peng, B. Dubuisson, M. Benjelloun. A study on the forms of smoothing filters for step and ramp edge detection [J]. Pattern Recognition,1992:741-744.
[58]K. H. Liang, T. Tjahjadi, Y. H. Yang. Roof edge detection using regularized cubic B-spline fitting [J]. Pattern Recognition,1997,30(5):719-728.
[59]张汗灵MATLAB在图像处理中的应用[M].北京：清华大学出版社,2008.
[60]L. G. Roberts. Machine percepetion of three-dimensional solids [D]. MIT:Department of Electrical Engineering,1963.
[61]I. Sobel. An isotropic 33 image gradient operator [M]. Machine Vision for Three-Dimensional Scenes. Stanford:Academic Press,1990. pp.376-379.
[62]J. M. S. Prewitt. Object enhancement and extraction [M]. Picture Processing and Psychopictorics. Philadelphia:Academic Press,1970.
[63]D. Marr, E. C. Hildreth. Theory of edge detection [J]. Proceedings of the Royal Society of London, Series B,1980,207:187-217.
[64]J. Canny. A computational approach to edge detection [J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1986,8(4):679-714.
[65]S. M. Smith, M. Brady. SUSAN—a new approach to low level image processing [J]. International Journal of Computer Vision,1997,23(1):45-78.
[66]罗忠亮.基于改进SUSAN算子的图像边缘检测算法[J].重庆工学院学报(自然科学),2009,23(5)：102-106.
[67]M. C. Morrone, R. A. Owens. Feature Detection from Local Energy [J]. Pattern Recog. Lett. 1987,6:303-313.
[68]M. C. Morrone, D. C. Burr. Feature Detection in Human Vision:A Phase-Dependent Energy Model [J]. Proc.R.Soc.Lond. B, Biol.Sci.1988,235(1280):221-245.
[69]H. P. Moravec. Towards Automatic Visual Obstacle Avoidance [C]. Proc.5th Internati- onal Joint Conference on Artificial Intelligence.1977:584-590.
[70]C. Harris, M. Stephens. A combined comer and edge detector [C]. Fourth Alvey Vision Conference.1988:189-192.
[71]K. Mikolajczyk, C. Schmid. Scale and affine invariant interest point detectors [J]. International Journal of Computer Vision,2004,60(1):63-86.
[72]T. Lindeberg. Feature Detection with Automatic Scale Selection [J]. IEEE Transactions Pattern Analysis Machine Intelligence,1998,30(2):77-116.
[73]D. G. Lowe. Local feature view clustering for 3D object recognition [C]. IEEE Conference on Computer Vision and Pattern Recognition. Kauai, Hawaii:IEEE,2001:682-688.
[74]D. G. Lowe. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision,2004,60(2):91-110.
[75]H. Bay, B. Fasel, L. V. Gool. Interactive museum guide:fast and robust recognition of museum ob-jects [C]. Proceedings of the First International Workshop on Mobile Vision.2006.
[76]H. Bay, T. Tuytelaars, L. V. Gool. SURF:Speeded up robust features [J]. Computer Vision and Image Understanding (CVIU),2008,110(3):346-359.
[77]李铁军,陈哲,王任享.基于尺度不变特征变换的图像快速拼接算法[J].微计算机信息,2008,4(3)：282-283.
[78]Dai Y. C.基于尺度不变特征变换的图像匹配研究(CSDN博客)[EB/OL].2007-05-07 [2011-10-20].http://www.8100.cc/link.php?url=http://blog. csdn.net%2 fdaiyuchao % 2farchive%2 f2007%2f05%2f.
[79]J. J. Koenderink. The structure of images [J]. Biological Cybernetics,1984,50:363-396.
[80]T. Lindeberg. Detecting salient blob-like image structures and their scales with a scale-space primal sketch:a method for focus-of-attention [J]. International Journal of Computer Vision, 1993,11(3):283-318.
[81]T. Lindeberg. Scale-space theory:A basic tool for analyzing structures at different scales [J]. Journal of Applied Statistics,1994,21(2):224-270.
[82]郑海珍.图像拼接技术的研究与应用[D].杭州：杭州电子科技大学,2010.
[83]J. Bauer, N. Sunderhauf, P. Protzel. Comparing Several Implementations of Two Recently Published Feature Detectors [C]. In Proc. of the International Conference on Intelligent and Autonomous Systems, IAV. Toulouse, France,2007.
[84]吕行军,韩宪忠,王克俭等.基于最大方差闽值法的火车票图像二值化处理[J].计算机应用与软件,2012,29(7)：249-253.
[85]吴成茂.基于核空间的Otsu阈值法[J].数据采集与处理,2010,25(6)：761-765.
[86]Peng W, Wenlong S. An image edge detection algorithm of plant roots based on support vector machine [J]. Acta Agriculturae Zhejiangensis,2012,24(4):721-726.
[87]姜红军,冯立异,平子良.数学形态学早期思想的形成[J].西北大学学报(自然科学版),2011,41(6)：1111-1116.
[88]李梦亮,翁正新.基于改进区域生长法和霍夫变换的车道分割法[J].计算机应用与软件,2011,28(12)：721-726.
[89]M. Dorigo, T. Stutzle. Ant Colony Optimization [M]. Cambridge:Massachusetts:The MIT Press,2004.
[90]M. Dorigo, M. Birattari, T. Stutzle. Ant Colony Optimization-Artificial Ants as a Computational Intelli-gence Technique [J]. IEEE Computation Intelligence Magazine,2006,1: 28-39.
[91]田沛,范瑾,李亮.基于类间方差和形态学的一类生物特征识别[J].清华大学学报(自然科学版),2007,47(2)：1747-1750.
[92]谢江,汪同庆.基于最大类间方差法的快速交通视频运动目标分割方法[J].计算机系统应用,2010,19(9)：151-154.
[93]Shannon C. E. A Mathematical Theory of Communication [J]. Bell System Technical Journal, 1948,27:379-429.
[94]马良,朱刚,宁爱兵.蚁群优化算法[M].北京：科学出版社,2008.
[95]段海滨.蚁群算法原理及其应用[M].北京：科学出版社,2005.
[96]刘德平,刘晓宇,陈建军.蚁群算法数据点排序技术研究[J].塑性工程学报,2008,15(6)：157-161.
[97]Malisia A. R., Tizhoosh H. R. Image Thresholding Using Ant Colony Optimization [C]. Third Cana-dian Conference on Computer and Robot Vision, CRV2006. Proceedings of Thirteenth International Symposium on Temporal Representation and Reasoning, TIME,2006.
[98]Zhao X, Lee M, Kim S. Improved Image Thresholding using Ant Colony Optimization Algo-rithm [C]. Proceedings of ALPIT 2008,7th International Conference on Advanced Language Processing and Web Information Technology.2008:210-215.
[99]Piatri T, Izquierdo E. Image Classification Using an Ant Colony Optimization Approach [J]. Springer-Verlag Berlin Heidelberg, Semantic Multimedia,2006,4306:159-168.
[100]Zhang W, Mao L, Xu W. Automatic Image Classification Using the Classification Ant Colony Algorithm [C]. Proceedings 2009 International Conference on Environmental Science and Information Application Technology, ESIAT.2009,3:325-329.
[101]Mullen J, Monekosso D, Barman S, et al. Artificial Ants to Extract Leaf Outlines and Primary Venation Patterns [C]. Lecture Notes In Computer Science, Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence.2008,5217:251-258.
[102]Guo Y., Wei Z., Zhang G. An Application of Ant Colony Algorithm for Edge Feature Extraction in City Aerial Image for Buiding Recognition [C]. The Eighth International Conference on Electronic Measurement and Instruments, ICEMI.2007,2:944-947.
[103]Wu Y., Hu Y., Lei W., et al. Edge Detection of Laser Range Image Based on a Fast Adaptive Ant Colony Algorithm [C]. Advances in Swarm Intelligence. Springer Verlag Berlin Heidelberg,2010,6145:265-272.
[104]Prakash V. O., Madasu H., Puneet K., et al. A Novel Approach for Edge Detection using Ant colony Optimization and Fuzzy Derivative Technique [C]. IEEE International Advance Computing Conference, IACC.2009:1206-1212.
[105]杨瑞臣,云庆夏.改进的蚁群算法在矿山物流配送路径优化中的研究[J].中国钼业,2004,28(6)：16-18.
[106]Pilat M. L., White T. Using genetic algorithms to optimize ACS-TSP [C]. Proceedings of Ant Algorithms:Third International Workshop. Brussels, Belgium.2002:282-287.
[107]丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法的融合[J].计算机研究与发展,2003,40(9)：1351-1356.
[108]王慧.改进的蚁群聚类分析算法的研究[D].河南：河南大学数学与信息科学学院,2009.
[109]苗京,黄红星,程卫生.基于蚁群模糊聚类算法的图像边缘检测[J].武汉大学学报(工学版),2005,38(5)：124-127.
[110]Shelokar P. S., Jayaraman V. K., Kulkarni B. D. An ant colony approach for clustering [J]. Elsevier, Ams-terdam, Analytica Chimica Acta,2004,509:187-195.
[111]韩彦芳,施鹏飞.基于蚁群算法的图像分割方法[J].计算机工程与应用,2004,40(18)：5-7.
[112]欧先锋,贾振红,郝军.多结构抗噪膨胀——腐蚀型数字灰度图像边缘检测的研究[J].激光杂志学报,2009,30(1)：40-41.
[113]张羽,徐端全.OpenCV分水岭算法的改进及其在细胞分割中的应用[J].计算机应用,2012,32(1)：134-136.
[114]祝儒德.风机齿轮箱监测诊断系统的研究与实现[D].哈尔滨：哈尔滨工业大学,2009.
[115]张海勇,马孝江,盖强.一种新的时频分析方法[J].火力与指挥控制,2000,25(3)：39-42.
[116]Huang E., Shen Z., Long S. R. The Empirical Mode Decomposition Method and the Hilbert Spectrum for Non-Stationary Time Series Analysis [J]. Proc. Royal. Soc. London A,1998, 454(A):903-995.
[117]徐争光.经验模式分解的数学理论研究[D].武汉：华中科技大学,2009.
[118]Nunes J. C., Niang O., Bouaoune Y., et al. Texture analysis based on the bidimensional empirical mode decomposition with gray-level co-occurrence models [C]. Signal Processing and Its Applications, Seventh International Symposium.2003,2:633-635.
[119]Nunes J. C., Guyot S., Delechelle E. Texture analysis based on local analysis of the Bidimensional Empirical Mode Decomposition [J]. Machine Vision and Applications,2005, 16(3):177-188.
[120]Nunes J. C., Bouaoune Y., Delechelle E., et al. Image analysis by bidimensional empirical mode decomposition [J]. Image and Vision Computing,2003,21(12):1019-1026.
[121]Rilling G., Flandrin P., Goncalves P., et al. Bivariate Empirical Mode Decomposition [J]. Signal Processing Letters, IEEE,2007,14(12):936-939.
[122]何佳英,张珣.基于多信号瞬时频率捕捉血压信号特定点的研究[J].机电工程,2009,26(12)：17-19.
[123]田岩,彭复员.数字图像处理与分析[M].武汉：华中科技大学出版社,2009.
[124]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,2002.
[125]Jean Serra. Image analysis and mathematical morphology [M]. London:Academic Press, Inc. Orlando, FL, USA.1983.
[126]周培德.平面散乱点线集三角剖分的算法[J].计算机辅助设计与图形学学报,2003,9(15)：1141-1144.
[127]Xu Y., Liu B., Liu J., et al. Two-dimensional empirical mode decomposition by finite elements [J]. Proceedings of the Royal Society A,2006,462(2074):3081-3096.
[128]吴宗敏.径向基函数、散乱数据拟合与无网格偏微分方程数值解[J].工程数学学报,2002,19(2)：1-12.
[129]Nur Arad, Daniel Reisfeld. Image Warping Using Few Anchor Points and Radial Functions [J]. Computer Graphics Forum,1995,14(1):35-46.
[130]聂煊,赵荣椿,等.一种改进的基于径向基函数图像变形方法[J].计算机科学,2005,32(4)：102-103.
[131]Antonio Criminisi, Patrick Perez, Kentaro Toyama Object Removal by Exemplar-based Inpainting [C]. Proc. of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2003,2:721-728.
[132]David J. Fleet, Allan D. Jepson. Stability of phase information [J]. IEEE Trans PAMI,1993, 15(12):1253-1268.
[133]M. Concetta Morrone, John Ross, David C. Burr, et al. Mach bands are phase dependent [J]. Nature,1986,324:250-253.
[134]Kovesi P. Image Features from Phase Congruency [J]. Videre:A Journal of Computer Vision Research. MIT Press,1999,1(3):1-27.
[135]黄令允.基于自适应阈值的SIFT算法研究及应用[D].大连：大连理工大学,2010.
[136]Martin A. Fischler, Robert C. Bolles. Random sample consensus:A paradigm for model fitting with application to image analysis and automated cartography [J]. Communications of the ACM CACM Homepage archive,1981,24(6):381-395.
[137]林焰.“新世纪一号”小水线面油田交通船船体线型优化设计与水动力性能试验研究[R].大连理工大学/中国海洋石油总公司,2004年12月.
[138]刘彬,叶丽娜.一种基于SIFT特征的序列图像拼接算法[J].兵工自动化,2009,28(6)：76-78.
[139]杨立,刘慧开.舰船尾流的光特性[J].光学技术,2005,31：253-257.
[140]钱东,韩啸.法国尾流自导仿真和对抗研究评述[J].鱼雷技术,2007,15(1)：8-11.
[141]朱邦元.水面舰艇对抗尾流自导鱼雷的措施及尾流自导鱼雷的对策[J].鱼雷技术,2007,15(5)：11-14.
[142]冯士笮,李凤岐,李少箐.海洋科学导论[M].北京：高等教育出版社,1999.
[143]张晓晖等.舰船尾流激光制导方法的研究[J].激光技术,2005,29(5)：494-496.
[144]Eldhuset K. An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions [J]. Transactions on Geoscience and Remote Sensing,1996,34(4):1010-1019.
[145]Jin M, Chen K. S. The application of wavelets correlator for ship wake detection in SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(6):1506-1511.
[146]Courmontagne Ph. An improvement of ship wake detection based on the radon transforms [J]. Signal Processing,2005,85(8):1634-1654.
[147]Zilman G., Zapolski A., Marom M. SAR imaging of ship wakes and inverse ship wake problem [C]. International Workshop on Water Waves and Floating Bodies,28-31 March, Cortona, Italy, 2004.
[148]林焰.船舶尾流试验方案及测试大纲[R].大连理工大学,2012年6月.
[149]Ojala T., Pietikainen M., et al. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions [C]. Proceedings of the 12th IAPR International Conference on Pattern Recognition.1994,1:582-585.
[150]Baraldi A., Parmiggiani F. An Investigation of the Texture Characteristics Associated with Gray Level Co-occurrence Matrix Statistical Parameters [J]. IEEE Transaction on Geoscience and Remote Sensing,1995,33(2):293-304.
[151]Leen-kiat Soh, Costas Tsatsoulis. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices [J]. IEEE Transactions on Geoscience and Remote Sensing,1999,37(2): 780-795.
[152]Robert M. Haralick, K. Shanmugam, It's Hak Dinstein. Textual Features for Image Classification [J]. Systems, Man and Cybernetics, IEEE Transactions on,1973,3(6):610-621.
[153]张骏.Boosting方法及其在图像理解中的应用研究[D].安徽：合肥工业大学,2009.
[154]王旭.基于BP网络手写数字识别系统的VC++实现[D].长春：吉林大学,2008.
[155]韩力群.人工神经网络教程[M].北京：北京邮电大学出版社,2006.
[156]郑丽霞.复数神经网络及其在说话人识别中的应用[D].上海：东华大学,2011.
[157]唐誉兴.基于人工神经网络和统计理论的混凝土碳化深度预测[D].南宁：广西大学,2004.
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