基于机器视觉的强化木地板表面质量检测方法研究
|
摘要
强化木地板符合国家可持续发展的资源政策,在地板行业发展迅速,未来发展空间巨大。强化木地板的外观质量缺陷是影响其使用性能的一项综合性指标。目前,国内仍然依靠人为目测对强化木地板外观质量进行检测,对此,本文提出了基于机器视觉的强化木地板表面质量检测方法的研究课题。
本文选用原北京柯诺森华地板厂生产的特定系列产品作为研究对象,研究缺陷包括生产中常见的干花、缺纸、污斑和纸裂四种。基于机器视觉的强化木地板表面质量检测主要包括强化木地板的图像获取、图像分割、特征参数的计算与提取和分类器设计几部分。图像分割步骤需要完成把缺陷图像从复杂的背景中提取出来的任务;在特征参数的计算与提取环节,需要在分析地板表面图像特点的基础上,选取能够代表合格地板或缺陷地板特征的参数,同时,为使提取参数达到具有可区别性、可靠性、独立性和数量少的要求,降低后期分类器的复杂程度和运算耗时,需要对特征参数进行降维;在分类器设计阶段,需要设计一种分类器能够将合格的强化木地板和存在缺陷的强化木地板赋以不同的表现形式加以识别。本研究针对强化木地板表面质量检测方法进行了比较系统的研究,主要工作和结论有以下几点:
1、在图像分割研究中,论文采用最大类间方差法、基于二维空间的蚁群算法和基于最大熵的遗传算法三种方法对强化木地板图像进行图像分割处理,比较了三种方法针对强化木地板表面缺陷检测的适用性。研究结果显示,最大类间方差法对浅色干花缺陷分割效果不理想;而基于二维空间的蚁群算法对深色缺纸、污斑和纸裂分割效果不理想;基于最大熵的遗传算法对浅色和深色的缺陷分割效果都比较理想,适用于强化木地板表面质量检测的图像分割处理。
2、在特征提取的研究中,本文在分析强化木地板表面图像特点的基础上,提出通过计算地板图像的颜色特征和纹理特征来表达地板图像特性。在颜色特征参数计算中,研究将HIS三维颜色空间通过加权求和降至一维空间,在该一维空间中计算颜色的一阶矩、二阶矩和三阶矩参数。由于三阶矩中出现了复数情况,研究将实部和虚部分别作为两个参数处理。在纹理计算时,分别对0~0、45~0、90~0和135~0四个方向的能量、惯性矩、熵、相关性和局部平稳五个参数计算均值和标准差作为特征参数。
3、针对高维数据会造成算法空间复杂度和时间复杂度指数增加的问题,论文采用PCA线性参数降维方法对特征参数计算环节得到的24维参数进行降维,得到一个新的4维特征参数,有效解决了高维参数对识别速度和存储容量的影响。
4、论文利用神经网络的RBF和BP网络结构对强化木地板表面质量检测进行了分类器设计,并针对特征提取阶段得到的24维和4维特征参数进行了分类识别实验。通过比较得出RBF神经网络在强化木地板表面质量检测方面更具有优势。
In accord with the national sustainable developing policies,laminated flooring is growing rapidly and having enormous room for future growth.The surface quality is an integrated-index which directly influences the application of laminated flooring.However,in domestic laminated flooring production line,outward appearance quality inspection still depends on human visual.Thus,this paper brings in the research of method for surface quality detection of laminated flooring which basing on machine vision.
The studying subject of this paper is a certain laminated flooring produced by former Beijing Kenuo Senhua floor factory,and mainly research on its most common four defects which are frosting, bare substrate,dirt and tearing of impregnated paper.The method for surface quality detection of laminated flooring basing on machine vision includes the image fetching and segmentation,Feature parameter calculating and extraction,and classifier designing.The mission of segmenting is that the target defects from the complicated wood-grain background should be achieved during the stage of image segmentation.And then calculating based on analyzing the laminated flooring defects to find out the feature parameter which could reflect the qualified and the defected flooring's attribute.At this stage, the parameter need to be dimensions reduced as well,in order to make sure the parameter is distinctive, reliable,independent and modicum,and reduce the complexity of classifier meanwhile.When coming to the classifier designing,we want to create one kind of classifier which can assign different identifiers to the qualified floor and defective floor.This paper did a systemic study for Surface Defects Detection of laminated flooring.The main contents are as follow:
1、At the stage of image segmentation,this paper puts forward three image segmentation methods: Image segmentation based on maximum between-cluster variance;Image Segmentation by Ant Colony Algorithm in two-dimension space;Image Segmentation based on Genetic Algorithm and the maximum entropy,and compares their explicabilities on detecting laminated flooring with each other.The segmentation based on OTSU is not applicable for light color defects including frosting;on contrary,the segmentation based on Ant Colony Algorithm is not applicable for dark color defects including bare substrate,dirt and tearing of impregnated paper defects;The method based on Genetic Algorithm has good segmentations for dark and light color defect,so,the method is applicable for surface image segmentation of Laminated flooring.
2、In the research of feature extraction,this paper advances that fetching out the color and grain features of images can express the character of floor based on analyzing the sampled laminated flooring surface image.In the calculation of the color parameter,the HIS three-dimensional space is reduced to one-dimensional space by weighting and summing.Then the first,second and third moment of color moment are calculated in the one-dimensional space.The real and imaginary parts of the third moment are conducted as two features.In the calculation of the texture parameter,the five fetures of energy, moment of inertia,entropy,correlation and local stationarity at four angle directions of 0~0,45~0,90~0 and 135~0 should be calculated and averaged,the standard deviation is resulted as well.
3、Targeting the problem that the high-dimensional data would increase the complexity degree of algorithm space and time,this paper use PCA linear parameter to reduce the twenty-four-dimensional parameter result,and get a new four-dimensional feature parameter,which effectively solve the recognizing-speed and storage-capacity problems caused by high-dimensional data.
4、Paper use RBF and BP neural network structure to progress the classifier design.Then feature parameters of the twenty-four-dimension and the four-dimension are classifier respectively by the two networks.The result shows that the RBF network has more advantage than the BP network in detection of surface defects for laminated flooring after comparing these two networks.
本文选用原北京柯诺森华地板厂生产的特定系列产品作为研究对象,研究缺陷包括生产中常见的干花、缺纸、污斑和纸裂四种。基于机器视觉的强化木地板表面质量检测主要包括强化木地板的图像获取、图像分割、特征参数的计算与提取和分类器设计几部分。图像分割步骤需要完成把缺陷图像从复杂的背景中提取出来的任务;在特征参数的计算与提取环节,需要在分析地板表面图像特点的基础上,选取能够代表合格地板或缺陷地板特征的参数,同时,为使提取参数达到具有可区别性、可靠性、独立性和数量少的要求,降低后期分类器的复杂程度和运算耗时,需要对特征参数进行降维;在分类器设计阶段,需要设计一种分类器能够将合格的强化木地板和存在缺陷的强化木地板赋以不同的表现形式加以识别。本研究针对强化木地板表面质量检测方法进行了比较系统的研究,主要工作和结论有以下几点:
1、在图像分割研究中,论文采用最大类间方差法、基于二维空间的蚁群算法和基于最大熵的遗传算法三种方法对强化木地板图像进行图像分割处理,比较了三种方法针对强化木地板表面缺陷检测的适用性。研究结果显示,最大类间方差法对浅色干花缺陷分割效果不理想;而基于二维空间的蚁群算法对深色缺纸、污斑和纸裂分割效果不理想;基于最大熵的遗传算法对浅色和深色的缺陷分割效果都比较理想,适用于强化木地板表面质量检测的图像分割处理。
2、在特征提取的研究中,本文在分析强化木地板表面图像特点的基础上,提出通过计算地板图像的颜色特征和纹理特征来表达地板图像特性。在颜色特征参数计算中,研究将HIS三维颜色空间通过加权求和降至一维空间,在该一维空间中计算颜色的一阶矩、二阶矩和三阶矩参数。由于三阶矩中出现了复数情况,研究将实部和虚部分别作为两个参数处理。在纹理计算时,分别对0~0、45~0、90~0和135~0四个方向的能量、惯性矩、熵、相关性和局部平稳五个参数计算均值和标准差作为特征参数。
3、针对高维数据会造成算法空间复杂度和时间复杂度指数增加的问题,论文采用PCA线性参数降维方法对特征参数计算环节得到的24维参数进行降维,得到一个新的4维特征参数,有效解决了高维参数对识别速度和存储容量的影响。
4、论文利用神经网络的RBF和BP网络结构对强化木地板表面质量检测进行了分类器设计,并针对特征提取阶段得到的24维和4维特征参数进行了分类识别实验。通过比较得出RBF神经网络在强化木地板表面质量检测方面更具有优势。
In accord with the national sustainable developing policies,laminated flooring is growing rapidly and having enormous room for future growth.The surface quality is an integrated-index which directly influences the application of laminated flooring.However,in domestic laminated flooring production line,outward appearance quality inspection still depends on human visual.Thus,this paper brings in the research of method for surface quality detection of laminated flooring which basing on machine vision.
The studying subject of this paper is a certain laminated flooring produced by former Beijing Kenuo Senhua floor factory,and mainly research on its most common four defects which are frosting, bare substrate,dirt and tearing of impregnated paper.The method for surface quality detection of laminated flooring basing on machine vision includes the image fetching and segmentation,Feature parameter calculating and extraction,and classifier designing.The mission of segmenting is that the target defects from the complicated wood-grain background should be achieved during the stage of image segmentation.And then calculating based on analyzing the laminated flooring defects to find out the feature parameter which could reflect the qualified and the defected flooring's attribute.At this stage, the parameter need to be dimensions reduced as well,in order to make sure the parameter is distinctive, reliable,independent and modicum,and reduce the complexity of classifier meanwhile.When coming to the classifier designing,we want to create one kind of classifier which can assign different identifiers to the qualified floor and defective floor.This paper did a systemic study for Surface Defects Detection of laminated flooring.The main contents are as follow:
1、At the stage of image segmentation,this paper puts forward three image segmentation methods: Image segmentation based on maximum between-cluster variance;Image Segmentation by Ant Colony Algorithm in two-dimension space;Image Segmentation based on Genetic Algorithm and the maximum entropy,and compares their explicabilities on detecting laminated flooring with each other.The segmentation based on OTSU is not applicable for light color defects including frosting;on contrary,the segmentation based on Ant Colony Algorithm is not applicable for dark color defects including bare substrate,dirt and tearing of impregnated paper defects;The method based on Genetic Algorithm has good segmentations for dark and light color defect,so,the method is applicable for surface image segmentation of Laminated flooring.
2、In the research of feature extraction,this paper advances that fetching out the color and grain features of images can express the character of floor based on analyzing the sampled laminated flooring surface image.In the calculation of the color parameter,the HIS three-dimensional space is reduced to one-dimensional space by weighting and summing.Then the first,second and third moment of color moment are calculated in the one-dimensional space.The real and imaginary parts of the third moment are conducted as two features.In the calculation of the texture parameter,the five fetures of energy, moment of inertia,entropy,correlation and local stationarity at four angle directions of 0~0,45~0,90~0 and 135~0 should be calculated and averaged,the standard deviation is resulted as well.
3、Targeting the problem that the high-dimensional data would increase the complexity degree of algorithm space and time,this paper use PCA linear parameter to reduce the twenty-four-dimensional parameter result,and get a new four-dimensional feature parameter,which effectively solve the recognizing-speed and storage-capacity problems caused by high-dimensional data.
4、Paper use RBF and BP neural network structure to progress the classifier design.Then feature parameters of the twenty-four-dimension and the four-dimension are classifier respectively by the two networks.The result shows that the RBF network has more advantage than the BP network in detection of surface defects for laminated flooring after comparing these two networks.
引文
1.艾矫燕.基于计算机视觉的墙地砖颜色分类和缺陷检测研究[D],华南理工大学,2003
2.白雪冰,王克奇,王辉.基于灰度共生矩阵的木材纹理分类方法的研究[J].哈尔滨工业大学学报,2005,37(12):1667-1670
3.边肇祺,张学工等.模式识别(第二版)[M].清华大学出版社,2000
4.陈才扣,宋枫溪,刘永俊.基于散度差准则的隐空间特征抽取方法[J].计算机科学.2006,33(12):174-176
5.陈丹.基于PCA变换的多传感器图像融合质量评价[J].科技广场.2009(1):133-135
6.陈波.基于数学形态学的图像特征提取[J].科技情报开发与经济.2007,17(17):161-162
7.陈惠明,冯素琴,邸未冬.基于数据场的PCA方法在人脸识别中的应用研究[J].福建电脑.2009(3):81-82
8.常青,王立,邢超.基于遗传算法的图像阈值选取[J].计算机工程与应用.2002,22:35-37
9.崔扬.图像检测技术在皮革缺陷检测中的应用研究[D].浙江大学.2004
10.曹顺茂,叶世伟.一种改进的局部线性嵌入算法[J].计算机仿真.2007,24(5):87-90
11.丁金明.金属镀层工件表面缺陷自动检测系统的研究[D].天津大学.2004
12.杜奇,向健勇,袁胜春.一种改进的最大类间方差法[J].红外技术.2003,25(5):33-36
13.关胜晓.机器视觉及其应用发展[J].自动化博览.2005,03:88-92
14.关子明,常文兵.基于主成分分析-RBF神经网络模型的备件预测研究[J].物流科技.2009(4):122-126
15.高宁,张建中.MATLAB在RBF神经网络模型中的应用[J].农业网络信息.2009,(2):110-116
16.高淑兰,刘英臣.强化复合地板的生产工艺及应用前景[J].林业科技.2001,26(2):46-47
17.耿冠宏,孙伟,罗培.神经网络模式识别[J].软件导刊,2008,7(10):81-83
18.高隽,人工神经网络原理及仿真实例[M].机械工业出版社,2003
19.郭德军,宋蛰存.基于灰度共生矩阵的纹理图像分类研究[J].林业机械与木工设备,2005,33(7):21-23
20.何友金,李楠.舰船红外图像边缘检测方法对比研究[J].计算机仿真.2006,23(4):201-203
21.洪龙海,韩丹红.影响强化地板质量的几种因素及控制措施,林业机械与木工设备.2004,32(4):38-39
22.黄国宏,邵惠鹤.一种新的基于构造型RBF神经元网络分类算法[J].控制与决策.2005,20(12):1411-1414
23.黄艺,杜宇人.基于边缘信息的图像分割技术研究[J].现代电子技术.2005,28(5):116-117
24.韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术.2002,24(6):91-94
25.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
26.金聪,彭嘉雄.利用遗传算法实现数字图像分割[J].小型微型计算机系统.2002,23(7):875-877
27.贾花萍.基本神经网络的特征选择与提取方法研究[J].办公自动化.2008,(135):33-35
28.景晓军,蔡安妮,孙景鳌.一种基于二维最大类间方差的图像分割算法[J].通信学报,2001,22(4):71-76
29.孔明,孙希平.一种改进的基于类间方差的阈值分割法[J].华中科技大学学 报,2004,32(7):46-47
30.阚江明.基于计算机视觉的活立木三维重建方法[D].北京林业大学,2008
31.芦蓉,沈毅.一种改进的二维直方图的图像阈值分割方法[J].系统工程与电子技术,2004,26(10):1487-1490
32.陆振波,章新华,康春玉.基于支持向量机的水中目标识别[J].信息与控制,2003,z1:739-742
33.李小泉.自动检测系统关键技术研究[D].武汉理工大学.2006
34.李立源,龚坚,陈维甫.基于二维灰度直方图最佳一维投影的图像分割方法[J].电子学报,1996,22(3):315-323
35.李红梅,宁功德.影响强化木地板质量因素的分析[J].林业机械与木工设备.2004,32(06):33-34
36.李国友,姚磊等.基于优化的RBF神经网络模式识别新方法[J].系统仿真学报.2006,18(1):181-184
37.李辉,吕英华.在遗传算法基础上的自动阈值选取方法[J],东北师大学报,2006,38(2):36-38
38.刘京南,陈从颜,余玲玲等.一种快速二维熵阈值分割算法[J].计算机应用研究,2002(1):67-70
39.刘红冰,基于神经网络的冷轧带钢表面缺陷检测与分类技术研究[D].电子科技大学,2005
40.刘婧然,马英杰,杨武建.预测水稻螟虫发生趋势的径向基函数人工神经网络模型及其与BP网络的比较[J].内蒙古农业科技.2009(1):26-28
41.刘循,游志胜.多尺度形态学图像边缘检测方法[J].光电工程,2003,1(3):56-58
42.刘维群,李为华.基于自组织选取中心的广义RBF神经网络学习算法{J}.信阳师范学院学报:自然科学版.2007,20(4):515-517
43.廖义奎,王志文.一种新的彩色图像特征提取方法及其应用[J].广西民族学院学报(自然科学版),2003,9(3):48-52
44.梁瑞鑫,孙长银,穆朝旭.基于二维投影特征提取的人脸识别算法[J].计算机工程与应用.2009,45(10):191-194
45.柳稼航等,一种基于优先搜索方向的边界跟踪算法[J].遥感技术与应用.2004,19(3):209-213
46.牟洪波,基于人工神经网络的木材缺陷检测研究[D].东北林业大学.2006
47.苗静,杨勇,谷欣超等.不变矩及其在基于形状特征图像检索中的应用[J].长春理工大学学报(自然科学版),2009,32(1):126-128
48.莫玉龙,张郑擎,彭明生.具有抗噪能力的图像阈值分割法[J].上海大学学报.2001,7(6):487-491
49.莫银辉.中纤板浸渍纸装饰表面缺陷浅析[J].林产工业.2002,29(1):41-42
50.聂汉军,沈永增.基于小波变换和模糊中值滤波的图像边缘检测[J].计算机工程与应用.2002,13:91-92
51.庞江伟,基于计算机视觉的脐橙表面常见缺陷种类识别的研究[J].浙江大学.2006
52.彭兴璇.一种基于HSV颜色空间的图像检索方法[J].科技信息.2008,27:381-383
53.乔志杰,蒋加伏.基于小波分解的纹理图像检索[J].计算机与数字工程.2006,34(6):75-78
54.邱少强,王惠敏,闫玉华.基于神经网络与遗传算法的纹理图像分割[J].武汉理工大学学报.2004,26(3):86-87
55.乔双,宋建中,胡硕.基于改进遗传算法的图像分割方法[J].微型机与应用.2004(3):50-51
56.冉丹.基于局部线形嵌入算法的雷达目标识别[J].四川文理学院学报(自然科学).2008,18(2):69-71
57.沈堃.模式识别-人工神经网络方法研究及其在材料领域的应用[D].武汉理工大学.2005
58.盛国芳,焦李成.基于遗传算法的最佳熵阈值的图像分割[J].计算机工程与应用.2003,12:103-105
59.田晓东,刘忠.基于自适应迭代阈值的声呐图像增强算法[J].舰船电子工程.2006,26(2):154-157
60.田美丽,李占杰,阎兵.基于RBF神经网络的难加工材料高速铣削粗糙度预报研究[J].工具技术.2008,42(3):35-37
61.陶保壮,李炜,张义超.RBF与BP网络实时性分析[J].皖西学院学报.2008,24(5):31-33
62.谭璐.高位数据的降维理论及应用[D].长沙:国防科技大学,2005
63.谭璐,吴翊,易东云.稳健局部线性嵌入方法[J].国防科技大学学报.2004,26(6):91-95
64.文贵华,包丽,丁月华.局部线性嵌入算法中参数的选取[J].计算机应用研究.2007(2):60-62
65.王立海,杨学春,徐凯宏.木材无损检测技术的研究现状与进展[J].森林工程.2001,17(6):1-4
66.王凤梅,基于机器视觉的小件陶瓷管检测系统的研究[D].西安科技大学.2004
67.王业琴,王克奇等.计算机视觉木材表面色差检测的研究[J].林业科技,2005,30(2):36-38
68.王业琴,赵志国.基于直方图和颜色矩方法的木材表面颜色特征的表达[J].林业科技,2006,31(5):56-58
69.王克奇,石岭,白雪冰等.基于吉布斯-马尔可夫随机场的板材表面纹理分析[J].东北林业大学学报.2006,34(4):8-9
70.王克奇,石岭,白雪冰等.基于高斯-马尔可夫随机场的板材表面纹理分析[J].林业科技.2005,30(6):46-48
71.王波,姚宏宇,李弼程.一种有效的基于灰度共生矩阵的图像检索方法[J].武汉大学学报:信息科学版,2006,3l(9):761-764
72.王晗,白雪冰,王辉.基于空间灰度共生矩阵木材纹理分类识别的研究[J].森林工程.2007,23(1):32-36
73.王婷婷.基于神经网络的多角度实物识别系统[J].微计算机信息.2009,25(2-1):240-241
74.王鹏.基于机器视觉的钢球表面缺陷检测系统[D].哈尔滨理工大学,2005
75.吴玲艳,沈庭芝,方子文等.基于直方图熵和遗传算法的图像分割[J].兵工学报.1999,20(3):255-258
76.汪黎明.织物褶皱纹理灰度共生矩阵分析[J].青岛大学学报,2003,18(4):5-7
77.汪莹,李炳富.神经网络的应用研究探讨[J].电脑知识与技术,2008,3(4):635-636
78.肖建华.智能模式识别方法[M].华南理工大学出版社,2006
79.徐海荣,田联房,陈萍等.改进的区域生长算法在医学图像分割中的应用[J].生物医学工程研究.2005,24(3):187-190
80.徐琨,李燕.基于分块颜色矩和纹理特征的图像检索方法[J].西安石油大学学报(自然科学版),2005,20(2):77-79
81.谢永华,基于分形理论木材表面缺陷识别的研究[D].东北林业大学.2006
82.谢维信,高新波,裴继红.模糊聚类理论的应用与发展[J].中国体视学与图像分析,1996,4(2):113-119
83.熊明,王汝言,唐琳.基于局部线性嵌入与主成分分析的人脸识别方法[J].重庆邮电大学学报(自然科学版).2009,21(1):92-94
84.尹平,王润生.基于边缘信息的分开合并图像分割方法[J].中国图像图形学报,1998,3(6):450-454
85.尹力.木材表面缺陷模式特征提取的图像处理方法研究[D].哈尔滨:东北林业大学,2001
86.叶银兰.基于颜色和形状的图像检索[J].太原理工大学学报,2008,39(2):127-131
87.余成波.数字图像处理及MATLAB实现[M].重庆大学出版社,2003
88.易乔木,程金树等.一种基于机器视觉的玻璃质量在线检测系统[J].武汉理工大学学报.2007,29(5):23-26
89.苑丽红,付丽,杨勇等.灰度共生矩阵提取纹理特征的实验结果分析[J].计算机应用,2009,29(4):1018-1021
90.杨云东,冯桂.一种基于遗传算法的图像区域合并分裂方法[J].电脑开发与应用.2003,16(3):28-32
91.杨远.基于神经网络的目标图像识别方法研究[D].南京航空航天大学.2004
92.杨春梅,胡万义,白帆.木材缺陷检测理论及方法的发展[J].林业机械与木工设备,2004,32(3):8-10
93.杨海峰,侯朝桢.基于二维灰度直方图的蚁群图像分割[J].激光与红外.2005,8:614-617
94.杨珣.基于神经网络的人脸检测研究[D].青岛大学,2006
95.杨晓云,梁鑫,岑敏仪等.基于主成分分析法的DEM粗差定位[D].测绘科学.2009,34(2):128-129
96.杨敏.模糊控制与神经计算方法在板形控制中的应用[D].北京工业大学.2005
97.杨静.基于神经网络的指纹识别[D].北京工业大学,2003
98.朱志刚,林学訚等,数字图像处理[M].北京:电子工业出版社,2005
99.朱贵锋.机器视觉在线检测图像处理技术研究[D].合肥工业大学,2006
100.朱明星,张德龙.RBF网络基函数中心选取算法的研究[J].安徽大学学报(自然科学版).2000,24(1):72-78
101.曾文华.基于灰度共生法和小波变换的遥感影像纹理信息提取[D].长春:东北师范大学,2006.
102.曾妍婷,朱宏擎.基于改进遗传算法的图像分割识别方法[J].武汉理工大学学报(交通科学与工程版).2004,28(3):435-437
103.周开利.神经网络模型及其Matlab仿真程序设计[M].北京:清华大学出版社,2005
104.周学成,罗锡文.采用区域生长法分割根系CT图像的改进算法[J].农业机械学报,2006,37(12):122-125
105.周泽魁.图像检测技术在皮革缺陷检测中的应用研究[D].浙江大学.2004
106.周莉,郑建彬,颜琬.基于小波多尺度和熵在图像字符特征提取方法的改进[J].电子技术应用,2004,7:6-8
107.周德芳,张健.二维最大熵阈值分割的一种快速递推算法及应用[J].现代电子技术,2003(24):85-87
108.郑守志,叶世伟.局部线性嵌入算法改进研究[J],计算机仿真,2007,24(04):78-81
109.郑杰,一种改进的LLE方法[J],湖南理工学院学报(自然科学版),2007,20(3):30-32
110.郑健峰.带钢表面缺陷检测方法研究[D].西安建筑科技大学,2006
111.张尧庭,方开泰.多元统计分析引论[M].北京:科学出版社,1982
112.张学工.关于统计学习理论与支持向量机[J].自动化学报,2000:26(1):32-42
113.张岩,尹力.主成分分析在舰船辐射噪声分类识别中的应用[J].应用声学.2009,28(1):20-26
114.张昱.基于机器视觉的TFT-LCD屏mura缺陷检测技术研究[D].哈尔滨工业大学.2006
115.张铁中,魏剑涛.蔬菜嫁接机器人视觉系统的研究(Ⅰ)-用图像形态学方法检测瓠瓜苗生长点[J].中国农业大学学报,1999,Vol.4,No.4:45-47
116.张建东,苏鸿根.基于内容的图像检索关键技术研究[J].计算机工程,2004,30(14):119-121
117.张晓波,基于图像处理技术的表面缺陷自动检测系统的研究[D].天津大学.2004
118.张健,杨丽丽,鲍际平.基于蚁群算法的强化木地板表面缺陷图像处理技术研究[J].仪器仪表学报.2008,29(4):109-112
119.张健,鲍际平等.基于遗传算法的强化木地板表面缺陷的图像分割[J].林业机械与木工设备,2008,36(7):27-28
120.赵春晖,张乾等.基于数学形态滤波算子的医学图像边缘检测[J].信息技术,2002,11:49-50
121.赵清林,郭艳兵,梅强等.确定RBF神经网络中心点的方法综述[J].广东:自动化与信息工程.2002,2:13-15
122.章毓晋.图像分割[M].北京:科学出版社,2001
123.B.Charles and L.Bede.Multiple resolution segmentation of textured images.IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(2):99-113
124.BARALDIA,PARMIGGIAN F.An investigation of texture characteristics associated with gray level co-occurrence matrix statistical parameters[J].IEEE Trans.On Geo-science and Remote sensing,1995,33(2):293-303
125.Bharati M H,LIU J J,MAC G,Image texture analysis:methods and comparisons[J].Chemometrics & Intelligent Laboratory Systems,2004,72(1):57-65
126.Cheng H D,Chen Y H,Jiang X H.Threshold usingtwo2dimensional histogram and fuzzy entropy principle[J].IEEE Transactions on Image Processing,2000,9(4):732-735
127.Cyra G,Tanaka C,Nakao T.On-line Control of Router Feed Speed U sing Acoustic Emission[J].Forest Prod.J.,1996,46(11/12):27-32
128.Costes J,Larricq P.Surface Characterization with 3-Dimensional Roughness Parameter[C].Proceedings of the 15th International Wood Machining Seminar.2001:473-476
129.D.Earl Kline,Y.Jason Hou,Richard W.Conners.LUMBER SCANNING SYSTEM FOR SURFACE DEFECT DETECTION[D].THE AMERICAN SOCIETY OF AGRICULTURAL ENGINEERS.1992
130.DAUGMAN JG.Uncertainty relation for resolution in space,spatial frequency,and orientation optimized by two-dimensional visual cortical filters[J].Journal of the Optical Society of America A,1985,2(7):1160-1169
131.Fu K S,Mui J K.A survey on image segmentation.Pttern Recognition[J],1981,13:3-16
132.Faust T D.Real Time Measurement of Veneer Surface Rough-ness by Image Analysis[J].Forest Prod.J.1987,37(6):34-40
133.Forrer J B,Funck J W.Dielectric Properties of Defects on Wood Surface[J].Holzals Rohund Werkstoff.1998,56(1):25-29
134.H.Elbehiery,A.Hefnawy,M.Elewa.Surface Defects Detection for Ceramic Tiles Using Image Processing and Morphological Techniques. TRANSACTIONS ON ENGINEERING,COMPUTING AND TECHNOLOGY. 2005,5:158-162
135.HanKJ, TewfikAH. Hybrid wavelet transform filter for image recovery [A]. In: Proceedings of the International Conference on Image Processing.Chicago USA, 1998: 540-544
136.HARALICK RM, SHANMUGAM K, DINSTEIN IH. Texture features for image classification[J].IEEE Transactions on Systems,Man and Cybernetics, 1973, 3(6): 610-621
137. Horowitz S L, Pavlidis T. Picture segmentation by a tree traversal algorithm. J. ACM, 1976,23:368-388
138.I.E.Abdou and W.K.Pratt.Quantitative design and evaluation of enhancement/thresholding edge detectors.Proc. IEEE, 1979,67:753-763
139.I.J. Jolliffe. Principal Component Analysis[M]. Springer, New York, 2002
140.J B Tenenbaum, V de Silva, J C Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction [J]. Science,2000, 290(5500): 2319-2323
141.JIANCHANGM, ANILK J. Texture classification and segmentation using multiresolution simultaneous autoregressive models[J]. Pattern Recognition, 1992, 25(2): 173-188
142.K. Jain and B. Chandrasekaran, "Dimensionality and Sample Size Considerations in Pattern Recognition Practice," Handbook of Statistics,(Vol. 2, pp. 835-855), North Holland Publishing Company, 1982.
143.K.Strasters and J.J.Gerbrands. Three-dimensional image segmentation using a split, merge and group approach. Pattern Recognition Letters,1991,12(3):307-325
144.L. Kanal, and B. Chandrasekaran, "On Dimensionality and Sample Size in Statistical Pattern Recognition," Pat. Rec, 1971 (3) :225-234
145.LawrenceK. Saul, SamT. Roweis, Think globally, fit locally unsupervised learning of low dimensional manifolds[ J].Journal of Machine Learning Research 2003(4): 179-155
146.Law T, Itoh H, SekiH. Image filtering, edge detection, and edge tracing using fuzzy reasoning [ J].IEEE T cans. Pattern Analysis and Machine Intelligence, 1996, 18(5):220-227
147.Lee C K, Wong S P. A mathematical morphological approach for segmenting heavily noise-corrupted images. Pattern Recognition. 1996,29(8):1347—1357
148. Liang Z, Macfall J R, Harrington D P. Parameter Estimation and Tissue Segmentation from Multispectral MR Images [J]. IEEE Trans on Medical Imaging, 1994, 13(3): 441—449
149. L emaster R, Beall C. The Use Optical Profilometer to Measure Surface Roughness in Mediu-m Density Fiberboard[J]. Forest Prod J., 1996,46(11/12):73-78
150.M.D.Levine and A. Nazif.Dynamic measurement of compuer generated image segmentations.IEEE Transactions on PAMI-7,1985,155-164
151.MilanSonka,VaclavHlavac.图像处理、分析与机器视觉[M].2版.北京:人民邮电出版社,2002
152.P.k.Sahoo,et al.A survey of thresholding techniques, CVGIP41,1988,233-260
153.Pal N R, Pal SK. A review on image segmentation techniques. Pattern Recognition, 1993, 26:1277-1294
154. Pham D L, Xu C Y. Prince J L. A Survey of Current Methods in Medical Image Segmentation[R]. Technical Report JHU/ECE99-01, Johns HoPkins University, 1998
155.S T Roweis, LK Sau.l Nonlinear Dimensionality Reduction by Locally Linear Embedding[J].Science, 2000,290(5500): 2323-2327
156. SONG J H. Maximum entropy thresholding image segmentation based on genetic algorithm [J].Electronic Engineer, 2005, 2(9):34-36
157.TAMURA H, MORI S, YAMAWAKI T. Texture features corresponding to visual perception[J].IEEE Transactions on Systems, Man, and Cybernetics, 2003, 8(6): 460-473
158. T. Young, "Further Considerations of Sample Size and Feature Size," IEEE Trans.IT-24(6):773-775,1978
159. Vapnik V N. The nature of statistical learnig theory[M].New-York: Springer-Verlag, 1995.
160. W.A.Yasnoff,et al. Error measures for scene segmentation. Pattern Recognition, 1997,9:217-231
161.W. Meisel, Computer-Oriented Approacher to Pattern Recognition[M], Academic Press, New York, 1972
162.Whatmough R J. Automatic threshold selection from a histogram using the exponential hull.CVGIP-GMIP,1991,53(6):592-600
163.WANG X P, CAO L M. Genetic algorithms-theory, application and software implementation [M].Xi'an: Xi'an jiaotong university press, 2002
164. Y.J.Zhang and J.J.Gerbrands. Transition region determination based thresholding, Pattern Recognition Letters, 1991,12(1):13-23
165. Y.J.Zhang and J.J.Gerbrands. Objective and quantitative segmentation evaluation and comparison. Signal Processing, 1994,39(1):43-54
166. zhang Y J, Gerbrands J J, Back E. Threshold three-dimensional image. SPIE, 1990,1360:1258-1269
2.白雪冰,王克奇,王辉.基于灰度共生矩阵的木材纹理分类方法的研究[J].哈尔滨工业大学学报,2005,37(12):1667-1670
3.边肇祺,张学工等.模式识别(第二版)[M].清华大学出版社,2000
4.陈才扣,宋枫溪,刘永俊.基于散度差准则的隐空间特征抽取方法[J].计算机科学.2006,33(12):174-176
5.陈丹.基于PCA变换的多传感器图像融合质量评价[J].科技广场.2009(1):133-135
6.陈波.基于数学形态学的图像特征提取[J].科技情报开发与经济.2007,17(17):161-162
7.陈惠明,冯素琴,邸未冬.基于数据场的PCA方法在人脸识别中的应用研究[J].福建电脑.2009(3):81-82
8.常青,王立,邢超.基于遗传算法的图像阈值选取[J].计算机工程与应用.2002,22:35-37
9.崔扬.图像检测技术在皮革缺陷检测中的应用研究[D].浙江大学.2004
10.曹顺茂,叶世伟.一种改进的局部线性嵌入算法[J].计算机仿真.2007,24(5):87-90
11.丁金明.金属镀层工件表面缺陷自动检测系统的研究[D].天津大学.2004
12.杜奇,向健勇,袁胜春.一种改进的最大类间方差法[J].红外技术.2003,25(5):33-36
13.关胜晓.机器视觉及其应用发展[J].自动化博览.2005,03:88-92
14.关子明,常文兵.基于主成分分析-RBF神经网络模型的备件预测研究[J].物流科技.2009(4):122-126
15.高宁,张建中.MATLAB在RBF神经网络模型中的应用[J].农业网络信息.2009,(2):110-116
16.高淑兰,刘英臣.强化复合地板的生产工艺及应用前景[J].林业科技.2001,26(2):46-47
17.耿冠宏,孙伟,罗培.神经网络模式识别[J].软件导刊,2008,7(10):81-83
18.高隽,人工神经网络原理及仿真实例[M].机械工业出版社,2003
19.郭德军,宋蛰存.基于灰度共生矩阵的纹理图像分类研究[J].林业机械与木工设备,2005,33(7):21-23
20.何友金,李楠.舰船红外图像边缘检测方法对比研究[J].计算机仿真.2006,23(4):201-203
21.洪龙海,韩丹红.影响强化地板质量的几种因素及控制措施,林业机械与木工设备.2004,32(4):38-39
22.黄国宏,邵惠鹤.一种新的基于构造型RBF神经元网络分类算法[J].控制与决策.2005,20(12):1411-1414
23.黄艺,杜宇人.基于边缘信息的图像分割技术研究[J].现代电子技术.2005,28(5):116-117
24.韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术.2002,24(6):91-94
25.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
26.金聪,彭嘉雄.利用遗传算法实现数字图像分割[J].小型微型计算机系统.2002,23(7):875-877
27.贾花萍.基本神经网络的特征选择与提取方法研究[J].办公自动化.2008,(135):33-35
28.景晓军,蔡安妮,孙景鳌.一种基于二维最大类间方差的图像分割算法[J].通信学报,2001,22(4):71-76
29.孔明,孙希平.一种改进的基于类间方差的阈值分割法[J].华中科技大学学 报,2004,32(7):46-47
30.阚江明.基于计算机视觉的活立木三维重建方法[D].北京林业大学,2008
31.芦蓉,沈毅.一种改进的二维直方图的图像阈值分割方法[J].系统工程与电子技术,2004,26(10):1487-1490
32.陆振波,章新华,康春玉.基于支持向量机的水中目标识别[J].信息与控制,2003,z1:739-742
33.李小泉.自动检测系统关键技术研究[D].武汉理工大学.2006
34.李立源,龚坚,陈维甫.基于二维灰度直方图最佳一维投影的图像分割方法[J].电子学报,1996,22(3):315-323
35.李红梅,宁功德.影响强化木地板质量因素的分析[J].林业机械与木工设备.2004,32(06):33-34
36.李国友,姚磊等.基于优化的RBF神经网络模式识别新方法[J].系统仿真学报.2006,18(1):181-184
37.李辉,吕英华.在遗传算法基础上的自动阈值选取方法[J],东北师大学报,2006,38(2):36-38
38.刘京南,陈从颜,余玲玲等.一种快速二维熵阈值分割算法[J].计算机应用研究,2002(1):67-70
39.刘红冰,基于神经网络的冷轧带钢表面缺陷检测与分类技术研究[D].电子科技大学,2005
40.刘婧然,马英杰,杨武建.预测水稻螟虫发生趋势的径向基函数人工神经网络模型及其与BP网络的比较[J].内蒙古农业科技.2009(1):26-28
41.刘循,游志胜.多尺度形态学图像边缘检测方法[J].光电工程,2003,1(3):56-58
42.刘维群,李为华.基于自组织选取中心的广义RBF神经网络学习算法{J}.信阳师范学院学报:自然科学版.2007,20(4):515-517
43.廖义奎,王志文.一种新的彩色图像特征提取方法及其应用[J].广西民族学院学报(自然科学版),2003,9(3):48-52
44.梁瑞鑫,孙长银,穆朝旭.基于二维投影特征提取的人脸识别算法[J].计算机工程与应用.2009,45(10):191-194
45.柳稼航等,一种基于优先搜索方向的边界跟踪算法[J].遥感技术与应用.2004,19(3):209-213
46.牟洪波,基于人工神经网络的木材缺陷检测研究[D].东北林业大学.2006
47.苗静,杨勇,谷欣超等.不变矩及其在基于形状特征图像检索中的应用[J].长春理工大学学报(自然科学版),2009,32(1):126-128
48.莫玉龙,张郑擎,彭明生.具有抗噪能力的图像阈值分割法[J].上海大学学报.2001,7(6):487-491
49.莫银辉.中纤板浸渍纸装饰表面缺陷浅析[J].林产工业.2002,29(1):41-42
50.聂汉军,沈永增.基于小波变换和模糊中值滤波的图像边缘检测[J].计算机工程与应用.2002,13:91-92
51.庞江伟,基于计算机视觉的脐橙表面常见缺陷种类识别的研究[J].浙江大学.2006
52.彭兴璇.一种基于HSV颜色空间的图像检索方法[J].科技信息.2008,27:381-383
53.乔志杰,蒋加伏.基于小波分解的纹理图像检索[J].计算机与数字工程.2006,34(6):75-78
54.邱少强,王惠敏,闫玉华.基于神经网络与遗传算法的纹理图像分割[J].武汉理工大学学报.2004,26(3):86-87
55.乔双,宋建中,胡硕.基于改进遗传算法的图像分割方法[J].微型机与应用.2004(3):50-51
56.冉丹.基于局部线形嵌入算法的雷达目标识别[J].四川文理学院学报(自然科学).2008,18(2):69-71
57.沈堃.模式识别-人工神经网络方法研究及其在材料领域的应用[D].武汉理工大学.2005
58.盛国芳,焦李成.基于遗传算法的最佳熵阈值的图像分割[J].计算机工程与应用.2003,12:103-105
59.田晓东,刘忠.基于自适应迭代阈值的声呐图像增强算法[J].舰船电子工程.2006,26(2):154-157
60.田美丽,李占杰,阎兵.基于RBF神经网络的难加工材料高速铣削粗糙度预报研究[J].工具技术.2008,42(3):35-37
61.陶保壮,李炜,张义超.RBF与BP网络实时性分析[J].皖西学院学报.2008,24(5):31-33
62.谭璐.高位数据的降维理论及应用[D].长沙:国防科技大学,2005
63.谭璐,吴翊,易东云.稳健局部线性嵌入方法[J].国防科技大学学报.2004,26(6):91-95
64.文贵华,包丽,丁月华.局部线性嵌入算法中参数的选取[J].计算机应用研究.2007(2):60-62
65.王立海,杨学春,徐凯宏.木材无损检测技术的研究现状与进展[J].森林工程.2001,17(6):1-4
66.王凤梅,基于机器视觉的小件陶瓷管检测系统的研究[D].西安科技大学.2004
67.王业琴,王克奇等.计算机视觉木材表面色差检测的研究[J].林业科技,2005,30(2):36-38
68.王业琴,赵志国.基于直方图和颜色矩方法的木材表面颜色特征的表达[J].林业科技,2006,31(5):56-58
69.王克奇,石岭,白雪冰等.基于吉布斯-马尔可夫随机场的板材表面纹理分析[J].东北林业大学学报.2006,34(4):8-9
70.王克奇,石岭,白雪冰等.基于高斯-马尔可夫随机场的板材表面纹理分析[J].林业科技.2005,30(6):46-48
71.王波,姚宏宇,李弼程.一种有效的基于灰度共生矩阵的图像检索方法[J].武汉大学学报:信息科学版,2006,3l(9):761-764
72.王晗,白雪冰,王辉.基于空间灰度共生矩阵木材纹理分类识别的研究[J].森林工程.2007,23(1):32-36
73.王婷婷.基于神经网络的多角度实物识别系统[J].微计算机信息.2009,25(2-1):240-241
74.王鹏.基于机器视觉的钢球表面缺陷检测系统[D].哈尔滨理工大学,2005
75.吴玲艳,沈庭芝,方子文等.基于直方图熵和遗传算法的图像分割[J].兵工学报.1999,20(3):255-258
76.汪黎明.织物褶皱纹理灰度共生矩阵分析[J].青岛大学学报,2003,18(4):5-7
77.汪莹,李炳富.神经网络的应用研究探讨[J].电脑知识与技术,2008,3(4):635-636
78.肖建华.智能模式识别方法[M].华南理工大学出版社,2006
79.徐海荣,田联房,陈萍等.改进的区域生长算法在医学图像分割中的应用[J].生物医学工程研究.2005,24(3):187-190
80.徐琨,李燕.基于分块颜色矩和纹理特征的图像检索方法[J].西安石油大学学报(自然科学版),2005,20(2):77-79
81.谢永华,基于分形理论木材表面缺陷识别的研究[D].东北林业大学.2006
82.谢维信,高新波,裴继红.模糊聚类理论的应用与发展[J].中国体视学与图像分析,1996,4(2):113-119
83.熊明,王汝言,唐琳.基于局部线性嵌入与主成分分析的人脸识别方法[J].重庆邮电大学学报(自然科学版).2009,21(1):92-94
84.尹平,王润生.基于边缘信息的分开合并图像分割方法[J].中国图像图形学报,1998,3(6):450-454
85.尹力.木材表面缺陷模式特征提取的图像处理方法研究[D].哈尔滨:东北林业大学,2001
86.叶银兰.基于颜色和形状的图像检索[J].太原理工大学学报,2008,39(2):127-131
87.余成波.数字图像处理及MATLAB实现[M].重庆大学出版社,2003
88.易乔木,程金树等.一种基于机器视觉的玻璃质量在线检测系统[J].武汉理工大学学报.2007,29(5):23-26
89.苑丽红,付丽,杨勇等.灰度共生矩阵提取纹理特征的实验结果分析[J].计算机应用,2009,29(4):1018-1021
90.杨云东,冯桂.一种基于遗传算法的图像区域合并分裂方法[J].电脑开发与应用.2003,16(3):28-32
91.杨远.基于神经网络的目标图像识别方法研究[D].南京航空航天大学.2004
92.杨春梅,胡万义,白帆.木材缺陷检测理论及方法的发展[J].林业机械与木工设备,2004,32(3):8-10
93.杨海峰,侯朝桢.基于二维灰度直方图的蚁群图像分割[J].激光与红外.2005,8:614-617
94.杨珣.基于神经网络的人脸检测研究[D].青岛大学,2006
95.杨晓云,梁鑫,岑敏仪等.基于主成分分析法的DEM粗差定位[D].测绘科学.2009,34(2):128-129
96.杨敏.模糊控制与神经计算方法在板形控制中的应用[D].北京工业大学.2005
97.杨静.基于神经网络的指纹识别[D].北京工业大学,2003
98.朱志刚,林学訚等,数字图像处理[M].北京:电子工业出版社,2005
99.朱贵锋.机器视觉在线检测图像处理技术研究[D].合肥工业大学,2006
100.朱明星,张德龙.RBF网络基函数中心选取算法的研究[J].安徽大学学报(自然科学版).2000,24(1):72-78
101.曾文华.基于灰度共生法和小波变换的遥感影像纹理信息提取[D].长春:东北师范大学,2006.
102.曾妍婷,朱宏擎.基于改进遗传算法的图像分割识别方法[J].武汉理工大学学报(交通科学与工程版).2004,28(3):435-437
103.周开利.神经网络模型及其Matlab仿真程序设计[M].北京:清华大学出版社,2005
104.周学成,罗锡文.采用区域生长法分割根系CT图像的改进算法[J].农业机械学报,2006,37(12):122-125
105.周泽魁.图像检测技术在皮革缺陷检测中的应用研究[D].浙江大学.2004
106.周莉,郑建彬,颜琬.基于小波多尺度和熵在图像字符特征提取方法的改进[J].电子技术应用,2004,7:6-8
107.周德芳,张健.二维最大熵阈值分割的一种快速递推算法及应用[J].现代电子技术,2003(24):85-87
108.郑守志,叶世伟.局部线性嵌入算法改进研究[J],计算机仿真,2007,24(04):78-81
109.郑杰,一种改进的LLE方法[J],湖南理工学院学报(自然科学版),2007,20(3):30-32
110.郑健峰.带钢表面缺陷检测方法研究[D].西安建筑科技大学,2006
111.张尧庭,方开泰.多元统计分析引论[M].北京:科学出版社,1982
112.张学工.关于统计学习理论与支持向量机[J].自动化学报,2000:26(1):32-42
113.张岩,尹力.主成分分析在舰船辐射噪声分类识别中的应用[J].应用声学.2009,28(1):20-26
114.张昱.基于机器视觉的TFT-LCD屏mura缺陷检测技术研究[D].哈尔滨工业大学.2006
115.张铁中,魏剑涛.蔬菜嫁接机器人视觉系统的研究(Ⅰ)-用图像形态学方法检测瓠瓜苗生长点[J].中国农业大学学报,1999,Vol.4,No.4:45-47
116.张建东,苏鸿根.基于内容的图像检索关键技术研究[J].计算机工程,2004,30(14):119-121
117.张晓波,基于图像处理技术的表面缺陷自动检测系统的研究[D].天津大学.2004
118.张健,杨丽丽,鲍际平.基于蚁群算法的强化木地板表面缺陷图像处理技术研究[J].仪器仪表学报.2008,29(4):109-112
119.张健,鲍际平等.基于遗传算法的强化木地板表面缺陷的图像分割[J].林业机械与木工设备,2008,36(7):27-28
120.赵春晖,张乾等.基于数学形态滤波算子的医学图像边缘检测[J].信息技术,2002,11:49-50
121.赵清林,郭艳兵,梅强等.确定RBF神经网络中心点的方法综述[J].广东:自动化与信息工程.2002,2:13-15
122.章毓晋.图像分割[M].北京:科学出版社,2001
123.B.Charles and L.Bede.Multiple resolution segmentation of textured images.IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(2):99-113
124.BARALDIA,PARMIGGIAN F.An investigation of texture characteristics associated with gray level co-occurrence matrix statistical parameters[J].IEEE Trans.On Geo-science and Remote sensing,1995,33(2):293-303
125.Bharati M H,LIU J J,MAC G,Image texture analysis:methods and comparisons[J].Chemometrics & Intelligent Laboratory Systems,2004,72(1):57-65
126.Cheng H D,Chen Y H,Jiang X H.Threshold usingtwo2dimensional histogram and fuzzy entropy principle[J].IEEE Transactions on Image Processing,2000,9(4):732-735
127.Cyra G,Tanaka C,Nakao T.On-line Control of Router Feed Speed U sing Acoustic Emission[J].Forest Prod.J.,1996,46(11/12):27-32
128.Costes J,Larricq P.Surface Characterization with 3-Dimensional Roughness Parameter[C].Proceedings of the 15th International Wood Machining Seminar.2001:473-476
129.D.Earl Kline,Y.Jason Hou,Richard W.Conners.LUMBER SCANNING SYSTEM FOR SURFACE DEFECT DETECTION[D].THE AMERICAN SOCIETY OF AGRICULTURAL ENGINEERS.1992
130.DAUGMAN JG.Uncertainty relation for resolution in space,spatial frequency,and orientation optimized by two-dimensional visual cortical filters[J].Journal of the Optical Society of America A,1985,2(7):1160-1169
131.Fu K S,Mui J K.A survey on image segmentation.Pttern Recognition[J],1981,13:3-16
132.Faust T D.Real Time Measurement of Veneer Surface Rough-ness by Image Analysis[J].Forest Prod.J.1987,37(6):34-40
133.Forrer J B,Funck J W.Dielectric Properties of Defects on Wood Surface[J].Holzals Rohund Werkstoff.1998,56(1):25-29
134.H.Elbehiery,A.Hefnawy,M.Elewa.Surface Defects Detection for Ceramic Tiles Using Image Processing and Morphological Techniques. TRANSACTIONS ON ENGINEERING,COMPUTING AND TECHNOLOGY. 2005,5:158-162
135.HanKJ, TewfikAH. Hybrid wavelet transform filter for image recovery [A]. In: Proceedings of the International Conference on Image Processing.Chicago USA, 1998: 540-544
136.HARALICK RM, SHANMUGAM K, DINSTEIN IH. Texture features for image classification[J].IEEE Transactions on Systems,Man and Cybernetics, 1973, 3(6): 610-621
137. Horowitz S L, Pavlidis T. Picture segmentation by a tree traversal algorithm. J. ACM, 1976,23:368-388
138.I.E.Abdou and W.K.Pratt.Quantitative design and evaluation of enhancement/thresholding edge detectors.Proc. IEEE, 1979,67:753-763
139.I.J. Jolliffe. Principal Component Analysis[M]. Springer, New York, 2002
140.J B Tenenbaum, V de Silva, J C Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction [J]. Science,2000, 290(5500): 2319-2323
141.JIANCHANGM, ANILK J. Texture classification and segmentation using multiresolution simultaneous autoregressive models[J]. Pattern Recognition, 1992, 25(2): 173-188
142.K. Jain and B. Chandrasekaran, "Dimensionality and Sample Size Considerations in Pattern Recognition Practice," Handbook of Statistics,(Vol. 2, pp. 835-855), North Holland Publishing Company, 1982.
143.K.Strasters and J.J.Gerbrands. Three-dimensional image segmentation using a split, merge and group approach. Pattern Recognition Letters,1991,12(3):307-325
144.L. Kanal, and B. Chandrasekaran, "On Dimensionality and Sample Size in Statistical Pattern Recognition," Pat. Rec, 1971 (3) :225-234
145.LawrenceK. Saul, SamT. Roweis, Think globally, fit locally unsupervised learning of low dimensional manifolds[ J].Journal of Machine Learning Research 2003(4): 179-155
146.Law T, Itoh H, SekiH. Image filtering, edge detection, and edge tracing using fuzzy reasoning [ J].IEEE T cans. Pattern Analysis and Machine Intelligence, 1996, 18(5):220-227
147.Lee C K, Wong S P. A mathematical morphological approach for segmenting heavily noise-corrupted images. Pattern Recognition. 1996,29(8):1347—1357
148. Liang Z, Macfall J R, Harrington D P. Parameter Estimation and Tissue Segmentation from Multispectral MR Images [J]. IEEE Trans on Medical Imaging, 1994, 13(3): 441—449
149. L emaster R, Beall C. The Use Optical Profilometer to Measure Surface Roughness in Mediu-m Density Fiberboard[J]. Forest Prod J., 1996,46(11/12):73-78
150.M.D.Levine and A. Nazif.Dynamic measurement of compuer generated image segmentations.IEEE Transactions on PAMI-7,1985,155-164
151.MilanSonka,VaclavHlavac.图像处理、分析与机器视觉[M].2版.北京:人民邮电出版社,2002
152.P.k.Sahoo,et al.A survey of thresholding techniques, CVGIP41,1988,233-260
153.Pal N R, Pal SK. A review on image segmentation techniques. Pattern Recognition, 1993, 26:1277-1294
154. Pham D L, Xu C Y. Prince J L. A Survey of Current Methods in Medical Image Segmentation[R]. Technical Report JHU/ECE99-01, Johns HoPkins University, 1998
155.S T Roweis, LK Sau.l Nonlinear Dimensionality Reduction by Locally Linear Embedding[J].Science, 2000,290(5500): 2323-2327
156. SONG J H. Maximum entropy thresholding image segmentation based on genetic algorithm [J].Electronic Engineer, 2005, 2(9):34-36
157.TAMURA H, MORI S, YAMAWAKI T. Texture features corresponding to visual perception[J].IEEE Transactions on Systems, Man, and Cybernetics, 2003, 8(6): 460-473
158. T. Young, "Further Considerations of Sample Size and Feature Size," IEEE Trans.IT-24(6):773-775,1978
159. Vapnik V N. The nature of statistical learnig theory[M].New-York: Springer-Verlag, 1995.
160. W.A.Yasnoff,et al. Error measures for scene segmentation. Pattern Recognition, 1997,9:217-231
161.W. Meisel, Computer-Oriented Approacher to Pattern Recognition[M], Academic Press, New York, 1972
162.Whatmough R J. Automatic threshold selection from a histogram using the exponential hull.CVGIP-GMIP,1991,53(6):592-600
163.WANG X P, CAO L M. Genetic algorithms-theory, application and software implementation [M].Xi'an: Xi'an jiaotong university press, 2002
164. Y.J.Zhang and J.J.Gerbrands. Transition region determination based thresholding, Pattern Recognition Letters, 1991,12(1):13-23
165. Y.J.Zhang and J.J.Gerbrands. Objective and quantitative segmentation evaluation and comparison. Signal Processing, 1994,39(1):43-54
166. zhang Y J, Gerbrands J J, Back E. Threshold three-dimensional image. SPIE, 1990,1360:1258-1269
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |