基于计算机视觉的花生仁外观品质无损检测方法的研究
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摘要
外贸出口对花生仁的破损粒、霉变粒、大小、外形等外观品质有着明确的规定,但目前市场上除了光电色选机可以对霉变粒进行分拣外,其他外观品质指标主要依靠人工进行,难以满足不同的市场需求。计算机视觉具有无损、快速、可一次完成多个品质指标的检测、有利于设计制造自动分级流水线等特点,在农产品品质自动识别上有非常好的应用前景。利用计算机视觉技术进行花生仁外观品质检测研究,实现花生仁外观品质的自动、无损检测,对提高我国花生的市场竞争力具有重要意义。在这样的背景下,本文利用计算机视觉技术、图像处理技术和模式识别技术研究了花生仁外观品质检测方法。主要研究内容如下:
1.结合花生仁图像特征提取的实际要求,分析了几种常用颜色模型的特点,对噪声滤除、图像增强、图像分割、特征提取等图像基础处理算法进行了分析和研究,确定了适用于花生仁外观品质检测的图像底层处理算法。
2.为了实现对霉变花生仁的检测,研究了花生仁霉变过程中,颜色特征参数、纹理特征参数的变化规律,提取颜色特征H、I、S及纹理特征RW、GW、BW作为MATLAB所创建的神经网络的输入,利用BP神经网络模型实现了对正常、不新鲜、霉变三种情况花生仁的判别,正确分类率为96.67%、90%、93.33%;为了实现对霉变花生仁表皮霉变程度的判断,采用H和S的阈值识别出霉变区域,再经形态学处理,根据像素数目计算霉变区域面积比,对霉变花生仁的表皮霉变程度进行了判别,正确率为90%。
3.为了实现对破损花生仁的检测,提取破损区域的颜色特征,基于模式匹配,建立了以R、G、B颜色信息为特征参数的破损花生仁检测系统,实现完好与破损花生仁的自动识别,检测准确率为80.12%。
4.为了实现对不同形状花生仁的检测,采用傅立叶变换与傅立叶反变换对描述花生仁形状,该傅立叶描述子前13个谐波的变化特性可以代表花生仁主要形状。利用傅立叶描述子与人工神经网络实现了长形、普通形、三角形、椭圆形和圆形五个类别花生仁形状检测,判别正确率分别为90%、93.3%、96.7%、100%、93.3%。
5.为了实现对不同尺寸花生仁的检测,研究了花生仁面积、周长、长轴长、短轴长、圆度、偏心率、当量直径、紧凑度等几何形状参数的提取方法,建立了花生仁图像投影面积和花生仁重量之间的相关模型,结果表明,图像投影面积和重量存在较显著相关关系;基于支持向量机和几何特征参数建立的网络系统,对花生仁五个尺寸级别的识别准确率大于90%。
6.为了利用视觉技术检测花生仁货架期,研究了花生仁贮藏过程中表皮颜色、纹理、光泽等的变化规律,利用马氏距离判别准则建立了H、I、S三个颜色特征值参数及RW、GW、BW三个纹理参数与贮藏时间之间的关系模型,经验证识别准确率为86.25%。
7.对包括机械系统、视觉检测系统、控制系统、程序软件在内的花生仁外观品质检测系统的软硬件进行了设计,为实现基于计算机视觉的花生仁外观多个外观品质的无损、快速检测提供了理论基础和技术依据。
There are definite stipulations on the imperfect granularity, musty granularity, size, shape and other apparent quality of peanut in the export.
But at present, there is only a kind of photoelectric device which can pick out the musty granularity based on the color in the market, and other apparent quality can only be identified manually, which can not satisfy the need of the market.
Computer vision technology has the features of lossless and rapidness, and it can test several index of quality at a time, it is also convenient for designing and manufacturing production line which can classify agricultural products automatically. All of these show us a bright prospect of the technology on agricultural products auto identification. Using computer vision technology to research the apparent quality of peanut and realizing the apparent quality test of peanut automatically without losses make great sense on enhancing the competitiveness of our peanut. Against such a background, a method of apparent quality test of peanut is discussed in the paper, a designed system suitable for analyzing and testing the apparent quality of peanuts is also included. The main contents are as follows:
1. Basing on the practical demand, several commonly used color models are analyzed, and the basic image processing algorithms of sound filtering, image enhancing, image dividing, and feature extracting are also analyzed and studied. Finally, a basic image processing algorithms suitable for the apparent quality is chosen.
2. To realize the test of musty peanut, the change law of the parameter of feature of color and the parameter of feature of vein is studied in the process of the peanut becoming musty. The feature of color H,I,S and the feature of vein RW、GW、BW are extracted as the inputs of Neural Networks created by MATLAB, and the identification of normal peanut, stale peanut and musty peanut is realized, with the accuracy rate of 95%,90%,100% separately. To identify the musty extent of peanut, H and S Threshold is used to identify the area which is musty. By processing the area morphologically, calculating the area ratio of musty area by total pixels, the musty extent of peanut coat is identified, with the accuracy rate of 90%.
3. To realize the test of imperfect peanut, the feature of color of the imperfect area is extracted, and the system of peanut imperfection test with the parameter of feature of R, G, and B color information is established based on the match of mode, and the identification of perfect and imperfect peanut is realized, with the accuracy rate of 88.56%.
4. To realize the test of different shapes of peanut, Fourier Descriptor and inverse Fourier Descriptor is used to describe the shape of peanut, it is found that the feature of fist thirteen Harmonic Changes can represent the main shape of peanut in the Fourier Descriptor. The Fourier Descriptor and the Artificial Neural Network realize the test of the shape of peanut of oblong, simple, Trilateral, Elliptical and circular, with the accuracy rate of 90%, 93.3%, 96.7%, 100%, 93.3% separately.
5. To realize the test of different sizes of peanut, the method of extracting the geometrical parameter of area, circumference, the length of major axis, the length of minor axis, roundness, eccentricity, equivalent diameter and the compactness of peanut is studied, and the correlation model of projected area and the weight of peanut are established. The result shows that there is a significant relativity between projected area and the weight of peanut. Based on the network system with the support of vector machine and geometrical feature parameter, the accuracy rate of the identification of the size of the peanut with five classes is 90%、95%,95%, 90%, 100% separately.
6. To test the shelf life of peanut by the vision technology, the change law of the color of the coat, the vein and the shine of peanut is studied, and the Bays classifier is used to establish the model which describes the relation between the three parameter of feather of color of H, I, S as well as the vein of RW, GW, BW and the time of storage, and the accuracy rate is 88%.
7. The design of the hardware and software system including machine, vision test, control and program provide the foundation of the rapid test of peanut without losses.
1.结合花生仁图像特征提取的实际要求,分析了几种常用颜色模型的特点,对噪声滤除、图像增强、图像分割、特征提取等图像基础处理算法进行了分析和研究,确定了适用于花生仁外观品质检测的图像底层处理算法。
2.为了实现对霉变花生仁的检测,研究了花生仁霉变过程中,颜色特征参数、纹理特征参数的变化规律,提取颜色特征H、I、S及纹理特征RW、GW、BW作为MATLAB所创建的神经网络的输入,利用BP神经网络模型实现了对正常、不新鲜、霉变三种情况花生仁的判别,正确分类率为96.67%、90%、93.33%;为了实现对霉变花生仁表皮霉变程度的判断,采用H和S的阈值识别出霉变区域,再经形态学处理,根据像素数目计算霉变区域面积比,对霉变花生仁的表皮霉变程度进行了判别,正确率为90%。
3.为了实现对破损花生仁的检测,提取破损区域的颜色特征,基于模式匹配,建立了以R、G、B颜色信息为特征参数的破损花生仁检测系统,实现完好与破损花生仁的自动识别,检测准确率为80.12%。
4.为了实现对不同形状花生仁的检测,采用傅立叶变换与傅立叶反变换对描述花生仁形状,该傅立叶描述子前13个谐波的变化特性可以代表花生仁主要形状。利用傅立叶描述子与人工神经网络实现了长形、普通形、三角形、椭圆形和圆形五个类别花生仁形状检测,判别正确率分别为90%、93.3%、96.7%、100%、93.3%。
5.为了实现对不同尺寸花生仁的检测,研究了花生仁面积、周长、长轴长、短轴长、圆度、偏心率、当量直径、紧凑度等几何形状参数的提取方法,建立了花生仁图像投影面积和花生仁重量之间的相关模型,结果表明,图像投影面积和重量存在较显著相关关系;基于支持向量机和几何特征参数建立的网络系统,对花生仁五个尺寸级别的识别准确率大于90%。
6.为了利用视觉技术检测花生仁货架期,研究了花生仁贮藏过程中表皮颜色、纹理、光泽等的变化规律,利用马氏距离判别准则建立了H、I、S三个颜色特征值参数及RW、GW、BW三个纹理参数与贮藏时间之间的关系模型,经验证识别准确率为86.25%。
7.对包括机械系统、视觉检测系统、控制系统、程序软件在内的花生仁外观品质检测系统的软硬件进行了设计,为实现基于计算机视觉的花生仁外观多个外观品质的无损、快速检测提供了理论基础和技术依据。
There are definite stipulations on the imperfect granularity, musty granularity, size, shape and other apparent quality of peanut in the export.
But at present, there is only a kind of photoelectric device which can pick out the musty granularity based on the color in the market, and other apparent quality can only be identified manually, which can not satisfy the need of the market.
Computer vision technology has the features of lossless and rapidness, and it can test several index of quality at a time, it is also convenient for designing and manufacturing production line which can classify agricultural products automatically. All of these show us a bright prospect of the technology on agricultural products auto identification. Using computer vision technology to research the apparent quality of peanut and realizing the apparent quality test of peanut automatically without losses make great sense on enhancing the competitiveness of our peanut. Against such a background, a method of apparent quality test of peanut is discussed in the paper, a designed system suitable for analyzing and testing the apparent quality of peanuts is also included. The main contents are as follows:
1. Basing on the practical demand, several commonly used color models are analyzed, and the basic image processing algorithms of sound filtering, image enhancing, image dividing, and feature extracting are also analyzed and studied. Finally, a basic image processing algorithms suitable for the apparent quality is chosen.
2. To realize the test of musty peanut, the change law of the parameter of feature of color and the parameter of feature of vein is studied in the process of the peanut becoming musty. The feature of color H,I,S and the feature of vein RW、GW、BW are extracted as the inputs of Neural Networks created by MATLAB, and the identification of normal peanut, stale peanut and musty peanut is realized, with the accuracy rate of 95%,90%,100% separately. To identify the musty extent of peanut, H and S Threshold is used to identify the area which is musty. By processing the area morphologically, calculating the area ratio of musty area by total pixels, the musty extent of peanut coat is identified, with the accuracy rate of 90%.
3. To realize the test of imperfect peanut, the feature of color of the imperfect area is extracted, and the system of peanut imperfection test with the parameter of feature of R, G, and B color information is established based on the match of mode, and the identification of perfect and imperfect peanut is realized, with the accuracy rate of 88.56%.
4. To realize the test of different shapes of peanut, Fourier Descriptor and inverse Fourier Descriptor is used to describe the shape of peanut, it is found that the feature of fist thirteen Harmonic Changes can represent the main shape of peanut in the Fourier Descriptor. The Fourier Descriptor and the Artificial Neural Network realize the test of the shape of peanut of oblong, simple, Trilateral, Elliptical and circular, with the accuracy rate of 90%, 93.3%, 96.7%, 100%, 93.3% separately.
5. To realize the test of different sizes of peanut, the method of extracting the geometrical parameter of area, circumference, the length of major axis, the length of minor axis, roundness, eccentricity, equivalent diameter and the compactness of peanut is studied, and the correlation model of projected area and the weight of peanut are established. The result shows that there is a significant relativity between projected area and the weight of peanut. Based on the network system with the support of vector machine and geometrical feature parameter, the accuracy rate of the identification of the size of the peanut with five classes is 90%、95%,95%, 90%, 100% separately.
6. To test the shelf life of peanut by the vision technology, the change law of the color of the coat, the vein and the shine of peanut is studied, and the Bays classifier is used to establish the model which describes the relation between the three parameter of feather of color of H, I, S as well as the vein of RW, GW, BW and the time of storage, and the accuracy rate is 88%.
7. The design of the hardware and software system including machine, vision test, control and program provide the foundation of the rapid test of peanut without losses.
引文
1.蔡昌,张永林.一种基于简单算法的图像边缘检测方案.暨南大学学报,2005,26(5):633-641
2.蔡文青,倪向东.枸杞色选机的研制.新疆农机化,2007(2):5-7
3.岑益科.基于机器视觉的鸡蛋品质检测方法研究[D].浙江大学,2006
4.成芳.稻种质量的机器视觉无损检测研究.杭州:浙江大学,2005
5.陈全胜,赵杰文,张海东.利用计算机视觉识别茶叶的色泽类型.江苏大学学报,2005,26(6):461-464
6.陈立君,王克奇,王辉.基于BP神经网络木材纹理分类的研究.森林工程,2007,23(1):40-42
7.崔晟,张英远,刘劲松.基于图像数据库的动态场景生成系统.红外与激光工程,2002,3l(1):31-36
8.邓继忠,张泰岭,洪添胜.一种用于水果碰压伤面积检测的数学模型.华南农业大学学报,2000(4):85-87
9.董李艳,胡海平,赵丛.基于人脸重要特征和SVM的人脸识别方法.应用数学与计算数学学报,2008,22(1):109-114
10.段淑芬,胡文广,戴良香.花生黄曲霉毒素国家标准与绿色贸易壁垒.中国农学通报.2006,22(6):95-98
11.冯斌,汪懋华.计算机视觉技术识别水果缺陷的一种新方法.中国农业大学学报,2002,(4):73-76
12.黄红艳,杨熠普.基于高阶神经网络的机械零件形状识别.上海交通大学学报,2001,35(8):1144-1147
13.侯宏花,陈树越,郭保全.医学B超图像降噪处理的三种方法比较.测试技术学报,2003,17(3):74-76
14.黄丽华,高瑞清.计算机自动检测羊绒细度的研究.农业工程学报,2000,16(2):132-134
15.黄星奕,吴守一,方如明.基于小波变换的稻米爆腰检测技术研究.农业工程学报,2004,20(6):194-196
16.李成.番茄果实的基础物理特征及力学性质的研究与应用探讨.哈尔滨:东北农业大学,2005
17.李锦卫,廖桂平.基于数字图像处理的油菜种子形状特征参数提取及模糊聚类分析.计算机辅助工程,2006,15(3):75-78.
18.李庆中,汪懋华.基于计算机视觉的苹果自动分级系统硬件开发.农业机械学报, 2000,31(2):56-59
19.李少军.汽车牌照识别研究与应用.武汉:武汉理工大学,2006
20.李秀智.基于机器视觉的苹果形状分级系统研究.南京:南京农业大学,2003
21.李玉榕,项国波.一种基于马氏距离的线性判别分析分类算法.计算机仿真.2006,23(8):68-70
22.梁炫强,周桂元,潘瑞炽.花生种皮蜡质和角质层与黄曲霉侵染和产毒的关系.热带亚热带植物学报,2003,11(1):11-14
23.林开颜,吴军辉,徐立鸿.基于计算机视觉技术的水果形状分级方法[J].农业机械学报,2005,36(6):72-74
24.凌云.基于分形维数的垩白米图像检测.农业机械学报,2005,36(7):92-95
25.刘国敏,邹猛,刘木华.脐橙色泽与着色率的机器视觉检测技术研究.江西农业大学学报,2008,30(3):551-554,574
26.刘木华,赵杰文,邹小波.牛胸椎骨图像中软骨和硬骨区域的自动分割技术.农业工程学报,2005,21(8):110-113
27.刘小英.基于图像分析的葡萄干分级技术研究.西北农林科技大学,2006
28.刘雪梅.图象处理检测列车轮对算法的研究.山东理工大学学报,2006,20(6):41-43
29.刘中合.基于计算机视觉的玉米种子特征提取及应用研究[D].山东农业大学,2007
30.毛罕平,吴雪梅,李萍萍.基于计算机视觉的番茄缺素神经网络识别.农业工程学报,2005,28(1):106-109
31.宁素俭,李强,杨文志.计算机视觉图形轮廓的获取及其尺寸测量.农业机械学报,1996,12(2):44-49
32.潘磊庆,屠康,苏子鹏.基于计算机视觉和神经网络检测鸡蛋裂纹的研究.2007,23(5):154-158
33.潘伟.计算机视觉在农产品自动检测与分级中的研究-蕃茄的自动检测与分级.哈尔滨:东北农业大学,2000
34.裴凯王卫红。基于免疫原理的入侵检测算法研究.计算机系统应用.2008,(3):19-22
35.任珂,屠康,潘磊庆.青花菜贮藏期间颜色变化动力学模型的建立.农业工程学报,2005,(8):158-162
36.容观澳.计算机图象处理.北京:清华大学出版社,2000
37.阮秋琦.数字图像处理学.北京:电子工业出版社,2003
38.尚艳芬,侯彩云,常国华.基于图像识别的黄粒米自动检测研究.农业工程学报, 2004,20(4):146-148
39.孙建国.花生出口产业亟待提升竞争力.中国检验检疫.2006,(5):21-22
40.孙明.基于计算机视觉的大米外观品质检测.沈阳农业大学学报:2005:36(6):659-662
41.孙永海,鲜于建川,石晶.基于计算机视觉的冷却牛肉嫩度分析方法.农业机械学报,2003,34(5):102-105
42.万书波,单世华,李春娟等.我国花生安全生产现状与策略.花生学报,2005,34(1):1-4
43.汪建,杜世平,王开明.茶叶的计算机识别应用研究.安徽农业科学,2006,34(10):2139-2140
44.汪黎明.织物褶皱纹理灰度共生矩阵分析.青岛大学学报,2003,18(4):5-7
45.王凤朝,黄树采,韩朝超.基于改进的二维Otsu法的图像分割法.航空计算技术,2008,38(4):4-7
王红永,曹其新,刘文秀等.基于神经网络的黄瓜等级判别.农业机械学报,1999,30(6):83-87
46.王慧琴.数字图像处理.北京:北京邮电大学出版社,2006
47.王树文,张长利,房俊龙.基于计算机视觉的番茄损伤自动检测与分类研究.农业工程学报,2005,21(8):98-101
48.王树文,闫成新,张天序等.数学形态学在图像处理中的应用.计算机工程与应用,2004,32:89-92
49.王树文,张长利,房俊龙.基于计算机视觉的番茄损伤自动检测与分类研究.农业工程学报,2005,1(5):98-101
50.王涛,刘文印,孙家广等.傅立叶描述子识别物体的形状.计算机研究与发展,2002,39(12):1714-1719
51.王巧华,熊利荣,丁幼春等.鸡蛋新鲜度神经网络检测系统的研究.华中农业大学学报,2005,24(6):630-632
52.王移收.我国花生产品加工业现状、问题及发展趋势.中国油料作物学报.2006,28(4):498-502
53.王耀波,张艺兵,张鹏等.中国花生产业的发展和出口对策.花生学报,2004,33(2):37-40
54.王志健,王勤海.关于色选机性能与质量的评价.西部粮油科技,2002(2):12-14
55.王忠.我国谷物色选机应用技术现状及发展趋势.农机化研究,2006(4):23-25
56.邢君.外贸花生的质量标准与改进措施.安徽农学通报.2001,7(1):43-45
57.向光蓉.基于图像识别的苹果分级研究.大连:大连海事大学:2003
58.熊利荣,王树才,任奕林等.鸡蛋大小等级模型的修正.农机化研究,2006,(2):104-105
59.徐贵力,毛罕平,李萍萍.彩色图像颜色和纹理特征提取的应用算法.计算机工程,2002,(6):33-35
60.徐小军,邵英,郭尚芬.基于灰度共生矩阵的火焰图像纹理特征分析.计算技术与自动化,2007,26(4):64-67
61.杨新华,王艳,段永军等.基于MATLAB的图像增强处理系统的设计与实现.火力与指挥控制,2008,33(6):143-145,153
62.应义斌,饶秀勤,赵匀等.机器视觉技术在农产品品质自动识别中的应用.农业工程学报,2000,16(1):103-108
63.应义斌,饶秀勤,马俊福.柑橘成熟度机器视觉无损检测方法研究.农业工程学报,2004,20(2):144-147
64.应义斌,景寒松,马俊福等.黄花梨果形的机器视觉识别方法研究.农业工程学报,1999,15(1):192-196
65.游迎荣,范影乐.基于邻域信息的图像自适应滤波.杭州电子科技大学学报,2005,25(3):82-85
66.袁金丽.计算机视觉在苹果外部品质检测与分级方面的应用研究.北京:中国农业大学,2006
67.曾欢,王浩.图像边缘检测算法的性能比较与分析.现代电子技术,2006,14(229):53-58
68.战国科,夏哲雷.基于支持向量机的飞机图像识别算法.现代电子技术.2007,(21):127-129
69.张凯丽,刘辉.边缘检测技术的发展研究.昆明理工大学学报,2000,25(5):36-39
70.张泰岭,邓继忠.应用计算机视觉技术对梨碰压伤的检测.农业工程学报,1999,15(1):205-209
71.张孝灿,黄智才,赵元洪.遥感数字图象处理.杭州:浙江大学出版社,1997
72.赵杰文,刘木华,张海东.基于数学形态学的牛肉图像中背长肌分割和大理石纹提取技术研究.农业工程学报,2004,20(1):144-146
73.赵静,何东健.果实形状的计算机识别方法研究.农业工程学报,2001,17(2):165-167
74.郑华东,刘木华,吴彦红等.基于计算机视觉的大米裂纹检测研究.农业工程学报,2006,22(7):129-133
75.郑丽敏,田立军,朱虹.基于MATLAB的BP神经网络在猪等级评定中的应用研究.计算机应用研究,2008,25(6):1642-1644
76.钟志光,卢君,刘伟荣.Visual C++.NET数字图像处理实例与解析.北京:清华大学出版社,2003
77.周浩敏.信号处理基础.北京:北京航空航天大学出版社,2000
78.周孝宽,曹晓光,陈建革.实用微机图像处理.北京:北京航空航天大学出版社,1994
79.Card A J,Tao Y,Berhardt J L,et al.High resolution machine vision for non-destructive internal inspection of damaged rice grain.In 1998 ASAE Annual International Meeting.Florida:ASAE,1998,No.986024
80.Chih Chung Chang,Chih Jen Lin.LIBSVM:a library for support vector machines[EB/OL].[2007—01—01].http://www.esie.ntu.edu.tw/—ejlin/libsvm
81.CHOI K,LEE G,HAN R J.Tomato maturity evaluation using color image analysis.Transactions of the ASAE,1995,38(1):171-176
82.Church ill D B,Bilsland D M,Cooper T M.Comparison of machine vision with human measurement of seed dimensions.T ransact ions of the ASAE,1992,35(1):61-64
83.Debayle J,Pinoli J C.Multiscale image filtering and segmentation by means of adaptive neighborhood mathematical morphology.Image Processing,2005,3(11-14):537-540
84.Ingrid Paulus,Eddie Schrevens.Shape characterization of new apple cultivars by fourier expansion of digitized images.J.Agric.Engng.Res,1999.72:113-118
85.J.Blasco,S.Cubero,J.Gomez-Sanchis.Development of a machine for the automatic sorting of pomegranate(Punica granatum) arils based on computer vision.Journal of Food Engineering,2008,5(35):27-34
86.Marchant J A,O nyango C M,St reet M J.High speed sort ing of potatoes using computer vision.ASAE Paper No.88-3540,1988
87.Maxime L,Long Q.Image2based rendering by joint view triangulation.IEEE Transactions on Circuits and Systems for Video Technology,2003,13(11):278-282
88.Nal M,Jayas D S.Dockage identification in wheat using machine vision.Canadian gricultural Engineering,1998,40(4):293-298
89.Ng H F,Wilcke W F,Morey R V,et al.Machine Vision Evaluation of Corn Kernel Mechanical and Mold Damage.Trans of the ASAE,1998,41(2):415-420
90.Ni B,Paulsen M R.Corn kenel crown shape identification using image processing.Trans of the ASAE,1997,40(3):835-83
91.Panigrahi S,Misra M K,Bern C,et al.Background segmentation and dimensional measurement of corn germplasm. Transactions of the ASAE,1995, 38 (1) : 291-297
92. Pearson T C, Doster M A, Michailides T J. Automated detection pistachio defects by machine vision. American Society of agricultural Engineers,2001, 17(5): 81-84
93. Pearson T C, Schatzki T F. Machine Vision system for automated detection of flatoxin-contaminated pistachios. Agric-food-chem, 1998, 46(6):2248-2252
94. Ribeiro A, Maria C, Garcia-Alegre M, Guinea D. Automatic Rules Generation by G. A. for Eggshell Detect Classification. European Congress on Computational Methods in Applied Sciences and Engineering, Barcelona,2000
95. Suykens JA K, Vandewalle J. Sparse least squares support vector machine classifiers. European symposium on artifi-cialneuralnetworks. Bruges Belgium, 2000,4:37-42
96. Tao Y, Heinemann P. H, Varghese, et al. Machine Vision for Color Inspection of Potatoes and Apples[J].Trans of the ASAE, 1995(5):1555-1561
97. Tao Y. Heinemann P H. Varghese Z, et al. Machine vision for color inspection of potatoes and apples. Trans of the ASAE, 1995, 38(5): 1555-1561
98. Tom Pearson. Machine Vision System for Automated Detection of Stained Pistachio Nuts. Lebensmittel-Wissenschaft and Technologie, 1996, 29(3) :203-209
99. Yamany S M, Farag A A. Surface signatures: an orientation independent free-form surface representation scheme for the purpose of objects registration and matching [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2002, 24(8): 1105-1120.
2.蔡文青,倪向东.枸杞色选机的研制.新疆农机化,2007(2):5-7
3.岑益科.基于机器视觉的鸡蛋品质检测方法研究[D].浙江大学,2006
4.成芳.稻种质量的机器视觉无损检测研究.杭州:浙江大学,2005
5.陈全胜,赵杰文,张海东.利用计算机视觉识别茶叶的色泽类型.江苏大学学报,2005,26(6):461-464
6.陈立君,王克奇,王辉.基于BP神经网络木材纹理分类的研究.森林工程,2007,23(1):40-42
7.崔晟,张英远,刘劲松.基于图像数据库的动态场景生成系统.红外与激光工程,2002,3l(1):31-36
8.邓继忠,张泰岭,洪添胜.一种用于水果碰压伤面积检测的数学模型.华南农业大学学报,2000(4):85-87
9.董李艳,胡海平,赵丛.基于人脸重要特征和SVM的人脸识别方法.应用数学与计算数学学报,2008,22(1):109-114
10.段淑芬,胡文广,戴良香.花生黄曲霉毒素国家标准与绿色贸易壁垒.中国农学通报.2006,22(6):95-98
11.冯斌,汪懋华.计算机视觉技术识别水果缺陷的一种新方法.中国农业大学学报,2002,(4):73-76
12.黄红艳,杨熠普.基于高阶神经网络的机械零件形状识别.上海交通大学学报,2001,35(8):1144-1147
13.侯宏花,陈树越,郭保全.医学B超图像降噪处理的三种方法比较.测试技术学报,2003,17(3):74-76
14.黄丽华,高瑞清.计算机自动检测羊绒细度的研究.农业工程学报,2000,16(2):132-134
15.黄星奕,吴守一,方如明.基于小波变换的稻米爆腰检测技术研究.农业工程学报,2004,20(6):194-196
16.李成.番茄果实的基础物理特征及力学性质的研究与应用探讨.哈尔滨:东北农业大学,2005
17.李锦卫,廖桂平.基于数字图像处理的油菜种子形状特征参数提取及模糊聚类分析.计算机辅助工程,2006,15(3):75-78.
18.李庆中,汪懋华.基于计算机视觉的苹果自动分级系统硬件开发.农业机械学报, 2000,31(2):56-59
19.李少军.汽车牌照识别研究与应用.武汉:武汉理工大学,2006
20.李秀智.基于机器视觉的苹果形状分级系统研究.南京:南京农业大学,2003
21.李玉榕,项国波.一种基于马氏距离的线性判别分析分类算法.计算机仿真.2006,23(8):68-70
22.梁炫强,周桂元,潘瑞炽.花生种皮蜡质和角质层与黄曲霉侵染和产毒的关系.热带亚热带植物学报,2003,11(1):11-14
23.林开颜,吴军辉,徐立鸿.基于计算机视觉技术的水果形状分级方法[J].农业机械学报,2005,36(6):72-74
24.凌云.基于分形维数的垩白米图像检测.农业机械学报,2005,36(7):92-95
25.刘国敏,邹猛,刘木华.脐橙色泽与着色率的机器视觉检测技术研究.江西农业大学学报,2008,30(3):551-554,574
26.刘木华,赵杰文,邹小波.牛胸椎骨图像中软骨和硬骨区域的自动分割技术.农业工程学报,2005,21(8):110-113
27.刘小英.基于图像分析的葡萄干分级技术研究.西北农林科技大学,2006
28.刘雪梅.图象处理检测列车轮对算法的研究.山东理工大学学报,2006,20(6):41-43
29.刘中合.基于计算机视觉的玉米种子特征提取及应用研究[D].山东农业大学,2007
30.毛罕平,吴雪梅,李萍萍.基于计算机视觉的番茄缺素神经网络识别.农业工程学报,2005,28(1):106-109
31.宁素俭,李强,杨文志.计算机视觉图形轮廓的获取及其尺寸测量.农业机械学报,1996,12(2):44-49
32.潘磊庆,屠康,苏子鹏.基于计算机视觉和神经网络检测鸡蛋裂纹的研究.2007,23(5):154-158
33.潘伟.计算机视觉在农产品自动检测与分级中的研究-蕃茄的自动检测与分级.哈尔滨:东北农业大学,2000
34.裴凯王卫红。基于免疫原理的入侵检测算法研究.计算机系统应用.2008,(3):19-22
35.任珂,屠康,潘磊庆.青花菜贮藏期间颜色变化动力学模型的建立.农业工程学报,2005,(8):158-162
36.容观澳.计算机图象处理.北京:清华大学出版社,2000
37.阮秋琦.数字图像处理学.北京:电子工业出版社,2003
38.尚艳芬,侯彩云,常国华.基于图像识别的黄粒米自动检测研究.农业工程学报, 2004,20(4):146-148
39.孙建国.花生出口产业亟待提升竞争力.中国检验检疫.2006,(5):21-22
40.孙明.基于计算机视觉的大米外观品质检测.沈阳农业大学学报:2005:36(6):659-662
41.孙永海,鲜于建川,石晶.基于计算机视觉的冷却牛肉嫩度分析方法.农业机械学报,2003,34(5):102-105
42.万书波,单世华,李春娟等.我国花生安全生产现状与策略.花生学报,2005,34(1):1-4
43.汪建,杜世平,王开明.茶叶的计算机识别应用研究.安徽农业科学,2006,34(10):2139-2140
44.汪黎明.织物褶皱纹理灰度共生矩阵分析.青岛大学学报,2003,18(4):5-7
45.王凤朝,黄树采,韩朝超.基于改进的二维Otsu法的图像分割法.航空计算技术,2008,38(4):4-7
王红永,曹其新,刘文秀等.基于神经网络的黄瓜等级判别.农业机械学报,1999,30(6):83-87
46.王慧琴.数字图像处理.北京:北京邮电大学出版社,2006
47.王树文,张长利,房俊龙.基于计算机视觉的番茄损伤自动检测与分类研究.农业工程学报,2005,21(8):98-101
48.王树文,闫成新,张天序等.数学形态学在图像处理中的应用.计算机工程与应用,2004,32:89-92
49.王树文,张长利,房俊龙.基于计算机视觉的番茄损伤自动检测与分类研究.农业工程学报,2005,1(5):98-101
50.王涛,刘文印,孙家广等.傅立叶描述子识别物体的形状.计算机研究与发展,2002,39(12):1714-1719
51.王巧华,熊利荣,丁幼春等.鸡蛋新鲜度神经网络检测系统的研究.华中农业大学学报,2005,24(6):630-632
52.王移收.我国花生产品加工业现状、问题及发展趋势.中国油料作物学报.2006,28(4):498-502
53.王耀波,张艺兵,张鹏等.中国花生产业的发展和出口对策.花生学报,2004,33(2):37-40
54.王志健,王勤海.关于色选机性能与质量的评价.西部粮油科技,2002(2):12-14
55.王忠.我国谷物色选机应用技术现状及发展趋势.农机化研究,2006(4):23-25
56.邢君.外贸花生的质量标准与改进措施.安徽农学通报.2001,7(1):43-45
57.向光蓉.基于图像识别的苹果分级研究.大连:大连海事大学:2003
58.熊利荣,王树才,任奕林等.鸡蛋大小等级模型的修正.农机化研究,2006,(2):104-105
59.徐贵力,毛罕平,李萍萍.彩色图像颜色和纹理特征提取的应用算法.计算机工程,2002,(6):33-35
60.徐小军,邵英,郭尚芬.基于灰度共生矩阵的火焰图像纹理特征分析.计算技术与自动化,2007,26(4):64-67
61.杨新华,王艳,段永军等.基于MATLAB的图像增强处理系统的设计与实现.火力与指挥控制,2008,33(6):143-145,153
62.应义斌,饶秀勤,赵匀等.机器视觉技术在农产品品质自动识别中的应用.农业工程学报,2000,16(1):103-108
63.应义斌,饶秀勤,马俊福.柑橘成熟度机器视觉无损检测方法研究.农业工程学报,2004,20(2):144-147
64.应义斌,景寒松,马俊福等.黄花梨果形的机器视觉识别方法研究.农业工程学报,1999,15(1):192-196
65.游迎荣,范影乐.基于邻域信息的图像自适应滤波.杭州电子科技大学学报,2005,25(3):82-85
66.袁金丽.计算机视觉在苹果外部品质检测与分级方面的应用研究.北京:中国农业大学,2006
67.曾欢,王浩.图像边缘检测算法的性能比较与分析.现代电子技术,2006,14(229):53-58
68.战国科,夏哲雷.基于支持向量机的飞机图像识别算法.现代电子技术.2007,(21):127-129
69.张凯丽,刘辉.边缘检测技术的发展研究.昆明理工大学学报,2000,25(5):36-39
70.张泰岭,邓继忠.应用计算机视觉技术对梨碰压伤的检测.农业工程学报,1999,15(1):205-209
71.张孝灿,黄智才,赵元洪.遥感数字图象处理.杭州:浙江大学出版社,1997
72.赵杰文,刘木华,张海东.基于数学形态学的牛肉图像中背长肌分割和大理石纹提取技术研究.农业工程学报,2004,20(1):144-146
73.赵静,何东健.果实形状的计算机识别方法研究.农业工程学报,2001,17(2):165-167
74.郑华东,刘木华,吴彦红等.基于计算机视觉的大米裂纹检测研究.农业工程学报,2006,22(7):129-133
75.郑丽敏,田立军,朱虹.基于MATLAB的BP神经网络在猪等级评定中的应用研究.计算机应用研究,2008,25(6):1642-1644
76.钟志光,卢君,刘伟荣.Visual C++.NET数字图像处理实例与解析.北京:清华大学出版社,2003
77.周浩敏.信号处理基础.北京:北京航空航天大学出版社,2000
78.周孝宽,曹晓光,陈建革.实用微机图像处理.北京:北京航空航天大学出版社,1994
79.Card A J,Tao Y,Berhardt J L,et al.High resolution machine vision for non-destructive internal inspection of damaged rice grain.In 1998 ASAE Annual International Meeting.Florida:ASAE,1998,No.986024
80.Chih Chung Chang,Chih Jen Lin.LIBSVM:a library for support vector machines[EB/OL].[2007—01—01].http://www.esie.ntu.edu.tw/—ejlin/libsvm
81.CHOI K,LEE G,HAN R J.Tomato maturity evaluation using color image analysis.Transactions of the ASAE,1995,38(1):171-176
82.Church ill D B,Bilsland D M,Cooper T M.Comparison of machine vision with human measurement of seed dimensions.T ransact ions of the ASAE,1992,35(1):61-64
83.Debayle J,Pinoli J C.Multiscale image filtering and segmentation by means of adaptive neighborhood mathematical morphology.Image Processing,2005,3(11-14):537-540
84.Ingrid Paulus,Eddie Schrevens.Shape characterization of new apple cultivars by fourier expansion of digitized images.J.Agric.Engng.Res,1999.72:113-118
85.J.Blasco,S.Cubero,J.Gomez-Sanchis.Development of a machine for the automatic sorting of pomegranate(Punica granatum) arils based on computer vision.Journal of Food Engineering,2008,5(35):27-34
86.Marchant J A,O nyango C M,St reet M J.High speed sort ing of potatoes using computer vision.ASAE Paper No.88-3540,1988
87.Maxime L,Long Q.Image2based rendering by joint view triangulation.IEEE Transactions on Circuits and Systems for Video Technology,2003,13(11):278-282
88.Nal M,Jayas D S.Dockage identification in wheat using machine vision.Canadian gricultural Engineering,1998,40(4):293-298
89.Ng H F,Wilcke W F,Morey R V,et al.Machine Vision Evaluation of Corn Kernel Mechanical and Mold Damage.Trans of the ASAE,1998,41(2):415-420
90.Ni B,Paulsen M R.Corn kenel crown shape identification using image processing.Trans of the ASAE,1997,40(3):835-83
91.Panigrahi S,Misra M K,Bern C,et al.Background segmentation and dimensional measurement of corn germplasm. Transactions of the ASAE,1995, 38 (1) : 291-297
92. Pearson T C, Doster M A, Michailides T J. Automated detection pistachio defects by machine vision. American Society of agricultural Engineers,2001, 17(5): 81-84
93. Pearson T C, Schatzki T F. Machine Vision system for automated detection of flatoxin-contaminated pistachios. Agric-food-chem, 1998, 46(6):2248-2252
94. Ribeiro A, Maria C, Garcia-Alegre M, Guinea D. Automatic Rules Generation by G. A. for Eggshell Detect Classification. European Congress on Computational Methods in Applied Sciences and Engineering, Barcelona,2000
95. Suykens JA K, Vandewalle J. Sparse least squares support vector machine classifiers. European symposium on artifi-cialneuralnetworks. Bruges Belgium, 2000,4:37-42
96. Tao Y, Heinemann P. H, Varghese, et al. Machine Vision for Color Inspection of Potatoes and Apples[J].Trans of the ASAE, 1995(5):1555-1561
97. Tao Y. Heinemann P H. Varghese Z, et al. Machine vision for color inspection of potatoes and apples. Trans of the ASAE, 1995, 38(5): 1555-1561
98. Tom Pearson. Machine Vision System for Automated Detection of Stained Pistachio Nuts. Lebensmittel-Wissenschaft and Technologie, 1996, 29(3) :203-209
99. Yamany S M, Farag A A. Surface signatures: an orientation independent free-form surface representation scheme for the purpose of objects registration and matching [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2002, 24(8): 1105-1120.
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