基于机器视觉的大豆品质的研究
|
摘要
目前,采用机器视觉技术检测食品及农产品的品质正逐步推广应用。大豆作为一种重要的农产品,日益受到人们的重视,但是目前对大豆品质的检测、分级仍然停留在人工水平上。因此应用机器视觉技术对大豆进行品质检测有着重要的意义。
本研究构建了基于计算机视觉的大豆检测系统:首先选择大豆图像预处理算法,然后提取大豆的形状和颜色特征,最后利用提取的大豆特征,基于MATLAB构建神经网络对大豆品质检测做了初步探索。得到以下结论:
1.在本研究条件下,对光源的照明方式进行试验得到,在前向光源下能够有效地得到大豆的绝大部分外观特征。在相同的光源与照明方式下,选用红色、蓝色、黄色、黑色和白色的背景,以相同的二值分割参数进行试验,得到以黑色为背景进行分析效果最佳。对传统的图像算法进行分析和比较,包括图像滤波,边缘检测等,得到中值滤波能够明显的降低大豆图像的噪声,在灰度值变化较小的情况下,降低了图像边界部分的模糊程度,有良好的平滑效果。对黏连籽粒,应用基于形态学的二值分割方法和分水岭分割方法进行对比分析,发现应用分水岭方法得到的黏连分割图像的效果良好。应用双峰法与生长法相结合的算法,实现了背景与目标豆粒的分离,并且除去大部分噪声,进而分离得到单个分离的豆粒。从而获得了目标图像。
2.定义并提取了大豆种子29个形态特征,可以同时对图像中多个大豆籽粒进行特征提取,大大提高了大豆特征提取的效率。根据BP人工神经网络的结构特点和网络参数设计要求,设计了面向MATLAB的BP神经网络。对比各种算法得到,Levenberg-Marquardt算法函数为各层之间的优化函数。对于单种缺陷的网络隐含层设为10,多种缺陷的网络设为23。
3.优化特征选择,应用SAS分析软件对29个特征进行LOGISTIC和CORR分析,根据检测要求选择特征指标。结果表明,神经网络对于识别单种缺陷有很好的检验能力,其中对未成熟的检测率达到了100%,而对其它的缺陷中标准粒的检出也达到了95%以上;相对来说对缺陷粒的检出较差,但其中对于检出率最差的破碎粒的检出率也达到了81%。对于一次性识别多项豆粒缺陷的研究,10特征值的网络能够很好的区分标准粒和缺陷粒,标准豆粒的识别率为99%,未能确定是否良好的比率为1%;把虫蚀、霉变、破碎、未成熟和菌斑五种影响大豆品质的豆粒误检为标准大豆的比率为0.2%。对虫蚀、霉变、破碎、未成熟和菌斑的识别率分别为40%、51%、42%、96%、87%。
4.经过SAS回归分析,得到了外观与营养品质的数学方程。经过回归分析得到蛋白质与长宽比、似圆度、G标准差、B标准差的回归方程:
脂肪与面积、H均值、H标准差的回归方程为:其中x1为面积, x1 5为H均值, x1 8为H标准差。
In recent years, machine vision technology has been developed in all sectors rapidly. Particularly, its superiority in the detection of food and agricultural products is outstanding. Soybean as a major agricultural product has provoked an increasing emphasis. However, the detection of soybean, classification remains at a level of artificial. Therefore, application of machine vision technology to detect soybean quality is of important significance.
In this study, soybean feature extraction system was constructed based on machine vision. Firstly, choosing image pre-processing algorithm. Secondly, extracting soybean shape and color characteristics. Finally, using soybean features to construct neural network and detecting soybean quality as a preliminary exploration. The main conclusions were obtained as following.
1. In this study conditions, lighting way was checked out through testing different ways. Finally, forward light source was selected. In the same light source and lighting way, black background was selected from white, blue, red, yellow, black ones. Series of image preprocessing, such as transformations geometric, color-transformation, image enhancement, morphological processing, and image segmentation and so on, were analyzed and compared. Median filter could significantly lower soybeans image noises. In the case of grey-scale changes in small value, well smoothing effect could be achieved. Compared to morphology algorithm, watershed algorithm could achieve better effect. Application of bimodal algorithm and growth algorithm combining algorithm, background and objective beans were separated, removed most of the noises, and single beans was obtained. Then objective image was obtained.
2. 29 morphology features of soybean were defined and extracted. The algorithm could extract multiple features of soybeans at the same time; the efficiency of soybeans feature extraction has been greatly improved. Structural characteristics and design parameter of BP artificial neural network were analyzed. BP artificial neural network structure of was designed using MATLAB software. Compared with kinds of optimization functions by experimental analysis, Levenberg-Marquardt function was selected as training parameter. Nodal points of hidden layer were selected to be 10 for one kind of defective soybeans detecting, which was selected to be 23 for all kinds of defective soybeans detecting.
3. Feature Selection Optimized. 29 characteristics were analyzed with LOGISTIC Regression and CORR analytical methods using SAS software. According to different requirements, different feature were selected .In terms of training results it was known that the effect of defective identification by the method of neural network was good to respectively detect different defects. The rate of immature beans detection was 100%, and others were above 95%, except broken was 81%. In one-time identification of a number of defective beans study, after SAS analyzing, 10 features were obtained to construct neural network to do the detection. The good one’s detection rate was 99%, and the rate of mistaking good ones for defect ones was 1%. The detection rate of moth-eaten beans, mildew beans, broken beans, immature beans and beans with spots caused by Bacteria were 40%,51%,42%,96%,87%,respectively. The rate of mistaking defect ones for good ones was 0.2%.
4. Based on regression analysis, using SAS, Mathematical equations between appearance and nutritional quality had been developed as following: Protein: x1 0, x1 2, x 25, x 26 were aspect ratio, roundness likeness, Standard deviation, B Standard deviation, respectively. Oil: x1 , x1 5, x1 8 were area average value Standard deviation, respectively.
本研究构建了基于计算机视觉的大豆检测系统:首先选择大豆图像预处理算法,然后提取大豆的形状和颜色特征,最后利用提取的大豆特征,基于MATLAB构建神经网络对大豆品质检测做了初步探索。得到以下结论:
1.在本研究条件下,对光源的照明方式进行试验得到,在前向光源下能够有效地得到大豆的绝大部分外观特征。在相同的光源与照明方式下,选用红色、蓝色、黄色、黑色和白色的背景,以相同的二值分割参数进行试验,得到以黑色为背景进行分析效果最佳。对传统的图像算法进行分析和比较,包括图像滤波,边缘检测等,得到中值滤波能够明显的降低大豆图像的噪声,在灰度值变化较小的情况下,降低了图像边界部分的模糊程度,有良好的平滑效果。对黏连籽粒,应用基于形态学的二值分割方法和分水岭分割方法进行对比分析,发现应用分水岭方法得到的黏连分割图像的效果良好。应用双峰法与生长法相结合的算法,实现了背景与目标豆粒的分离,并且除去大部分噪声,进而分离得到单个分离的豆粒。从而获得了目标图像。
2.定义并提取了大豆种子29个形态特征,可以同时对图像中多个大豆籽粒进行特征提取,大大提高了大豆特征提取的效率。根据BP人工神经网络的结构特点和网络参数设计要求,设计了面向MATLAB的BP神经网络。对比各种算法得到,Levenberg-Marquardt算法函数为各层之间的优化函数。对于单种缺陷的网络隐含层设为10,多种缺陷的网络设为23。
3.优化特征选择,应用SAS分析软件对29个特征进行LOGISTIC和CORR分析,根据检测要求选择特征指标。结果表明,神经网络对于识别单种缺陷有很好的检验能力,其中对未成熟的检测率达到了100%,而对其它的缺陷中标准粒的检出也达到了95%以上;相对来说对缺陷粒的检出较差,但其中对于检出率最差的破碎粒的检出率也达到了81%。对于一次性识别多项豆粒缺陷的研究,10特征值的网络能够很好的区分标准粒和缺陷粒,标准豆粒的识别率为99%,未能确定是否良好的比率为1%;把虫蚀、霉变、破碎、未成熟和菌斑五种影响大豆品质的豆粒误检为标准大豆的比率为0.2%。对虫蚀、霉变、破碎、未成熟和菌斑的识别率分别为40%、51%、42%、96%、87%。
4.经过SAS回归分析,得到了外观与营养品质的数学方程。经过回归分析得到蛋白质与长宽比、似圆度、G标准差、B标准差的回归方程:
脂肪与面积、H均值、H标准差的回归方程为:其中x1为面积, x1 5为H均值, x1 8为H标准差。
In recent years, machine vision technology has been developed in all sectors rapidly. Particularly, its superiority in the detection of food and agricultural products is outstanding. Soybean as a major agricultural product has provoked an increasing emphasis. However, the detection of soybean, classification remains at a level of artificial. Therefore, application of machine vision technology to detect soybean quality is of important significance.
In this study, soybean feature extraction system was constructed based on machine vision. Firstly, choosing image pre-processing algorithm. Secondly, extracting soybean shape and color characteristics. Finally, using soybean features to construct neural network and detecting soybean quality as a preliminary exploration. The main conclusions were obtained as following.
1. In this study conditions, lighting way was checked out through testing different ways. Finally, forward light source was selected. In the same light source and lighting way, black background was selected from white, blue, red, yellow, black ones. Series of image preprocessing, such as transformations geometric, color-transformation, image enhancement, morphological processing, and image segmentation and so on, were analyzed and compared. Median filter could significantly lower soybeans image noises. In the case of grey-scale changes in small value, well smoothing effect could be achieved. Compared to morphology algorithm, watershed algorithm could achieve better effect. Application of bimodal algorithm and growth algorithm combining algorithm, background and objective beans were separated, removed most of the noises, and single beans was obtained. Then objective image was obtained.
2. 29 morphology features of soybean were defined and extracted. The algorithm could extract multiple features of soybeans at the same time; the efficiency of soybeans feature extraction has been greatly improved. Structural characteristics and design parameter of BP artificial neural network were analyzed. BP artificial neural network structure of was designed using MATLAB software. Compared with kinds of optimization functions by experimental analysis, Levenberg-Marquardt function was selected as training parameter. Nodal points of hidden layer were selected to be 10 for one kind of defective soybeans detecting, which was selected to be 23 for all kinds of defective soybeans detecting.
3. Feature Selection Optimized. 29 characteristics were analyzed with LOGISTIC Regression and CORR analytical methods using SAS software. According to different requirements, different feature were selected .In terms of training results it was known that the effect of defective identification by the method of neural network was good to respectively detect different defects. The rate of immature beans detection was 100%, and others were above 95%, except broken was 81%. In one-time identification of a number of defective beans study, after SAS analyzing, 10 features were obtained to construct neural network to do the detection. The good one’s detection rate was 99%, and the rate of mistaking good ones for defect ones was 1%. The detection rate of moth-eaten beans, mildew beans, broken beans, immature beans and beans with spots caused by Bacteria were 40%,51%,42%,96%,87%,respectively. The rate of mistaking defect ones for good ones was 0.2%.
4. Based on regression analysis, using SAS, Mathematical equations between appearance and nutritional quality had been developed as following: Protein: x1 0, x1 2, x 25, x 26 were aspect ratio, roundness likeness, Standard deviation, B Standard deviation, respectively. Oil: x1 , x1 5, x1 8 were area average value Standard deviation, respectively.
引文
岑益郎. 2006.计算机视觉技术在杂草识别中的应用[D].浙江大学
柴玉华,高立群,王蓉等. 2006.基于多尺度形态学大豆图像滤波方法[J].农业工程学报.22(6):112~119
陈庆园,陈雪,袁有波. 2008.初烤烟叶外观质量与主要化学成分关系的研究[J].中国烟草科学.29(1):30~32
陈国庆,姜东,朱艳等. 2004.利用计算机视觉识别小麦叶色的光照模型研究[J].农业工程学报. 20(4):143~145
成芳. 2004.稻种质量的机器视觉无损检测研究[D].浙江大学
丁浩,艾矫燕. 2007.基于计算机视觉的植物黑腐病病斑分析.计算技术与自动化[J].26(1):115~118
范彦英,赵继文,郭凤英. 2003.影响大豆种子外观品质的因素及对策[J].种子.3:58~59
方如明. 1992.计算机图像处理与米的品质检测[J].农业工程学报.8(3):104~112
冯斌,汪懋华. 2002.计算机视觉系统中图像外边缘检测的新方法[J].中国农业大学学报.7(2):72~75
冯斌. 2002.计算机视觉信息处理方法与水果分级检测技术研究[D].中国农业大学
冯丽娟. 2007.不同基因型与环境因素对高油大豆品质性状的影响[D].黑龙江八一农垦大学
傅艳华,关景贵. 1989.大豆子粒几个外观品质性状与其蛋白质含量相关的探讨[J].吉林农业科学. 46~49
高艳霞,李禹生. 2007.基于粒子群算法和神经网络的大豆识别研究[J].中国粮油学报. 22(4)121~124
谷彩连,孙国凯,王立地. 2006.基于MATLAB和BP神经网络的角点检测方法研究[J].基于MATLAB和BP神经网络的角点检测方法研究.19(2):22~26
谷口庆治. 2002.数字图像处理[M].北京:科学出版社
海鹰. 2007.基于分水岭算法的彩色细胞图像分割研究[D].内蒙古师范大学
贺秋伟. 2007.基于计算机视觉的微小尺寸精密检测理论与技术研究[D].吉林大学
胡国华. 2004.大豆品质生态特性及其遗传表达特性的研究[D].东北农业大学
黄星奕. 1999.基于计算机视觉的稻米外观质量检测系统研究[D].江苏理工大学
黄玉,王远弟. 2006.基于灰度值数学形态学算子的中值曲率驱动方程去噪模型[J].上海大学学报(自然科学版).12(5):488~499
纪延俊,杜玉杰. 2007.黏连气泡图像的分割[J].滨州学院学报.23(6):30~33
贾迪野. 2005.基于偏微分方程的图像平滑与分割研究[D].哈尔滨工程大学.
金剑,王光华,刘晓. 2007.50年黑龙江省大豆遗传改良的产量及品质变化[J].农业现代化研究.28(6):757~759
孔明. 2005.颗粒粒径和形态计算机视觉测量方法研究[D].东南大学
黎静,薛龙,刘木华等.2006.基于计算机视觉的脐橙分级系统研究[J].江西农业大学学报. 28(2):304~307
李海燕,刘惕若,甄鸿杰. 2005.灰斑病所致大豆品质与产量损失的研究[J].农业现代化研究.27(3):66~69
李姣. 2006.茶叶品质的计算机视觉分级技术研究[D].西北农林科技大学
梁琴. 2006.常用图像文件颜色特征提取的研究[D].辽宁工程科技大学
林家春,李伟,荀一. 2005.多目标动态检测中摄像机标定技术研究[J].农业工程学报.21(6):18~21
刘奇,谢甫绨,谢志涛. 2007.不同来源大豆品种籽粒品质的比较研究[J].大豆科学.26(2):154~157
刘少伯,任振森. 1993.试论我国动物蛋白饲料源的开发[J].中国畜牧杂志.29(3):13~15.
刘顺湖,周瑞宝,盖钧镒. 2004.大豆蛋白质及其组分含量对豆腐产量和品质的影响[J].中国油脂.32(2):65~68
刘新利,王厚大. 2006.球形黏连图像分割算法的研究[J].球形黏连图像分割算法的研究.32(7):45~46
刘玉兰,汪学德. 2007.对品质受损大豆加工产品质量的研究及评价[J].中国粮油学报.22(6):50~54
刘兆艳. 2006.基于机器视觉的稻种品种识别研究[D].浙江大学
罗敏,朱晓岷,李小红等. 2008.基于径向小波变换的图像特征提取算法[J].武汉大学学报.33(1):29~31
宁海龙,李文霞,潘相文等. 2002.大豆氨基酸组分影响蛋白质含量的通径分析[J].大豆科学.21(4):259~262
宋韬,曾德超. 1996.作物籽粒图象识别的光照条件研究[J].农业机械学报.27(2):39~42.
孙树亮. 2006.基于计算机视觉的芒果分类[D].广西大学
唐传核. 2004.植物功能性食品[M].北京:化学工业出版社
王继峰,赵小铭,宋玉发等. 2006.浅谈大豆外观品质对化学品质的影响[J].黑龙江农业科学.(5):23~24
王家文,曹宇. 2004.MATLAB6.5图形图像处理[M].北京:国防工业出版社.
王茹芳,卢思慧,曹金锋等. 2007.夏大豆品种农艺性状及子粒品质分析[J].作物杂志.1:25~27
魏海坤. 2005.神经网络结构设计的理论与方法[M].北京:国防工业出版社.
魏瑾,韩斌,张其亮. 2006.黏连蚕卵图像分离算法研究[J].江苏科技大学学报(自然科学版).20(1):46~50
吴春林. 2006.基于PDE的隐式曲面上的图像处理[D].中国科学技术大学.
吴雪梅,程尧,韦龙琴. 2005.在MATLAB环境中基于计算机视觉的番茄识别研究[J]. 31(4):15~17
吴雪梅,毛罕平. 2004.计算机视觉描述缺素番茄叶片颜色变化的研究[J].5:212~214
辛惠普,台莲梅,范文艳等. 2003.大豆病虫害防治彩色图谱[M]北京:中国农业出版社
徐红华,申德超. 2007.改进的挤压膨化浸出工艺对大豆油脂及粕蛋白品质的改善[J].农业工程学报.23(4):249~252
徐建瑜,姜雄晖,刘鹰. 2006.基于计算机视觉的鱼体色明暗程度量化方法[J].农机化研究.6:140~152
徐立新,梁娟,查金水等. 2007.基于颜色常量的图像特征提取及其降维方法[J].计算机工程与设.28(3):602~604
徐全生,李美怡. 2007.人脸图像特征点的定位与提取方法的研究[J].沈阳工业大学学报.29(1):90~94
许俐. 1997.机器视觉测量大米留胚率的研究[J].江苏理工大学学报.18(6):5~11
荀一,蔡卫国,李伟. 2006.谷物种子精选自动化系统研究[J].高技术通讯.16(3):167~170
闫之烨. 2003.基于计算机视觉的苹果颜色分级系统的研究[D].南京农业大学
阎少宏. 2005.离散曲面上曲率的刻划[D].大连理工大学
杨庆凯. 2000.论大豆蛋白质与油分含量品质的变化及影响的因素[J].大豆科学.19(4): 386~391
杨淑莹. 2008.模式识别与智能计算MATLAB技术实现[M].北京:电子工业出版社
杨述平. 2005.基于计算机视觉的三维测试技术研究[D].中北大学
叶志刚. 2001.基于颜色特征的图像检索匹配算法的研究及其系统的开发[D].中国农业大学
阴法明. 2006.一种黏连颗粒图像的快速分割方法[J].微处理机.4:80~81
袁金丽. 2005.计算机视觉在苹果外部品质检测与分级方面的应用研究[D].中国农业大学
岳爱琴,杜维俊,赵晋忠等. 2005.不同大豆品种品质分析[J].华北农学报. 20(2):30~32
曾绍校,张怡,梁静. 2007.中国22个莲子品种外观品质和淀粉品质的研究[J].中国食品学.7(1):74~78
张广军. 2005.机器视觉[M].北京:科学出版社
张立华. 2006.基于计算机视觉的芒果表面缺陷检测方法研究[D].广西大学
张丽莉,姚勇,刘志镜. 2008.一种相对势能分水岭算法[J].西安电子科技大学学报. 35(1):121~125
张平,檀结庆,何蕾. 2007.基于曲率和中值滤波的快速推移图像修复方法[J].计算机应用研究.24(8):186~192
章海亮,左雪平. 2006.苹果自动分级中计算机视觉信息处理技术的研究[J].农机化研究.1:28~30
章文英,应义斌. 2005.苹果着色面积的计算机视觉研究[J].农机化研究.4:90~92
钟金传. 2005.中国大豆产业国际竞争力研究[D].中国农业大学
周佳璐. 2006.基于机器视觉的小麦质量判别系统的研究[D].同济大学
朱圣盼. 2007.基于计算机视觉技术的植物病害检测方法的研究[D].浙江大学
Adelson E, Movshon J. 1982. Phonemenal coherence of moving plaid pattems [J].Nature.300(5892):523~525
Aggarwal J, Cai Q. 1999. Human motion analysis: A Review [J] .Computer vision and image understanding.73 (3):428~440
Amari S. 2001.Information geometry on hierarchy of probability distributions[J].IEEE Transaction on information theory.47(5):1701~1711
Amira. 2004.Visualization and Modeling System [OL].http://www.AmiraVis.com.
Atanasov I.S, Durrell J.H, Vulkova L.A, et al. 2006.Statistical characterization of surface morphologies[J]. Physica.371:361~367
Ballester C, Caselles V, Verdera J. 2003.Disocclusion by joint interpolation of vector fields and Gray [J]. Multistage modeling and simulation.2:80~123
Barrett T. M, Zuuring H. R, Christopher T. 2007.Interpretation of forest characteristics from computer-generated images [J]. Landscape and Urban Planning.80 :396~403
Beaulieu C. 2002.The basis of anisotropic water diffusion in the nervous Systema technical review [J].NMR Biomed.15:435~455
Blasco J, Aleixos N, MoltóE, et al. 2007.Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm [J].Journal of Food Engineering. 81:535~543
Cagnoni S, Evelyne, Lutton. 2006.Introduction to the special issue on evolutionary computer vision and image understanding [J]. Pattern Recognition Letters.27:1161~1163
Cardarelli A.J, Tao Y, Berhardt J.L, et al. 1998. High resolution machine vision for non-destructive Internal inspection of damaged rice grain. In: 1999 ASAE Annual international Meeting.No. 986024
Cheng J, Yang J, Zhou Y. 2006.Flexible background mixture models for foreground segmentation. Image and Vision Computing.24:473~482
Churchill D. B, Bilsland D. M, Cooper T. M. 1992.Comparison of Machine Vision with Human Measurement of Seed Dimensions [J].Transactions of the ASAE.35(1):61~64
Dana W, Ivo W. 2008.Computer image analysis of seed shape and seed color for ?ax cultivar description [J]. Computers and electronics in agriculture.61:126~135
Du C.J, Sun D.W. 2005.Pizza sauce spread classification using colour vision and support vector machines [J]. Journal of Food Engineering.66:137~145
Ercana M.F, Fung Y.F. 2000.The design and evaluation of a multiprocessor system for computer vision [J]. Microprocessors and Microsystems.24:365~377
Ercana M.F, Fung Y.F. 2000.The design and evaluation of a multiprocessor system for computer vision [J].Microprocessors and Microsystems.24:365~377
Ge G.K, Zheng X, Wu J.G, et al.2007.Analysis of the conditional correlations from different genetic systems between the protein content and the appearance quality traits of indica rice [J].Journal of Genetics and Genomics.34(2):129~137
Gómez S. J, MoltóE, Camps-Valls G, et al. 2008.Automatic correction of the effects of thelight source on spherical objects.An application to the analysis of hyperspectral images of citrus fruits [J].Journal of Food Engineering,85:191~200
Gouton P. F, Devaux J.C, Truchete F.T. 2004.New methods for analyzing colour texture based on the Karhunen–Loeve transform and quantification [J].Pattern Recognition. 37:1661~1674
Hagan M. T, Demuth H. B, Mark H.Beale. 2005.神经网络设计[M].北京:机械工业出版社
Hepworth N.J, Hammond J.R.M, Varley J. 2004. Novel application of computer vision to determine bubble size distributions in beer [J].Journal of Food Engineering.61:119~124
K?l?c K, Hakkiì. B, K?lsel H, et al. 2007.A classification system for beans using computer vision system and artificial neural networks [J].Journal of Food Engineering. 78:897~904
Leonardis A, Bischof H. 2003.Kernel and subspace methods for computer vision [J].Pattern Recognition.36:1925~1927
Liao,K, paulsen M.R, Reid J.F, et al. 1993.Corn kernel break age classification by machine vision using a neural network classifier.Trans of the ASAE,36(6):1949~1954
Luo X, Jayas D.S, Symons S.J.1997.comparison of statistical and neural network methods for classifying cereal grains using machine vision[C].Transactions of the ASAE,No.973106
Manickavasagan A, G.Sathya, Jayas D.S. 2008.Comparison of illuminations to identify wheat classes using monochrome images [J]. Computers and Electronics in Agriculture.3(2):237-244
Munkevik P, Hall G, Duckett T. 2007. A computer vision system for appearance-based descriptive sensory evaluation of meals [J].Journal of Food Engineering.78:246~256
Nair M, Jayas D.S. 1998.Dockage identification in wheat using machine vision [J].Canadian Agricultural Engineering.40 (4):293~298
Paliwal J, Shashidhar N.S, Jayas D.S. 1999. Grain kernel identification using kernel signature.Trans of the ASAE.42 (6):1921-1924
Paragios N, Chen Y.M, Faugeras O. 2006. Handbook of mathematical models in computer vision [M].Springer science & business media
Pedreschi F, León J, Mery D, et al. 2006. Development of a computer vision system to measure the color of potato chips [J].Food research international.39:1092~1098
Perrier E, Ana M, Tarquis, Dathe A. 2006.A program for fractal and multifractal analysis of two-dimensional binary images: Computer algorithms versus mathematical theory [J].Geoderma. 134:284~294
Purcara M, Dorochenkoa A, Bortelsa L. 2008. Advanced CAD integrated approach for 3D electrochemical machining simulations [J].Journal of materials processing technology.203:58~71.
Quevedo R, Mendoza F, Aguilera J.M. 2006.Determination of senescent spotting in banana (Musa cavendish) using fractal texture Fourier image [J].Journal of Food Engineering. 2008, 84:509~515
Reid J.F, Kim C, Paulsen M.R. 1991.Computer vision sensing of stress cracks in cornkernels [J]. Transactions of the ASAE,34(5):2236~2244
Schaeffer G. W, Sharpe F.T, Sicher R.C. 1997.Fructose 1,6-bisphosphate aldolase activity in leaves of a rice mutant selected for enhanced lysine[J].Phytochemistry. 6(8):1335~1338
Shashidhar N.S, Jayas D.S, Crowe T.G, et al.1997. Processing of digital images of touching kernels by ellipse fitting [J].Canadian Agricultural Engineering.39 (2):139~142
Shatadal,P, Jayas D.S, Bulley N.R. 1995.Digital image analysis for software separation and classification of touching grains: 2 Classification.[J]. Transactions of the ASAE,38(2):645~649
?kaloudováa B, K?ivanb V, Zemek R. 2006. Computer-assisted estimation of leaf damage caused by spider mites [J].Computers and Electronics in Agriculture.53:81~91
Steenhoek L, Misra M, Batchelor W, et al. 1999. Probabilistic neural networks for segmentation of features in corn kernel images[C]. In: 1999 ASAE Annual international Meeting. No.993198
Stewar C.T, Tsai C.L, Roysam B. 2003. The dual-bootstrap iterative closest point algorithm with application to retinal image registration [J].IEEE Transactions on medical imaging.22:1379~1394
Stewart C. 1999. Robust parameter estimation in computer nision [J].SIAM Reviews.41: 513~537
Strecha C, Fransens R, Van G.L. 2004. Wide base line stereo from multiple views: A probabilistic account [J].In IEEE international conference on computer vision and pattern recognition.2: 552~559 Strecha C, Tuytelaars T, Van G.L. 2003.Dense Matching of multiple wide-baseline views [J].In IEEE International Conference on Computer Vision.1194~1201
Sun D.W.2002.Computer vision–an objective,rapid and non-contact quality evaluation tool for the food industry [J].Journal of Food Engineering.61:1~2
Sun Y.R, Fisher R. 2003. Object based visual attention for computer vision [J].Artificial Intelligence.146: 77~123.
Szummer M, Picard R. 1996. Temporal texture modeling [J].In IEEE International Conference on Image Processing.3:823~826
Tagare H, O'shea D, Groisser D. 2002. Non-rigid shape comparison of plane curves in Images [J] Math. Imaging Vis. 16(l):57~68
Tahir A.R, Neethirajan S, Jayas D.S, et al. 2007.Evaluation of the effect of moisture content on cereal grains by digital image analysis [J]. Food Research International, 40(9): 1140~1145
Tan J.L. 2004. Meat quality evaluation by computer vision[J].Journal of Food Engineering. 61:27~35
Tan P, Lin S, Quan L, et al. 2003. Highlight removal by illumination~constrained in painting [J].In Proceedings International Conference on Computer Vision.164~169
Tang B, Sapiro G, Caselles V. 2001. Color image enhancement via chromaticity diffusion[J] .IEEE Transactions on Image Processing.10:701~707
Tschumperle D , Deriche R. 2001. Diffusion tensor regularization with constraints preservation [J] .In CVPR.948~954
Venora G, Grillo O, Shahin M.A, Symons S.J. 2007.Identification of sicilian landraces and canadian cultivars of lentil using an image analysis system [J].Food Research International.40:161~166
Villette S, Piron E, Cointault F, et al. 2008. Centrifugal spreading of fertilizer: Deducing three-dimensional velocities from horizontal outlet angles using computer vision [J]. Biosystems Engineering.99:496~507
Visen.N.S, Shashidhar N.S, Paliwal J, et al. 2001. Identification and segmentation of occluding groups of grain kernels in a grains sample image [J].Agric. Engineering Res.79 (2):159~166
Wang H.H, Sun D.W. 2004.Evaluation of the oiling off property of cheese with computer visiorrelation with fat ring test [J].Journal of Food Engineering.61: 47~55
Xiao J, Shah M. 2004. Motion layer extraction in the presence of occlusion using graph cut [J].In CVPR.11: 972~979
Zang D, Sommer G. 2007. Signal modeling for two dimensional image structures [J]. J. Vis. Commun. Image R. 18:81~99
Zayas I, Pomeranz Y, Lai E.S. 1989.Discrimination of wheat and nonwheat components in grain samples by image analysis [J]. Cereal Chemistry.66 (3):233~237
Zayas I, Ponreranz Y, Lai F. 1986 .Discrimination between wheat classes and varieties by image analysis [J].Cereal chemistry,63(l):52~56
Zayas I, Converse H, Steele J.L. 1990.Discrimination of whole from broken corn kernels with image analysis [J].Transactions of the ASAE.33 (5):1642~1646
Zayas I, Martin C.R, Steele J.L,et al. 1996. Wheat classification using image analysis and crush force parameters [J]. Transactions of the ASAE.39 (6):2199~2204
Zheng C.X, Sun D.W, Zheng L.Y. 2007. Predicting shrinkage of ellipsoid beef joints as affected by water immersion cooking using image analysis and neural network [J].Journal of Food Engineering.79:1243~1249
Zion B, Alchanatis V, Ostrovsky V, et al. 2007.Real-time underwater sorting of edible fish species [J]. Computers and Electronics in Agriculture.56:34~45
柴玉华,高立群,王蓉等. 2006.基于多尺度形态学大豆图像滤波方法[J].农业工程学报.22(6):112~119
陈庆园,陈雪,袁有波. 2008.初烤烟叶外观质量与主要化学成分关系的研究[J].中国烟草科学.29(1):30~32
陈国庆,姜东,朱艳等. 2004.利用计算机视觉识别小麦叶色的光照模型研究[J].农业工程学报. 20(4):143~145
成芳. 2004.稻种质量的机器视觉无损检测研究[D].浙江大学
丁浩,艾矫燕. 2007.基于计算机视觉的植物黑腐病病斑分析.计算技术与自动化[J].26(1):115~118
范彦英,赵继文,郭凤英. 2003.影响大豆种子外观品质的因素及对策[J].种子.3:58~59
方如明. 1992.计算机图像处理与米的品质检测[J].农业工程学报.8(3):104~112
冯斌,汪懋华. 2002.计算机视觉系统中图像外边缘检测的新方法[J].中国农业大学学报.7(2):72~75
冯斌. 2002.计算机视觉信息处理方法与水果分级检测技术研究[D].中国农业大学
冯丽娟. 2007.不同基因型与环境因素对高油大豆品质性状的影响[D].黑龙江八一农垦大学
傅艳华,关景贵. 1989.大豆子粒几个外观品质性状与其蛋白质含量相关的探讨[J].吉林农业科学. 46~49
高艳霞,李禹生. 2007.基于粒子群算法和神经网络的大豆识别研究[J].中国粮油学报. 22(4)121~124
谷彩连,孙国凯,王立地. 2006.基于MATLAB和BP神经网络的角点检测方法研究[J].基于MATLAB和BP神经网络的角点检测方法研究.19(2):22~26
谷口庆治. 2002.数字图像处理[M].北京:科学出版社
海鹰. 2007.基于分水岭算法的彩色细胞图像分割研究[D].内蒙古师范大学
贺秋伟. 2007.基于计算机视觉的微小尺寸精密检测理论与技术研究[D].吉林大学
胡国华. 2004.大豆品质生态特性及其遗传表达特性的研究[D].东北农业大学
黄星奕. 1999.基于计算机视觉的稻米外观质量检测系统研究[D].江苏理工大学
黄玉,王远弟. 2006.基于灰度值数学形态学算子的中值曲率驱动方程去噪模型[J].上海大学学报(自然科学版).12(5):488~499
纪延俊,杜玉杰. 2007.黏连气泡图像的分割[J].滨州学院学报.23(6):30~33
贾迪野. 2005.基于偏微分方程的图像平滑与分割研究[D].哈尔滨工程大学.
金剑,王光华,刘晓. 2007.50年黑龙江省大豆遗传改良的产量及品质变化[J].农业现代化研究.28(6):757~759
孔明. 2005.颗粒粒径和形态计算机视觉测量方法研究[D].东南大学
黎静,薛龙,刘木华等.2006.基于计算机视觉的脐橙分级系统研究[J].江西农业大学学报. 28(2):304~307
李海燕,刘惕若,甄鸿杰. 2005.灰斑病所致大豆品质与产量损失的研究[J].农业现代化研究.27(3):66~69
李姣. 2006.茶叶品质的计算机视觉分级技术研究[D].西北农林科技大学
梁琴. 2006.常用图像文件颜色特征提取的研究[D].辽宁工程科技大学
林家春,李伟,荀一. 2005.多目标动态检测中摄像机标定技术研究[J].农业工程学报.21(6):18~21
刘奇,谢甫绨,谢志涛. 2007.不同来源大豆品种籽粒品质的比较研究[J].大豆科学.26(2):154~157
刘少伯,任振森. 1993.试论我国动物蛋白饲料源的开发[J].中国畜牧杂志.29(3):13~15.
刘顺湖,周瑞宝,盖钧镒. 2004.大豆蛋白质及其组分含量对豆腐产量和品质的影响[J].中国油脂.32(2):65~68
刘新利,王厚大. 2006.球形黏连图像分割算法的研究[J].球形黏连图像分割算法的研究.32(7):45~46
刘玉兰,汪学德. 2007.对品质受损大豆加工产品质量的研究及评价[J].中国粮油学报.22(6):50~54
刘兆艳. 2006.基于机器视觉的稻种品种识别研究[D].浙江大学
罗敏,朱晓岷,李小红等. 2008.基于径向小波变换的图像特征提取算法[J].武汉大学学报.33(1):29~31
宁海龙,李文霞,潘相文等. 2002.大豆氨基酸组分影响蛋白质含量的通径分析[J].大豆科学.21(4):259~262
宋韬,曾德超. 1996.作物籽粒图象识别的光照条件研究[J].农业机械学报.27(2):39~42.
孙树亮. 2006.基于计算机视觉的芒果分类[D].广西大学
唐传核. 2004.植物功能性食品[M].北京:化学工业出版社
王继峰,赵小铭,宋玉发等. 2006.浅谈大豆外观品质对化学品质的影响[J].黑龙江农业科学.(5):23~24
王家文,曹宇. 2004.MATLAB6.5图形图像处理[M].北京:国防工业出版社.
王茹芳,卢思慧,曹金锋等. 2007.夏大豆品种农艺性状及子粒品质分析[J].作物杂志.1:25~27
魏海坤. 2005.神经网络结构设计的理论与方法[M].北京:国防工业出版社.
魏瑾,韩斌,张其亮. 2006.黏连蚕卵图像分离算法研究[J].江苏科技大学学报(自然科学版).20(1):46~50
吴春林. 2006.基于PDE的隐式曲面上的图像处理[D].中国科学技术大学.
吴雪梅,程尧,韦龙琴. 2005.在MATLAB环境中基于计算机视觉的番茄识别研究[J]. 31(4):15~17
吴雪梅,毛罕平. 2004.计算机视觉描述缺素番茄叶片颜色变化的研究[J].5:212~214
辛惠普,台莲梅,范文艳等. 2003.大豆病虫害防治彩色图谱[M]北京:中国农业出版社
徐红华,申德超. 2007.改进的挤压膨化浸出工艺对大豆油脂及粕蛋白品质的改善[J].农业工程学报.23(4):249~252
徐建瑜,姜雄晖,刘鹰. 2006.基于计算机视觉的鱼体色明暗程度量化方法[J].农机化研究.6:140~152
徐立新,梁娟,查金水等. 2007.基于颜色常量的图像特征提取及其降维方法[J].计算机工程与设.28(3):602~604
徐全生,李美怡. 2007.人脸图像特征点的定位与提取方法的研究[J].沈阳工业大学学报.29(1):90~94
许俐. 1997.机器视觉测量大米留胚率的研究[J].江苏理工大学学报.18(6):5~11
荀一,蔡卫国,李伟. 2006.谷物种子精选自动化系统研究[J].高技术通讯.16(3):167~170
闫之烨. 2003.基于计算机视觉的苹果颜色分级系统的研究[D].南京农业大学
阎少宏. 2005.离散曲面上曲率的刻划[D].大连理工大学
杨庆凯. 2000.论大豆蛋白质与油分含量品质的变化及影响的因素[J].大豆科学.19(4): 386~391
杨淑莹. 2008.模式识别与智能计算MATLAB技术实现[M].北京:电子工业出版社
杨述平. 2005.基于计算机视觉的三维测试技术研究[D].中北大学
叶志刚. 2001.基于颜色特征的图像检索匹配算法的研究及其系统的开发[D].中国农业大学
阴法明. 2006.一种黏连颗粒图像的快速分割方法[J].微处理机.4:80~81
袁金丽. 2005.计算机视觉在苹果外部品质检测与分级方面的应用研究[D].中国农业大学
岳爱琴,杜维俊,赵晋忠等. 2005.不同大豆品种品质分析[J].华北农学报. 20(2):30~32
曾绍校,张怡,梁静. 2007.中国22个莲子品种外观品质和淀粉品质的研究[J].中国食品学.7(1):74~78
张广军. 2005.机器视觉[M].北京:科学出版社
张立华. 2006.基于计算机视觉的芒果表面缺陷检测方法研究[D].广西大学
张丽莉,姚勇,刘志镜. 2008.一种相对势能分水岭算法[J].西安电子科技大学学报. 35(1):121~125
张平,檀结庆,何蕾. 2007.基于曲率和中值滤波的快速推移图像修复方法[J].计算机应用研究.24(8):186~192
章海亮,左雪平. 2006.苹果自动分级中计算机视觉信息处理技术的研究[J].农机化研究.1:28~30
章文英,应义斌. 2005.苹果着色面积的计算机视觉研究[J].农机化研究.4:90~92
钟金传. 2005.中国大豆产业国际竞争力研究[D].中国农业大学
周佳璐. 2006.基于机器视觉的小麦质量判别系统的研究[D].同济大学
朱圣盼. 2007.基于计算机视觉技术的植物病害检测方法的研究[D].浙江大学
Adelson E, Movshon J. 1982. Phonemenal coherence of moving plaid pattems [J].Nature.300(5892):523~525
Aggarwal J, Cai Q. 1999. Human motion analysis: A Review [J] .Computer vision and image understanding.73 (3):428~440
Amari S. 2001.Information geometry on hierarchy of probability distributions[J].IEEE Transaction on information theory.47(5):1701~1711
Amira. 2004.Visualization and Modeling System [OL].http://www.AmiraVis.com.
Atanasov I.S, Durrell J.H, Vulkova L.A, et al. 2006.Statistical characterization of surface morphologies[J]. Physica.371:361~367
Ballester C, Caselles V, Verdera J. 2003.Disocclusion by joint interpolation of vector fields and Gray [J]. Multistage modeling and simulation.2:80~123
Barrett T. M, Zuuring H. R, Christopher T. 2007.Interpretation of forest characteristics from computer-generated images [J]. Landscape and Urban Planning.80 :396~403
Beaulieu C. 2002.The basis of anisotropic water diffusion in the nervous Systema technical review [J].NMR Biomed.15:435~455
Blasco J, Aleixos N, MoltóE, et al. 2007.Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm [J].Journal of Food Engineering. 81:535~543
Cagnoni S, Evelyne, Lutton. 2006.Introduction to the special issue on evolutionary computer vision and image understanding [J]. Pattern Recognition Letters.27:1161~1163
Cardarelli A.J, Tao Y, Berhardt J.L, et al. 1998. High resolution machine vision for non-destructive Internal inspection of damaged rice grain. In: 1999 ASAE Annual international Meeting.No. 986024
Cheng J, Yang J, Zhou Y. 2006.Flexible background mixture models for foreground segmentation. Image and Vision Computing.24:473~482
Churchill D. B, Bilsland D. M, Cooper T. M. 1992.Comparison of Machine Vision with Human Measurement of Seed Dimensions [J].Transactions of the ASAE.35(1):61~64
Dana W, Ivo W. 2008.Computer image analysis of seed shape and seed color for ?ax cultivar description [J]. Computers and electronics in agriculture.61:126~135
Du C.J, Sun D.W. 2005.Pizza sauce spread classification using colour vision and support vector machines [J]. Journal of Food Engineering.66:137~145
Ercana M.F, Fung Y.F. 2000.The design and evaluation of a multiprocessor system for computer vision [J]. Microprocessors and Microsystems.24:365~377
Ercana M.F, Fung Y.F. 2000.The design and evaluation of a multiprocessor system for computer vision [J].Microprocessors and Microsystems.24:365~377
Ge G.K, Zheng X, Wu J.G, et al.2007.Analysis of the conditional correlations from different genetic systems between the protein content and the appearance quality traits of indica rice [J].Journal of Genetics and Genomics.34(2):129~137
Gómez S. J, MoltóE, Camps-Valls G, et al. 2008.Automatic correction of the effects of thelight source on spherical objects.An application to the analysis of hyperspectral images of citrus fruits [J].Journal of Food Engineering,85:191~200
Gouton P. F, Devaux J.C, Truchete F.T. 2004.New methods for analyzing colour texture based on the Karhunen–Loeve transform and quantification [J].Pattern Recognition. 37:1661~1674
Hagan M. T, Demuth H. B, Mark H.Beale. 2005.神经网络设计[M].北京:机械工业出版社
Hepworth N.J, Hammond J.R.M, Varley J. 2004. Novel application of computer vision to determine bubble size distributions in beer [J].Journal of Food Engineering.61:119~124
K?l?c K, Hakkiì. B, K?lsel H, et al. 2007.A classification system for beans using computer vision system and artificial neural networks [J].Journal of Food Engineering. 78:897~904
Leonardis A, Bischof H. 2003.Kernel and subspace methods for computer vision [J].Pattern Recognition.36:1925~1927
Liao,K, paulsen M.R, Reid J.F, et al. 1993.Corn kernel break age classification by machine vision using a neural network classifier.Trans of the ASAE,36(6):1949~1954
Luo X, Jayas D.S, Symons S.J.1997.comparison of statistical and neural network methods for classifying cereal grains using machine vision[C].Transactions of the ASAE,No.973106
Manickavasagan A, G.Sathya, Jayas D.S. 2008.Comparison of illuminations to identify wheat classes using monochrome images [J]. Computers and Electronics in Agriculture.3(2):237-244
Munkevik P, Hall G, Duckett T. 2007. A computer vision system for appearance-based descriptive sensory evaluation of meals [J].Journal of Food Engineering.78:246~256
Nair M, Jayas D.S. 1998.Dockage identification in wheat using machine vision [J].Canadian Agricultural Engineering.40 (4):293~298
Paliwal J, Shashidhar N.S, Jayas D.S. 1999. Grain kernel identification using kernel signature.Trans of the ASAE.42 (6):1921-1924
Paragios N, Chen Y.M, Faugeras O. 2006. Handbook of mathematical models in computer vision [M].Springer science & business media
Pedreschi F, León J, Mery D, et al. 2006. Development of a computer vision system to measure the color of potato chips [J].Food research international.39:1092~1098
Perrier E, Ana M, Tarquis, Dathe A. 2006.A program for fractal and multifractal analysis of two-dimensional binary images: Computer algorithms versus mathematical theory [J].Geoderma. 134:284~294
Purcara M, Dorochenkoa A, Bortelsa L. 2008. Advanced CAD integrated approach for 3D electrochemical machining simulations [J].Journal of materials processing technology.203:58~71.
Quevedo R, Mendoza F, Aguilera J.M. 2006.Determination of senescent spotting in banana (Musa cavendish) using fractal texture Fourier image [J].Journal of Food Engineering. 2008, 84:509~515
Reid J.F, Kim C, Paulsen M.R. 1991.Computer vision sensing of stress cracks in cornkernels [J]. Transactions of the ASAE,34(5):2236~2244
Schaeffer G. W, Sharpe F.T, Sicher R.C. 1997.Fructose 1,6-bisphosphate aldolase activity in leaves of a rice mutant selected for enhanced lysine[J].Phytochemistry. 6(8):1335~1338
Shashidhar N.S, Jayas D.S, Crowe T.G, et al.1997. Processing of digital images of touching kernels by ellipse fitting [J].Canadian Agricultural Engineering.39 (2):139~142
Shatadal,P, Jayas D.S, Bulley N.R. 1995.Digital image analysis for software separation and classification of touching grains: 2 Classification.[J]. Transactions of the ASAE,38(2):645~649
?kaloudováa B, K?ivanb V, Zemek R. 2006. Computer-assisted estimation of leaf damage caused by spider mites [J].Computers and Electronics in Agriculture.53:81~91
Steenhoek L, Misra M, Batchelor W, et al. 1999. Probabilistic neural networks for segmentation of features in corn kernel images[C]. In: 1999 ASAE Annual international Meeting. No.993198
Stewar C.T, Tsai C.L, Roysam B. 2003. The dual-bootstrap iterative closest point algorithm with application to retinal image registration [J].IEEE Transactions on medical imaging.22:1379~1394
Stewart C. 1999. Robust parameter estimation in computer nision [J].SIAM Reviews.41: 513~537
Strecha C, Fransens R, Van G.L. 2004. Wide base line stereo from multiple views: A probabilistic account [J].In IEEE international conference on computer vision and pattern recognition.2: 552~559 Strecha C, Tuytelaars T, Van G.L. 2003.Dense Matching of multiple wide-baseline views [J].In IEEE International Conference on Computer Vision.1194~1201
Sun D.W.2002.Computer vision–an objective,rapid and non-contact quality evaluation tool for the food industry [J].Journal of Food Engineering.61:1~2
Sun Y.R, Fisher R. 2003. Object based visual attention for computer vision [J].Artificial Intelligence.146: 77~123.
Szummer M, Picard R. 1996. Temporal texture modeling [J].In IEEE International Conference on Image Processing.3:823~826
Tagare H, O'shea D, Groisser D. 2002. Non-rigid shape comparison of plane curves in Images [J] Math. Imaging Vis. 16(l):57~68
Tahir A.R, Neethirajan S, Jayas D.S, et al. 2007.Evaluation of the effect of moisture content on cereal grains by digital image analysis [J]. Food Research International, 40(9): 1140~1145
Tan J.L. 2004. Meat quality evaluation by computer vision[J].Journal of Food Engineering. 61:27~35
Tan P, Lin S, Quan L, et al. 2003. Highlight removal by illumination~constrained in painting [J].In Proceedings International Conference on Computer Vision.164~169
Tang B, Sapiro G, Caselles V. 2001. Color image enhancement via chromaticity diffusion[J] .IEEE Transactions on Image Processing.10:701~707
Tschumperle D , Deriche R. 2001. Diffusion tensor regularization with constraints preservation [J] .In CVPR.948~954
Venora G, Grillo O, Shahin M.A, Symons S.J. 2007.Identification of sicilian landraces and canadian cultivars of lentil using an image analysis system [J].Food Research International.40:161~166
Villette S, Piron E, Cointault F, et al. 2008. Centrifugal spreading of fertilizer: Deducing three-dimensional velocities from horizontal outlet angles using computer vision [J]. Biosystems Engineering.99:496~507
Visen.N.S, Shashidhar N.S, Paliwal J, et al. 2001. Identification and segmentation of occluding groups of grain kernels in a grains sample image [J].Agric. Engineering Res.79 (2):159~166
Wang H.H, Sun D.W. 2004.Evaluation of the oiling off property of cheese with computer visiorrelation with fat ring test [J].Journal of Food Engineering.61: 47~55
Xiao J, Shah M. 2004. Motion layer extraction in the presence of occlusion using graph cut [J].In CVPR.11: 972~979
Zang D, Sommer G. 2007. Signal modeling for two dimensional image structures [J]. J. Vis. Commun. Image R. 18:81~99
Zayas I, Pomeranz Y, Lai E.S. 1989.Discrimination of wheat and nonwheat components in grain samples by image analysis [J]. Cereal Chemistry.66 (3):233~237
Zayas I, Ponreranz Y, Lai F. 1986 .Discrimination between wheat classes and varieties by image analysis [J].Cereal chemistry,63(l):52~56
Zayas I, Converse H, Steele J.L. 1990.Discrimination of whole from broken corn kernels with image analysis [J].Transactions of the ASAE.33 (5):1642~1646
Zayas I, Martin C.R, Steele J.L,et al. 1996. Wheat classification using image analysis and crush force parameters [J]. Transactions of the ASAE.39 (6):2199~2204
Zheng C.X, Sun D.W, Zheng L.Y. 2007. Predicting shrinkage of ellipsoid beef joints as affected by water immersion cooking using image analysis and neural network [J].Journal of Food Engineering.79:1243~1249
Zion B, Alchanatis V, Ostrovsky V, et al. 2007.Real-time underwater sorting of edible fish species [J]. Computers and Electronics in Agriculture.56:34~45