基于光技术的皮蛋蛋壳破损检测方法研究
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摘要
皮蛋是我国独创的一种蛋类加工产品,其营养丰富、容易消化、具有清凉解热等作用,深受人们喜爱。目前在生产加工中主要采用眼看、手掂等人工方法来检测并剔除破损皮蛋,本文结合偏振光技术、图像处理技术及模式识别技术,研究出缸皮蛋蛋壳裂纹的自动识别方法。主要研究内容如下：
(1)搭建了自然光图像采集系统,对图像的噪声滤除、图像增强、边缘检测、形态学运算等进行了深入分析和比较,研究出适合提取皮蛋蛋壳裂纹的图像处理算法。通过大量试验验证,提出了形态学边缘提取算法,试验结果表明：形态学边缘提取算法能有效区分皮蛋蛋壳信息和裂纹信息。
(2)在形态学边缘提取算法处理蛋壳图像的基础上,分别提取了图像中裂纹信息的形状特征参数和纹理特征参数,具体为：裂纹长度L、裂纹深度K、投影变换后水平方向和垂直方向的灰度最大值Tmax、角点数等四个特征参数。采用支持向量机分类器进行自动识别,分别采用线性、多项式、径向基、Sigmoid等四种SVM核函数分别对样本进行分类处理,根据不同的训练样本和测试样本数,综合得出径向基核函数SVM分类器的识别率最高,检测率最高可达94%。
(3)针对皮蛋蛋壳表面大块黑斑和裂纹的偏振度的不同,研制了偏振图像采集系统,根据本课题研究的需要选用了Stokes矢量法描述偏振光,根据Stokes矢量描述法中所需测量的偏振角为0°、45°、90°、-45°的偏振图像,搭建了偏振图像采集系统,详细介绍了偏振图像采集系统的硬件设计和软件设计,其中包括软件界面的设计、界面可直接控制CCD的各个参数设计、采集图像、控制电机自动旋转所需角度、自动获取所需要的各角度偏振图像等多种操作,整个图像采集系统能完成控制电机旋转一周,能自动采集相应的偏振图像。
(4)利用偏振图像采集系统采集样本偏振角为0°、45°、90°、-45°的偏振图像,拟合各样本的偏振图像,通过试验数据表明,皮蛋蛋壳表面大块黑斑的偏振度相对稳定,大多在0.1左右,而裂纹的偏振度不太稳定,但大多数情况下一般大于表壳黑斑的偏振度,本文先采用阈值方法滤除样本表壳黑斑信息,然后以偏振图像中像素最高且连通区域最大部分作为中心,截取100*100的像素图像,提取该图像的裂纹长度、均方比、偏度和峰度等四个特征参数,采用Kmeans聚类分析算法识别蛋壳裂纹。通过实验证明,该方法效果良好,其中好壳皮蛋的识别准确率为100%、裂纹蛋的识别准确率为88.3%,综合准确率为93%。
(5)对同一批100枚皮蛋样本分别用自然光图像处理方法和偏振光技术进行样本裂纹识别,其检测结果均为91%,但自然光图像处理方法中好壳蛋误判率较高,而偏振光技术裂纹蛋的误判率较高,最后将所有样本先经过自然光检测,再经过偏振光技术识别,其检测率高达96%,说明多特征融合算法提高了检测精度。
本文结果表明该方法能有效地识别皮蛋蛋壳的裂纹,具有较高的应用价值,可作为工业自动检测装备研制的重要理论依据。
Pidan or century egg also known as preserved egg is one of the most traditional and popular preserved egg products in China. Pidan is widely adopted into the local cuisine as an appetizer, its nutritional value and a pleasant fragrant taste that is preferred by most people in Southeast Asian countries. The traditional detecting crack of pickled egg was based on manual work in many factories. On the basis of analyzing relevant researches, This dissertation presented a method of detecting preserved eggshell cracks automatically based on image processing techniques and polarized optical technology in the experiment. The main contents are as follows:
(1) Set-up image acquisition system on ordinary source, a series of image processing foundational algorithms, such as image denoising, image inhancenment, edge detection operators and morphology calculus, were analyzed in this work. It is configured in these ways by selecting different image processing algorithms in each stage. According to experiments and desired results obtained from this experiment, it was found that morphology calculus algorithms was the most effective way to detect cracks on preserved eggshell.
(2) Some shape feature parameters and texture parameters were extracted after the image processing with morphology calculus algorithms. The four characteristic parameters were the crack length L, the crack degree coefficient K, the maximum between horizontal and vertical gray Tmax, the angler points. The inact and cracked preserved eggs was classified with SVM model. A comparison study of the classification capacibility of different kernel function of SVM model, training sample size and test smple size, it was found that the SVM model with RBF kernel function procided the best classification performance and the classification accuracy was up to94%.
(3) For the different polarization degree of black spots and cracks on preserved eggshell, a polarization optical system was designed in this work. It accepted the Stokes Vector method to describe polarization image. Using the polarization optical system acquired different polarization angles images, including degree of zero, degree of forty five, degree of negative forty five and degree of nintey. It detailed introduced the hardware and software design of the polarization optical system. The image of different polarization angles was acquired by analyzer rotating automatically, while the analyzer was controlled by stepper motor. Every angle of analyzer rotating was obtained automatically by computer controlling the stepper motor using DLL (dynamic link library) and instructions.
(4) According to different depolarization mechanism for black spots and cracks on preserved eggshell, it can distinguish black spots and cracks on polarization image. The polarization image after pretreated processing, it took the most connected area of high gray value as center to cut an image about100*100pixel area. Four characteristic parameters was extracted to distinguish the cracks on preserved eggshell, including the length of the crack as L, mean variance ratio as R, skewness as α and kurtosis as β. We put forward four characteristic parameters and use cluster analysis to detect cracks. This paper employed Info-Kmeans clustering algorithm, and the clustering of high-dimensional sparse data extract from images. The result showed that all preserved eggs were classified into intact and cracked groups with the Info-Kmeans clustering algorithm, and the accuracy was up to93%.
(5) Comparing two different identification methods on the same samples, a fusion based on multi-recognition technology and multi-feature method was proposed. The results showed that the total classification accuracy rate was up to96%.
It showed that image processing techniques and polarized optical technology distinguished intact and cracked preserved eggs efficiently, and these results could provide a theoretical basis for detecting cracks of preserved egg on product lines.
(1)搭建了自然光图像采集系统,对图像的噪声滤除、图像增强、边缘检测、形态学运算等进行了深入分析和比较,研究出适合提取皮蛋蛋壳裂纹的图像处理算法。通过大量试验验证,提出了形态学边缘提取算法,试验结果表明：形态学边缘提取算法能有效区分皮蛋蛋壳信息和裂纹信息。
(2)在形态学边缘提取算法处理蛋壳图像的基础上,分别提取了图像中裂纹信息的形状特征参数和纹理特征参数,具体为：裂纹长度L、裂纹深度K、投影变换后水平方向和垂直方向的灰度最大值Tmax、角点数等四个特征参数。采用支持向量机分类器进行自动识别,分别采用线性、多项式、径向基、Sigmoid等四种SVM核函数分别对样本进行分类处理,根据不同的训练样本和测试样本数,综合得出径向基核函数SVM分类器的识别率最高,检测率最高可达94%。
(3)针对皮蛋蛋壳表面大块黑斑和裂纹的偏振度的不同,研制了偏振图像采集系统,根据本课题研究的需要选用了Stokes矢量法描述偏振光,根据Stokes矢量描述法中所需测量的偏振角为0°、45°、90°、-45°的偏振图像,搭建了偏振图像采集系统,详细介绍了偏振图像采集系统的硬件设计和软件设计,其中包括软件界面的设计、界面可直接控制CCD的各个参数设计、采集图像、控制电机自动旋转所需角度、自动获取所需要的各角度偏振图像等多种操作,整个图像采集系统能完成控制电机旋转一周,能自动采集相应的偏振图像。
(4)利用偏振图像采集系统采集样本偏振角为0°、45°、90°、-45°的偏振图像,拟合各样本的偏振图像,通过试验数据表明,皮蛋蛋壳表面大块黑斑的偏振度相对稳定,大多在0.1左右,而裂纹的偏振度不太稳定,但大多数情况下一般大于表壳黑斑的偏振度,本文先采用阈值方法滤除样本表壳黑斑信息,然后以偏振图像中像素最高且连通区域最大部分作为中心,截取100*100的像素图像,提取该图像的裂纹长度、均方比、偏度和峰度等四个特征参数,采用Kmeans聚类分析算法识别蛋壳裂纹。通过实验证明,该方法效果良好,其中好壳皮蛋的识别准确率为100%、裂纹蛋的识别准确率为88.3%,综合准确率为93%。
(5)对同一批100枚皮蛋样本分别用自然光图像处理方法和偏振光技术进行样本裂纹识别,其检测结果均为91%,但自然光图像处理方法中好壳蛋误判率较高,而偏振光技术裂纹蛋的误判率较高,最后将所有样本先经过自然光检测,再经过偏振光技术识别,其检测率高达96%,说明多特征融合算法提高了检测精度。
本文结果表明该方法能有效地识别皮蛋蛋壳的裂纹,具有较高的应用价值,可作为工业自动检测装备研制的重要理论依据。
Pidan or century egg also known as preserved egg is one of the most traditional and popular preserved egg products in China. Pidan is widely adopted into the local cuisine as an appetizer, its nutritional value and a pleasant fragrant taste that is preferred by most people in Southeast Asian countries. The traditional detecting crack of pickled egg was based on manual work in many factories. On the basis of analyzing relevant researches, This dissertation presented a method of detecting preserved eggshell cracks automatically based on image processing techniques and polarized optical technology in the experiment. The main contents are as follows:
(1) Set-up image acquisition system on ordinary source, a series of image processing foundational algorithms, such as image denoising, image inhancenment, edge detection operators and morphology calculus, were analyzed in this work. It is configured in these ways by selecting different image processing algorithms in each stage. According to experiments and desired results obtained from this experiment, it was found that morphology calculus algorithms was the most effective way to detect cracks on preserved eggshell.
(2) Some shape feature parameters and texture parameters were extracted after the image processing with morphology calculus algorithms. The four characteristic parameters were the crack length L, the crack degree coefficient K, the maximum between horizontal and vertical gray Tmax, the angler points. The inact and cracked preserved eggs was classified with SVM model. A comparison study of the classification capacibility of different kernel function of SVM model, training sample size and test smple size, it was found that the SVM model with RBF kernel function procided the best classification performance and the classification accuracy was up to94%.
(3) For the different polarization degree of black spots and cracks on preserved eggshell, a polarization optical system was designed in this work. It accepted the Stokes Vector method to describe polarization image. Using the polarization optical system acquired different polarization angles images, including degree of zero, degree of forty five, degree of negative forty five and degree of nintey. It detailed introduced the hardware and software design of the polarization optical system. The image of different polarization angles was acquired by analyzer rotating automatically, while the analyzer was controlled by stepper motor. Every angle of analyzer rotating was obtained automatically by computer controlling the stepper motor using DLL (dynamic link library) and instructions.
(4) According to different depolarization mechanism for black spots and cracks on preserved eggshell, it can distinguish black spots and cracks on polarization image. The polarization image after pretreated processing, it took the most connected area of high gray value as center to cut an image about100*100pixel area. Four characteristic parameters was extracted to distinguish the cracks on preserved eggshell, including the length of the crack as L, mean variance ratio as R, skewness as α and kurtosis as β. We put forward four characteristic parameters and use cluster analysis to detect cracks. This paper employed Info-Kmeans clustering algorithm, and the clustering of high-dimensional sparse data extract from images. The result showed that all preserved eggs were classified into intact and cracked groups with the Info-Kmeans clustering algorithm, and the accuracy was up to93%.
(5) Comparing two different identification methods on the same samples, a fusion based on multi-recognition technology and multi-feature method was proposed. The results showed that the total classification accuracy rate was up to96%.
It showed that image processing techniques and polarized optical technology distinguished intact and cracked preserved eggs efficiently, and these results could provide a theoretical basis for detecting cracks of preserved egg on product lines.
引文
1.包玉龙,张继全,刘晓静,王永芳,马东来,孙仲秋.受风灾影响后的农作物冠层反射信息测量与分析[J].光谱学与光谱分析,2013,33(4)：1057-1060.
2.宾冬梅,马美湖,易诚.清料法加工皮蛋工艺改进与中试[J].中国食品工业,2006(8)：50-52.
3.曹念文,刘文清,张玉钧,王峰平,宋炳超,徐克军.水下目标圆偏振成像及最远成像距离的计算[J].中国激光,2000,27(2)：150-154.
4.陈家焱.鸭蛋破损声检与分级技术的研究[D].华中农业大学,2004.
5.陈坤杰,夏建春.光谱分析检测大米加工精度等级[J].农业机械学报,2008,1：187-190.
6.陈英,廖涛,林初靠.基于计算机视觉的葡萄检测分级系统[J].农业机械学报,2010,3：169-172.
7.陈香维,杨公明.测试部位对猕猴桃近红外光谱响应特性的影响[J].农业机械学报,2009.1：124-128.
8.郭峰,曹其新,崔永杰.用于草莓收获机器人的果实定位和果柄检测方法[J].农业工程学报,2008,10：89-94.
9.何东健,乔永亮,李攀,高瞻,李海洋,唐晶磊.基于SVM-DS多特征融合的杂草识别[J].农业机械学报,2013.
10.何丽红,刘金刚,文友先.基于粗糙集与支持向量机的禽蛋蛋壳无损检测[J].农业机械学报,2009,(3)：167-171.
11.侯大军,李洪军.碱处理对风味皮蛋物理品质的影响[J].食品工业科技,2008,27(5)：236-241.
12.胡道栋.基于DSP的皮蛋破损检测技术研究[D].[硕士学位论文]华中农业大学,2011.
13.简小华,张淳民,赵葆常,张霖,朱兰艳.利用偏振干涉成像光谱仪进行偏振检测的最佳角度分析[J].物理学报,2009,58(4)：2286-2293.
14.乐立强.皮蛋表面斑点成分分析与控制方法的研究[D].[硕士学位论文]华中农业大学,2011.
15.李建平,魏宝刚.松子的声学特性及分形判别[J].农业工程学报,2006年,3：15-18.
16.李军鹏,候畅,熊善柏等.腌制条件对皮蛋品质的影响[J].食品研究与开发,2009,30(8)：101-108.
17.李伟于振东陈涛.基于形态学的棉花种子破损检测[J].农业机械学报,2009,4：169-172.
18.李昕,李立君,高自成,周健,闵淑辉.基于偏好人工免疫网络多特征融合的油茶果图像识别[J].农业工程学报,2012,28(14)：133-137.
19.李锡嘏.锌法皮蛋新工艺料液中NaOH和Zn2+浓度变化与产品质量的关系[J].湖南农学院学报,1993,19(5)：464-469.
20.廖延彪.偏振光学[M]().科学出版社,北京,2003.
21.刘风娟,潘磊庆,屠康.新型中草药松花蛋的研制[J].食品工业科技,2009,30(05)：221-223.
22.刘良忠.以锌盐为主体盐分加工皮蛋的研究[J].食品发酵工业,2003,29(7)：93-94.
23.刘龙,付美章,王树才.基于小波能量谱和BP神经网络的皮蛋破损检测[J].华中农业大学学报,2012,31(4)：524-527.
24.罗赋毅,吕伟,郑丽玉,龚慧然,林碧芬,王庆新.皮蛋腌渍过程内容物PH值影响因素的研究[J].中国食品学报,2010,10(4)：240-244.
25.吕云峰.基于反射特性的玉米苗期光谱与农药污染程度关系研究[J].东北师大学报,2012,44(2)：127-131.
26.马美湖.我国蛋品工业科技的几大热点(上)[J],肉类研究.2000(3)：23-7.
27.马美湖.我国蛋与蛋制品加工重大关键技术筛选研究报告[J].中国家禽,2004,26(24)：1-12.
28.马美湖.蛋与蛋制品加工学[M].北京：中国农业出版社,2005.
29.马兰,夏俊芳,张战锋.基于小波变换的番茄总糖近红外无损检测[J].农业工程学报2009,10：350-354.
30.欧阳玲花,冯建雄,闵华等.皮蛋加工研究现状与展望[J].食品工业技术,2009,30(4)：349-354.
31.潘磊庆,屠康,赵立.潘秀娟;敲击振动检测鸡蛋裂纹的初步研究[J].农业工程学报,2005,04：11-15.
32.彭辉,文友先,王巧华.基于小波变换和BP神经网络的蛋壳破损检测[J].农业机械学报,2009,2(12)：170-174
33.饶秀勤,应义斌,吕飞玲.水果声学特性测试系统的研制[J].农业机械学报,2004,2：69-71.
34.史波林,赵镭,刘文.苹果内部品质近红外光谱检测的异常样本分析[J].农业机械学报,2010,2：132-137.
35.宋开山,张柏,赵云升.东北主要高达落叶乔木单叶的偏振特性研究[J].东北 师大学报自然科学版,2004,36(1)：88-94.
36.孙晓兵,乔延利,洪津,王乐意.人工目标偏振特征实验研究[J].高科技通讯,2003,8.
37.孙静,马美湖,吴玲,乐立强.热处理对皮蛋生产周期和品质的影响[J].农业工程学报,2011,27(5)：367-372.
38.孙静,杜金平,马美湖.间歇式加压技术快速腌制皮蛋工艺研究[J].食品科学,2012,33(06)：1-6.
39.王巧华,邓小炎,文友先.鸡蛋敲击响应的奇异性特征与蛋壳裂纹多层检测[J].农业机械学报,2008,12：127-131.
40.王树才,文友先,刘俭英.基于机器人的禽蛋自动检测与分级系统集成开发[J].农业工程学报,2008,4：186-189.
41.王书茂.西瓜成熟度无损检测的振动冲击方法[J].农业工程学报,1999,15(3)：241-245.
42.王俊,滕斌,周鸣.梨敲击激励后产生的动力学特性研究[J].农业机械学报,2004,2：65-68.
43.王良玉.锌盐和铜盐在皮蛋加工中的应用及对产品品质的影响[J].农产品加工学刊,2005,(11)：76-80.
44.文友先,王巧华,宗望远.鸭蛋破损检测的试验研究[J].华中农业大学学报,2002,6：285-288.
45.吴彦红,刘木华,杨君,郑华东.基于计算机视觉的大米外观品质检测[J].农业机械学报,2007,38(7)：107-111.
46.徐志刚.基于多特征融合的路面破损图像自动识别技术研究[D].[博士学位论文]长安大学,2012
47.阎华,朱端卫.铜、锌、铁在皮蛋加工中作用差异研究{J}.食品科学,2006,27(12)：164-167.
48.杨祯,王树才.淋蜡式皮蛋封蜡机的设计与试验[J].农业工程学报,2011,27(5)：348-352.
49.殷涌光,丁筠.基于计算机视觉的蔬菜中活菌总数快速检测[J].农业工程学报,2009,7：249-254.
50.展慧,李小昱,王为.基于机器视觉的板栗分级检测方法[J].农业工程学报,2010,4：327-331.
51.赵文兵,马万征.利用偏振信息进行作物营养水平检测的初步研究[J].农业装备技术,2011,37(6)
52.赵永强,张洪才,潘泉.基于偏振特征的目标检测[J].量子电子学报,2003,20 (6)：685-688.
53.赵燕,徐明生,涂永刚.皮蛋加工相关机理研究进展[J],食品科学,2010,,31(17)：472-475.
54.赵燕,李鑫,李建科,徐明生,涂勇刚.电感耦合等离子发射光谱法测定铅法皮蛋中的多种无机元素[J].食品科学,2010,31(24)：337-340.
55.章建浩.蛋品包蜡机[J].食品与机械,1992,4(30)：28.
56.张兰威.蛋制品工艺学[M].黑龙江：黑龙江出版社,1996.
57.张所非,陈斌,褚静.基于声学特性的苹果无损检测方法[J].现代仪器,2007,2：11-14.
58.张荞,孙晓兵,洪津.不同湿度的低植被覆盖土壤表面偏振特性研究[J].光谱学与光谱分析,2010,30(11)：3086-3092.
59.张所非,陈斌,褚静.基于声学特性的苹果无损检测方法[J].现代仪器,2007,2：11-14.
60.张焰林,朱敏.基于多特征的指纹图像分割算法[J].计算机系统应用,2008,10：43-46.
61.张志建.新型蛋制品加工工艺与配方[M].科学技术文献出版社,2000.
62.周斌,林喜荣,贾惠波,宋榕.多特征融合的手背血管识别算法[J].清华大学学报(自然科学版),2007,47(2)：194-197.
63.郑晓鸣,吕世颖,王晓东.基于免疫粒子群优化的聚类算法[J].计算机工程,2008,34(15)：179-84.
64. Belie N, Abbort J A,H A Affeldt and L A Liljedahl.Firmness measurement of stored'delicious'apples by sensory methods, Mngness-Taylor, and sonic transmission, J. Amer, Soc, Hort[J]. Sci,1992, (4):590-595.
65. B. De Ketelaere, H. Vanhoutte, Parameter estimation and multivariable model building for the non-destructive, on-line determination of egg shell strength[J]. Journal of Sound and Vibration.2003 (266):699-709.
66. Bickel W. S, Videen G. W. Polarized light scattering applications and measurements of fundamental systems. Proceedings of the International Society for Optical Engineering(SPIE),1994,1746:14-21.
67. Breon F M, Tanre D, Lecomte P, et al. Polarized reflectance of bare soils and vegetation:measurements and models[J]. IEEE Transactions on Geoscience and Remote Sensing,1995,33(2):487-499.
68. Cho H K, Choi W K, Paek J K. Detection of surface in shell eggs by acoustic impulse method [J]. Transaxtions of ASAE,2000,43(6):1921-1926.
69. Chung-wei, Jui-chun Chang, Ching-wei Cheng, Li-cheng Hsieh. A novel non-destructive technology for inspecting eggshell cracks using impulsive response time [J]. Food Sci. Technol. Res.,2011,17(1):1-10.
70. Curran P. The relationship between polarized visible light and vegetation amount[J]. Remote Sensing of Environment,1981,11:87-92.
71. Dror Fixler, Rinat Ankri, Hamootal Duadi, Rachel Lubart, Zeev Zalevsky. Depolarization of light in biological tissues[J]. Optics and Lasers in Engineering,2012,50:850-854.
72. Firooz A. Sadjadi. New experiments in the use of infrared polarization in the detection of small targets[J]. Proceedings of SPIE,2001, 4379:144-155.
73. F. R. Bamelis, B. De Ketelaere, K. Mertens et..Measuring the conductance of eggshells using the acoustic resonance technique and optical transmission spectra[J]. Computers and Electronics in Agriculture,2008, 62(1):35-40.
74. Gong Jieqiong, Zhan Haigang, Liu Dazhao. A review on polarization information in the remote sensing detection. Spectroscopy and Spectral Analysis,2010,30(4):1088-1095.
75. Grosjean T, Conrion D. Polarization filtering induced by imaging system: effect on image structure[J]. Phys Rev E,2003,67:235-241.
76. Jenshin L, Lin Y, Hsieh M. An Automatic System for Eggshell Quality Monitoring [J]. Transactions of the ASAE,2001,44(3):1028-1034.
77. Jessica C. Ramella-Roman, Ken Lee, Scott A. Prahl. Desigh, testing and clinical strdies of a handheld polarized light camera[J]. Journal of Biomedical Oprics,2004,9(6):1305-1310.
78. Kohzo Homma, Hirokimi Shingu, Hiromichi Yamamoto, et al. Application of an imaging spectropolarimeter to agro-environmental sciences[J]. Proceedings of SPIE,2004,5234:639-647.
79. Lawrence, Kurt, Yoon, Seung. Imaging system with chamber modified-pressure for crack detection in shell eggs[J]. Sensing and Instrumentation for Food Quality and Safety,2008,2(2):116-122.
80. Litivinov P, Hasekamp 0, Cairns B, et al. Reflection models for soil and vegetation surfaces from multiple-viewing angle photopolarimetric measurements[J]. Journal of Quantitative Spectroscopy and Radiative Transfer,2010,111:529-539.
81. Maignan F, Breon F M, Fedele E, et al. Polarized reflectances of natural surfaces:spaceborne measurements and analytical modeling[J]. Remote Sensing Environ,2009,113:2642-2650.
82. Matthew H. Smith. Interpreting Mueller matrix images of tissue[J]. Laser-Tissue Interaction Ⅻ, SPIE,2001,4257:82-89.
83. M. barcaglia, P. Ferrazzoli. A fully polarimentic multiple scattering model for agricultural fields[J]. IEEE International,1995,1556-1562.
84. M. C. Garcia-Alegre, A. Ribeiro, D. Guinea and G. Cristobal. Eggshell defects detection based on color processing [C]. SPIE2000 Electronic Imaging Conf. CA, Jan.2000.
85. N.Gupta, L. Denes, M.Gottlieb et al. Spectropolarimetric Imaging Using a Field-Portable Imager[J]. Imaging Spectrometry VISPIE,2000, 4132:372-381.
86. Palanivel Ganasen, Soottawat Benjakul. Physical Properties and Microstructure of Pidan Yolk as Affected by Different Divalent and Monovalent Cations. LWT-Food Science and Technology,2010,43:77-85.
87. Palanivel Ganesan, Soottawat Benjakul. Influence of Different Cations on Chemical Composition and Microstructure of Pidan White and Yolk During Pickling and Aging[J]. International Journal of Food Properties,2010, 13:1150-1160.
88. Peltoniemi J, Hakala T, Suomalainen J, et al. Polarised bidirection factor measurement from soil, stones and snow[J]. Journal of Quantitative Spectroscopy and Radiative Transfer,2009,110:1940-1953.
89. Pelyushenko S. A.. Microwave radiometrer system for the detection of oil slicks [J]. Spill Science & Technology Bulletin,1995,2(4):249-254.
90. P. N. Raven, D.C.Jordan. Polarized directional reflectance from laurel and mullein leaves[J]. Opt. Eng.,2002,41(5):1002-1012.
91. Roy M. Matchko, Grant R. Gerhart, Polarization azimuth angle in daylight scenes, Optical Engineering,2005,44(2):1-9.
92. Scchotte S. Acoustic impulse-response technique for evaluation and modeling of firmness of tomato fruit[J]. Postharvest biology and technology,1999,17(2):105-115.
93. Schechner Y. Y, Shamir Joseph. Polaarization and statistical analysis of scenes containing a semireflector[J]. Opt. Soc. Am. A,2000,17(2):276-284.
94. S. H. Sposato, Fetrow M. P. Two Long-Wave Infrared Spectral Polarimeters for Use in Understanding Polarization Phe-nomenology[J]. Opt. Eng.,2002, 41(5):1055-1064.
95. Snelick R, U ludag U, Mink A, et al. Large scale evaluation of multimodal biometric authentication using state-of-the-art systems[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005, 27(3):450-455.
96. S.L. Jacques, J. C. Ramella-Roman, K. Lee. Imaging Skin Pathology with Polarized Light. J Biomed Optics,2002,7:329-340.
97. Stenven L. Jacques, Jessica R. Roman, Ken Lee. Imaging superficial tissues with polarized light. Laser. Sur. Med,2000,26:119-129.
98. Steven L. Jacques, Ravikant Samatham, Scott Isenhath, Ken Lee. Polarized light camera to guide surgical excision of skin cancers [J]. Proc. of SPIE, 2008,6842:684301-684207.
99. Sugiyamma J, T Katsural, J Hong, et al. Portable melon firmness tester using acoustic implus transmission[C]. Orlando, Florida:Proceedings from the sensors for nondestructive testing international conference,1997.
100. Vanderbil T V C. Plant canopy specular reflectance model[J]. IEEE Transactions on Geoscience and Remote Sensing,1985,23(5):722-730.
101. Youxian Wen, Qiaohua Wang, Wangyuan Zong, Lirong Xiong, Jianying Liu, Yousheng Yu. Study on dilapidation of duck egg by audio signal [J]. Journal of Huazhong Agricultural University,2002,21:285-287.
102. Xiaoyan Deng, Qiaohua Wang, Hong Chen, Hong, Xie. Eggshell crack detection using a wavelet-based support vector machine[J]. Comput. Electron. Agric.,2010,70:135-143.
103. Yao Y, Wang Y. Design of a polarimeter for the infrared camera OASIS. ACTA Astron Sin,1999,40:213-222.
104. Zhao Y, Zhang L, Zhang D, et al. Object separation by polarimetric and spectral imagery fusion [J]. Computer Vision and Image Understanding,2009, 113(8):855-866.
2.宾冬梅,马美湖,易诚.清料法加工皮蛋工艺改进与中试[J].中国食品工业,2006(8)：50-52.
3.曹念文,刘文清,张玉钧,王峰平,宋炳超,徐克军.水下目标圆偏振成像及最远成像距离的计算[J].中国激光,2000,27(2)：150-154.
4.陈家焱.鸭蛋破损声检与分级技术的研究[D].华中农业大学,2004.
5.陈坤杰,夏建春.光谱分析检测大米加工精度等级[J].农业机械学报,2008,1：187-190.
6.陈英,廖涛,林初靠.基于计算机视觉的葡萄检测分级系统[J].农业机械学报,2010,3：169-172.
7.陈香维,杨公明.测试部位对猕猴桃近红外光谱响应特性的影响[J].农业机械学报,2009.1：124-128.
8.郭峰,曹其新,崔永杰.用于草莓收获机器人的果实定位和果柄检测方法[J].农业工程学报,2008,10：89-94.
9.何东健,乔永亮,李攀,高瞻,李海洋,唐晶磊.基于SVM-DS多特征融合的杂草识别[J].农业机械学报,2013.
10.何丽红,刘金刚,文友先.基于粗糙集与支持向量机的禽蛋蛋壳无损检测[J].农业机械学报,2009,(3)：167-171.
11.侯大军,李洪军.碱处理对风味皮蛋物理品质的影响[J].食品工业科技,2008,27(5)：236-241.
12.胡道栋.基于DSP的皮蛋破损检测技术研究[D].[硕士学位论文]华中农业大学,2011.
13.简小华,张淳民,赵葆常,张霖,朱兰艳.利用偏振干涉成像光谱仪进行偏振检测的最佳角度分析[J].物理学报,2009,58(4)：2286-2293.
14.乐立强.皮蛋表面斑点成分分析与控制方法的研究[D].[硕士学位论文]华中农业大学,2011.
15.李建平,魏宝刚.松子的声学特性及分形判别[J].农业工程学报,2006年,3：15-18.
16.李军鹏,候畅,熊善柏等.腌制条件对皮蛋品质的影响[J].食品研究与开发,2009,30(8)：101-108.
17.李伟于振东陈涛.基于形态学的棉花种子破损检测[J].农业机械学报,2009,4：169-172.
18.李昕,李立君,高自成,周健,闵淑辉.基于偏好人工免疫网络多特征融合的油茶果图像识别[J].农业工程学报,2012,28(14)：133-137.
19.李锡嘏.锌法皮蛋新工艺料液中NaOH和Zn2+浓度变化与产品质量的关系[J].湖南农学院学报,1993,19(5)：464-469.
20.廖延彪.偏振光学[M]().科学出版社,北京,2003.
21.刘风娟,潘磊庆,屠康.新型中草药松花蛋的研制[J].食品工业科技,2009,30(05)：221-223.
22.刘良忠.以锌盐为主体盐分加工皮蛋的研究[J].食品发酵工业,2003,29(7)：93-94.
23.刘龙,付美章,王树才.基于小波能量谱和BP神经网络的皮蛋破损检测[J].华中农业大学学报,2012,31(4)：524-527.
24.罗赋毅,吕伟,郑丽玉,龚慧然,林碧芬,王庆新.皮蛋腌渍过程内容物PH值影响因素的研究[J].中国食品学报,2010,10(4)：240-244.
25.吕云峰.基于反射特性的玉米苗期光谱与农药污染程度关系研究[J].东北师大学报,2012,44(2)：127-131.
26.马美湖.我国蛋品工业科技的几大热点(上)[J],肉类研究.2000(3)：23-7.
27.马美湖.我国蛋与蛋制品加工重大关键技术筛选研究报告[J].中国家禽,2004,26(24)：1-12.
28.马美湖.蛋与蛋制品加工学[M].北京：中国农业出版社,2005.
29.马兰,夏俊芳,张战锋.基于小波变换的番茄总糖近红外无损检测[J].农业工程学报2009,10：350-354.
30.欧阳玲花,冯建雄,闵华等.皮蛋加工研究现状与展望[J].食品工业技术,2009,30(4)：349-354.
31.潘磊庆,屠康,赵立.潘秀娟;敲击振动检测鸡蛋裂纹的初步研究[J].农业工程学报,2005,04：11-15.
32.彭辉,文友先,王巧华.基于小波变换和BP神经网络的蛋壳破损检测[J].农业机械学报,2009,2(12)：170-174
33.饶秀勤,应义斌,吕飞玲.水果声学特性测试系统的研制[J].农业机械学报,2004,2：69-71.
34.史波林,赵镭,刘文.苹果内部品质近红外光谱检测的异常样本分析[J].农业机械学报,2010,2：132-137.
35.宋开山,张柏,赵云升.东北主要高达落叶乔木单叶的偏振特性研究[J].东北 师大学报自然科学版,2004,36(1)：88-94.
36.孙晓兵,乔延利,洪津,王乐意.人工目标偏振特征实验研究[J].高科技通讯,2003,8.
37.孙静,马美湖,吴玲,乐立强.热处理对皮蛋生产周期和品质的影响[J].农业工程学报,2011,27(5)：367-372.
38.孙静,杜金平,马美湖.间歇式加压技术快速腌制皮蛋工艺研究[J].食品科学,2012,33(06)：1-6.
39.王巧华,邓小炎,文友先.鸡蛋敲击响应的奇异性特征与蛋壳裂纹多层检测[J].农业机械学报,2008,12：127-131.
40.王树才,文友先,刘俭英.基于机器人的禽蛋自动检测与分级系统集成开发[J].农业工程学报,2008,4：186-189.
41.王书茂.西瓜成熟度无损检测的振动冲击方法[J].农业工程学报,1999,15(3)：241-245.
42.王俊,滕斌,周鸣.梨敲击激励后产生的动力学特性研究[J].农业机械学报,2004,2：65-68.
43.王良玉.锌盐和铜盐在皮蛋加工中的应用及对产品品质的影响[J].农产品加工学刊,2005,(11)：76-80.
44.文友先,王巧华,宗望远.鸭蛋破损检测的试验研究[J].华中农业大学学报,2002,6：285-288.
45.吴彦红,刘木华,杨君,郑华东.基于计算机视觉的大米外观品质检测[J].农业机械学报,2007,38(7)：107-111.
46.徐志刚.基于多特征融合的路面破损图像自动识别技术研究[D].[博士学位论文]长安大学,2012
47.阎华,朱端卫.铜、锌、铁在皮蛋加工中作用差异研究{J}.食品科学,2006,27(12)：164-167.
48.杨祯,王树才.淋蜡式皮蛋封蜡机的设计与试验[J].农业工程学报,2011,27(5)：348-352.
49.殷涌光,丁筠.基于计算机视觉的蔬菜中活菌总数快速检测[J].农业工程学报,2009,7：249-254.
50.展慧,李小昱,王为.基于机器视觉的板栗分级检测方法[J].农业工程学报,2010,4：327-331.
51.赵文兵,马万征.利用偏振信息进行作物营养水平检测的初步研究[J].农业装备技术,2011,37(6)
52.赵永强,张洪才,潘泉.基于偏振特征的目标检测[J].量子电子学报,2003,20 (6)：685-688.
53.赵燕,徐明生,涂永刚.皮蛋加工相关机理研究进展[J],食品科学,2010,,31(17)：472-475.
54.赵燕,李鑫,李建科,徐明生,涂勇刚.电感耦合等离子发射光谱法测定铅法皮蛋中的多种无机元素[J].食品科学,2010,31(24)：337-340.
55.章建浩.蛋品包蜡机[J].食品与机械,1992,4(30)：28.
56.张兰威.蛋制品工艺学[M].黑龙江：黑龙江出版社,1996.
57.张所非,陈斌,褚静.基于声学特性的苹果无损检测方法[J].现代仪器,2007,2：11-14.
58.张荞,孙晓兵,洪津.不同湿度的低植被覆盖土壤表面偏振特性研究[J].光谱学与光谱分析,2010,30(11)：3086-3092.
59.张所非,陈斌,褚静.基于声学特性的苹果无损检测方法[J].现代仪器,2007,2：11-14.
60.张焰林,朱敏.基于多特征的指纹图像分割算法[J].计算机系统应用,2008,10：43-46.
61.张志建.新型蛋制品加工工艺与配方[M].科学技术文献出版社,2000.
62.周斌,林喜荣,贾惠波,宋榕.多特征融合的手背血管识别算法[J].清华大学学报(自然科学版),2007,47(2)：194-197.
63.郑晓鸣,吕世颖,王晓东.基于免疫粒子群优化的聚类算法[J].计算机工程,2008,34(15)：179-84.
64. Belie N, Abbort J A,H A Affeldt and L A Liljedahl.Firmness measurement of stored'delicious'apples by sensory methods, Mngness-Taylor, and sonic transmission, J. Amer, Soc, Hort[J]. Sci,1992, (4):590-595.
65. B. De Ketelaere, H. Vanhoutte, Parameter estimation and multivariable model building for the non-destructive, on-line determination of egg shell strength[J]. Journal of Sound and Vibration.2003 (266):699-709.
66. Bickel W. S, Videen G. W. Polarized light scattering applications and measurements of fundamental systems. Proceedings of the International Society for Optical Engineering(SPIE),1994,1746:14-21.
67. Breon F M, Tanre D, Lecomte P, et al. Polarized reflectance of bare soils and vegetation:measurements and models[J]. IEEE Transactions on Geoscience and Remote Sensing,1995,33(2):487-499.
68. Cho H K, Choi W K, Paek J K. Detection of surface in shell eggs by acoustic impulse method [J]. Transaxtions of ASAE,2000,43(6):1921-1926.
69. Chung-wei, Jui-chun Chang, Ching-wei Cheng, Li-cheng Hsieh. A novel non-destructive technology for inspecting eggshell cracks using impulsive response time [J]. Food Sci. Technol. Res.,2011,17(1):1-10.
70. Curran P. The relationship between polarized visible light and vegetation amount[J]. Remote Sensing of Environment,1981,11:87-92.
71. Dror Fixler, Rinat Ankri, Hamootal Duadi, Rachel Lubart, Zeev Zalevsky. Depolarization of light in biological tissues[J]. Optics and Lasers in Engineering,2012,50:850-854.
72. Firooz A. Sadjadi. New experiments in the use of infrared polarization in the detection of small targets[J]. Proceedings of SPIE,2001, 4379:144-155.
73. F. R. Bamelis, B. De Ketelaere, K. Mertens et..Measuring the conductance of eggshells using the acoustic resonance technique and optical transmission spectra[J]. Computers and Electronics in Agriculture,2008, 62(1):35-40.
74. Gong Jieqiong, Zhan Haigang, Liu Dazhao. A review on polarization information in the remote sensing detection. Spectroscopy and Spectral Analysis,2010,30(4):1088-1095.
75. Grosjean T, Conrion D. Polarization filtering induced by imaging system: effect on image structure[J]. Phys Rev E,2003,67:235-241.
76. Jenshin L, Lin Y, Hsieh M. An Automatic System for Eggshell Quality Monitoring [J]. Transactions of the ASAE,2001,44(3):1028-1034.
77. Jessica C. Ramella-Roman, Ken Lee, Scott A. Prahl. Desigh, testing and clinical strdies of a handheld polarized light camera[J]. Journal of Biomedical Oprics,2004,9(6):1305-1310.
78. Kohzo Homma, Hirokimi Shingu, Hiromichi Yamamoto, et al. Application of an imaging spectropolarimeter to agro-environmental sciences[J]. Proceedings of SPIE,2004,5234:639-647.
79. Lawrence, Kurt, Yoon, Seung. Imaging system with chamber modified-pressure for crack detection in shell eggs[J]. Sensing and Instrumentation for Food Quality and Safety,2008,2(2):116-122.
80. Litivinov P, Hasekamp 0, Cairns B, et al. Reflection models for soil and vegetation surfaces from multiple-viewing angle photopolarimetric measurements[J]. Journal of Quantitative Spectroscopy and Radiative Transfer,2010,111:529-539.
81. Maignan F, Breon F M, Fedele E, et al. Polarized reflectances of natural surfaces:spaceborne measurements and analytical modeling[J]. Remote Sensing Environ,2009,113:2642-2650.
82. Matthew H. Smith. Interpreting Mueller matrix images of tissue[J]. Laser-Tissue Interaction Ⅻ, SPIE,2001,4257:82-89.
83. M. barcaglia, P. Ferrazzoli. A fully polarimentic multiple scattering model for agricultural fields[J]. IEEE International,1995,1556-1562.
84. M. C. Garcia-Alegre, A. Ribeiro, D. Guinea and G. Cristobal. Eggshell defects detection based on color processing [C]. SPIE2000 Electronic Imaging Conf. CA, Jan.2000.
85. N.Gupta, L. Denes, M.Gottlieb et al. Spectropolarimetric Imaging Using a Field-Portable Imager[J]. Imaging Spectrometry VISPIE,2000, 4132:372-381.
86. Palanivel Ganasen, Soottawat Benjakul. Physical Properties and Microstructure of Pidan Yolk as Affected by Different Divalent and Monovalent Cations. LWT-Food Science and Technology,2010,43:77-85.
87. Palanivel Ganesan, Soottawat Benjakul. Influence of Different Cations on Chemical Composition and Microstructure of Pidan White and Yolk During Pickling and Aging[J]. International Journal of Food Properties,2010, 13:1150-1160.
88. Peltoniemi J, Hakala T, Suomalainen J, et al. Polarised bidirection factor measurement from soil, stones and snow[J]. Journal of Quantitative Spectroscopy and Radiative Transfer,2009,110:1940-1953.
89. Pelyushenko S. A.. Microwave radiometrer system for the detection of oil slicks [J]. Spill Science & Technology Bulletin,1995,2(4):249-254.
90. P. N. Raven, D.C.Jordan. Polarized directional reflectance from laurel and mullein leaves[J]. Opt. Eng.,2002,41(5):1002-1012.
91. Roy M. Matchko, Grant R. Gerhart, Polarization azimuth angle in daylight scenes, Optical Engineering,2005,44(2):1-9.
92. Scchotte S. Acoustic impulse-response technique for evaluation and modeling of firmness of tomato fruit[J]. Postharvest biology and technology,1999,17(2):105-115.
93. Schechner Y. Y, Shamir Joseph. Polaarization and statistical analysis of scenes containing a semireflector[J]. Opt. Soc. Am. A,2000,17(2):276-284.
94. S. H. Sposato, Fetrow M. P. Two Long-Wave Infrared Spectral Polarimeters for Use in Understanding Polarization Phe-nomenology[J]. Opt. Eng.,2002, 41(5):1055-1064.
95. Snelick R, U ludag U, Mink A, et al. Large scale evaluation of multimodal biometric authentication using state-of-the-art systems[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005, 27(3):450-455.
96. S.L. Jacques, J. C. Ramella-Roman, K. Lee. Imaging Skin Pathology with Polarized Light. J Biomed Optics,2002,7:329-340.
97. Stenven L. Jacques, Jessica R. Roman, Ken Lee. Imaging superficial tissues with polarized light. Laser. Sur. Med,2000,26:119-129.
98. Steven L. Jacques, Ravikant Samatham, Scott Isenhath, Ken Lee. Polarized light camera to guide surgical excision of skin cancers [J]. Proc. of SPIE, 2008,6842:684301-684207.
99. Sugiyamma J, T Katsural, J Hong, et al. Portable melon firmness tester using acoustic implus transmission[C]. Orlando, Florida:Proceedings from the sensors for nondestructive testing international conference,1997.
100. Vanderbil T V C. Plant canopy specular reflectance model[J]. IEEE Transactions on Geoscience and Remote Sensing,1985,23(5):722-730.
101. Youxian Wen, Qiaohua Wang, Wangyuan Zong, Lirong Xiong, Jianying Liu, Yousheng Yu. Study on dilapidation of duck egg by audio signal [J]. Journal of Huazhong Agricultural University,2002,21:285-287.
102. Xiaoyan Deng, Qiaohua Wang, Hong Chen, Hong, Xie. Eggshell crack detection using a wavelet-based support vector machine[J]. Comput. Electron. Agric.,2010,70:135-143.
103. Yao Y, Wang Y. Design of a polarimeter for the infrared camera OASIS. ACTA Astron Sin,1999,40:213-222.
104. Zhao Y, Zhang L, Zhang D, et al. Object separation by polarimetric and spectral imagery fusion [J]. Computer Vision and Image Understanding,2009, 113(8):855-866.
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