苹果早期机械损伤的红外热成像检测研究
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摘要
水果的缺陷检测一直是农产品加工中非常重要的步骤之一。尤其是苹果的早期机械损伤,通常具有轻微凹陷、色稍变暗、无汁液外溢、肉眼难于察觉、病原微生物容易入侵而引起腐烂等特点,影响后续存储及销售,容易造成较大经济损失。目前,我国现有的水果缺陷检测技术主要还是以人工目测或常规机器视觉检测技术为主,容易受水果表皮色泽干扰,损伤果漏检率很高。
针对以上问题,本文将红外热成像技术引入到果品的早期缺陷检测当中,结合我国实际情况,以红富士苹果为研究对象,重点从传热分析和图像处理两个方面,展开对苹果表面常见机械损伤的红外热成像无损检测的研究。主要研究内容及研究成果如下:文章首先分析比较了水果质量无损检测技术的优缺点,综述了本文所用方法——红外热成像技术在水果缺陷检测中所取得的成就以及存在的问题和关键技术,随后给出了本文的研究内容及技术路线。
论文前部分主要从传热学方面来研究水果机械损伤的红外热成像检测。首先介绍了红外辐射的基本理论及定律,建立了苹果早期机械损伤的红外热成像检测系统,分析了球面辐射规律,研究了水果表面损伤的红外热成像检测机理,并建立了球形水果的一维非稳态导热方程传热学模型。随后通过实验验证上述理论模型,分析了获取的水果热图像的不同区域温度分布特征,分析了损伤苹果的降温特性曲线。实验结果表明:苹果的水平温度轮廓曲线的形态特征可以用来较好地区分不同机械损伤和果梗及花萼;苹果某部分的过余温度比的对数是关于时间t的一次函数,由此斜率可以判断出损伤类型;划伤与完好组织间较好的温度差异能持续60s~240s,最佳检测时间为加热后0s~30s,碰伤与完好组织间较好的温度差异能持续20s~100s左右,最佳检测时间在加热后30s~60s间。
论文后半部分主要从数字图像处理方面来研究水果机械损伤的红外热成像检测。首先,在分析了苹果的红外热图像的灰度不均匀特征的基础上,提出了一种基于直方图匹配和低尺度条件下的SSR(Single-Scale-Retinex)算子的非均匀校正方法。该方法以直方图匹配技术提高水果缺陷对比度,以低尺度条件下SSR处理来增强水果的边缘特征,最后通过加权方式将两幅图像融合起来,以达到灰度均匀校正的目的。对校正后的水果热图像进行简单的阈值分割,并引入缺陷完整分割下的边缘损失率来评价分割效果。对分割后的二值图像进行缺陷分离、形态学处理以及边缘轮廓提取,以便于对苹果缺陷部位的热衍射效应的研究及最佳检测时机的判别。实验结果表明:经本文方法校正后的图像的灰度不均匀特点明显被改善,整体灰度大致较为接近。通过缺陷分割后,水果边缘的最大损失率仅为3.05%;完好苹果的准确检测率为92%,缺陷准确检测率为在87.5%,可以较好地用于水果早期机械损伤的检测当中。
The defects detection in fruit is very important in agriculture products processing. Especially the apple with early mechanical damage which has no apparent changes of color, smell and taste, it will lead to big economic losses result from infection of microbes and pulp rottenness. At present, defect detection in fruits in our country has been predominantly performed manually or by visible spectrum imaging systems, which are not capable of effectively distinguishing fruit with mechanical damage which occur a short time before inspection.
Aiming at above problems, the infrared thermography nondestructive technique introduced in this paper can address it. According to the specific situation of China, Red-Fuji apple was chosen and the detailed study of infrared thermography detection of early mechanical damage in it was done from two perspectives mainly: heat transfer analysis and image processing. The main work and achievements are as follows:
Firstly, it analyzed and compared the merits and demerits of fruit quality non-destructive techniques, and reviewed the achievements and potential problems and key technologies of infrared thermography technology introduced in this paper of fruit defects detection, and then presented the main content of our research and the way we use.
The first three parts are analysis in accordance with heat transfer theory. Firstly, it introduced the basic theory and laws of infrared radiation, established the infrared thermography system and experimental method for detecting apples mechanical damages, analyzed the radiation laws of spherical surface, researched the non-destructive detection mechanism of infrared thermography of fruit, and build a heat transfer model of one-dimension unsteady state heat conduction equation for evaluating the cooling properties of spherical fruits. And then it analyzed the temperature behavior within different regions of apples in thermal images, and analyzed the dynamic temperature cooling curves from experimental measurements of defective apples. The experiments results revealed that the features of level temperature profile of apples in thermal images could be as an effective method to identify different mechanical damages, stalk and calyx. From the theory analysis and experimental study, we gained ftinction logarithmic curve of apples surface temperature and time ideally, it would provide another way to recognize different mechanical damages. Besides, from the cooling analysis, we found that the good temperature contrast between scratch and sound tissue could last 60 second to 240 second; the best detection time varied 0 second to 30 second after thermal excitation. And the good temperature contrast between bruise and sound tissue could last 20 second to 100 second; the best detection time varied 30 second to 60 second after thermal excitation.
The last half parts tries to study the infrared thermography detection of fruit damages from digital image processing. Firstly, on the basis of analyzing the gray features of apple thermal images, it proposed a non-uniformity correction method based on histogram matching and Single-Scale-Retinex algorithm under low scale, which using the histogram matching algorithm to highlight the contrast of raw thermal images, and utilizing SSR algorithm to enhance the edges of fruits in raw thermal images, with combination of contrast adjustment image and edge enhanced model, final fused image can be obtained. Then selecting a threshold to segment defects and utilizing the loss rate at accurate segmentation of defects to assess the effectiveness of above methods. And then the post-processing measures such as defects dividing, morphological processing and edge extraction were done in order to get the contour profile of damage regions and research the thermal diffraction effects of these regions. The testing results demonstrate that the thermal images after non-uniformity correction show rich details and good contras. After thresholding, the biggest loss rate at accurate segmentation of defects is only 3.05%. Through numerical experiments show that the accurate detection rate of sound apples can reach to 92%, and the accurate detection rate of defective apples is still over 87%, which can be well used in fruit early mechanical damages detection.
针对以上问题,本文将红外热成像技术引入到果品的早期缺陷检测当中,结合我国实际情况,以红富士苹果为研究对象,重点从传热分析和图像处理两个方面,展开对苹果表面常见机械损伤的红外热成像无损检测的研究。主要研究内容及研究成果如下:文章首先分析比较了水果质量无损检测技术的优缺点,综述了本文所用方法——红外热成像技术在水果缺陷检测中所取得的成就以及存在的问题和关键技术,随后给出了本文的研究内容及技术路线。
论文前部分主要从传热学方面来研究水果机械损伤的红外热成像检测。首先介绍了红外辐射的基本理论及定律,建立了苹果早期机械损伤的红外热成像检测系统,分析了球面辐射规律,研究了水果表面损伤的红外热成像检测机理,并建立了球形水果的一维非稳态导热方程传热学模型。随后通过实验验证上述理论模型,分析了获取的水果热图像的不同区域温度分布特征,分析了损伤苹果的降温特性曲线。实验结果表明:苹果的水平温度轮廓曲线的形态特征可以用来较好地区分不同机械损伤和果梗及花萼;苹果某部分的过余温度比的对数是关于时间t的一次函数,由此斜率可以判断出损伤类型;划伤与完好组织间较好的温度差异能持续60s~240s,最佳检测时间为加热后0s~30s,碰伤与完好组织间较好的温度差异能持续20s~100s左右,最佳检测时间在加热后30s~60s间。
论文后半部分主要从数字图像处理方面来研究水果机械损伤的红外热成像检测。首先,在分析了苹果的红外热图像的灰度不均匀特征的基础上,提出了一种基于直方图匹配和低尺度条件下的SSR(Single-Scale-Retinex)算子的非均匀校正方法。该方法以直方图匹配技术提高水果缺陷对比度,以低尺度条件下SSR处理来增强水果的边缘特征,最后通过加权方式将两幅图像融合起来,以达到灰度均匀校正的目的。对校正后的水果热图像进行简单的阈值分割,并引入缺陷完整分割下的边缘损失率来评价分割效果。对分割后的二值图像进行缺陷分离、形态学处理以及边缘轮廓提取,以便于对苹果缺陷部位的热衍射效应的研究及最佳检测时机的判别。实验结果表明:经本文方法校正后的图像的灰度不均匀特点明显被改善,整体灰度大致较为接近。通过缺陷分割后,水果边缘的最大损失率仅为3.05%;完好苹果的准确检测率为92%,缺陷准确检测率为在87.5%,可以较好地用于水果早期机械损伤的检测当中。
The defects detection in fruit is very important in agriculture products processing. Especially the apple with early mechanical damage which has no apparent changes of color, smell and taste, it will lead to big economic losses result from infection of microbes and pulp rottenness. At present, defect detection in fruits in our country has been predominantly performed manually or by visible spectrum imaging systems, which are not capable of effectively distinguishing fruit with mechanical damage which occur a short time before inspection.
Aiming at above problems, the infrared thermography nondestructive technique introduced in this paper can address it. According to the specific situation of China, Red-Fuji apple was chosen and the detailed study of infrared thermography detection of early mechanical damage in it was done from two perspectives mainly: heat transfer analysis and image processing. The main work and achievements are as follows:
Firstly, it analyzed and compared the merits and demerits of fruit quality non-destructive techniques, and reviewed the achievements and potential problems and key technologies of infrared thermography technology introduced in this paper of fruit defects detection, and then presented the main content of our research and the way we use.
The first three parts are analysis in accordance with heat transfer theory. Firstly, it introduced the basic theory and laws of infrared radiation, established the infrared thermography system and experimental method for detecting apples mechanical damages, analyzed the radiation laws of spherical surface, researched the non-destructive detection mechanism of infrared thermography of fruit, and build a heat transfer model of one-dimension unsteady state heat conduction equation for evaluating the cooling properties of spherical fruits. And then it analyzed the temperature behavior within different regions of apples in thermal images, and analyzed the dynamic temperature cooling curves from experimental measurements of defective apples. The experiments results revealed that the features of level temperature profile of apples in thermal images could be as an effective method to identify different mechanical damages, stalk and calyx. From the theory analysis and experimental study, we gained ftinction logarithmic curve of apples surface temperature and time ideally, it would provide another way to recognize different mechanical damages. Besides, from the cooling analysis, we found that the good temperature contrast between scratch and sound tissue could last 60 second to 240 second; the best detection time varied 0 second to 30 second after thermal excitation. And the good temperature contrast between bruise and sound tissue could last 20 second to 100 second; the best detection time varied 30 second to 60 second after thermal excitation.
The last half parts tries to study the infrared thermography detection of fruit damages from digital image processing. Firstly, on the basis of analyzing the gray features of apple thermal images, it proposed a non-uniformity correction method based on histogram matching and Single-Scale-Retinex algorithm under low scale, which using the histogram matching algorithm to highlight the contrast of raw thermal images, and utilizing SSR algorithm to enhance the edges of fruits in raw thermal images, with combination of contrast adjustment image and edge enhanced model, final fused image can be obtained. Then selecting a threshold to segment defects and utilizing the loss rate at accurate segmentation of defects to assess the effectiveness of above methods. And then the post-processing measures such as defects dividing, morphological processing and edge extraction were done in order to get the contour profile of damage regions and research the thermal diffraction effects of these regions. The testing results demonstrate that the thermal images after non-uniformity correction show rich details and good contras. After thresholding, the biggest loss rate at accurate segmentation of defects is only 3.05%. Through numerical experiments show that the accurate detection rate of sound apples can reach to 92%, and the accurate detection rate of defective apples is still over 87%, which can be well used in fruit early mechanical damages detection.
引文
[1].黄星奕,刘益权,赵杰文.基于近红外图像处理技术的水果轻微损伤检测[J].微计算机信息, 2007, (23): 229-231.
[2].王莉萍.水果质量快速无损检测技术的研究进展[EB/OL]. http://www.nsfc.gov.cn/Portal0/ InfoModule_375/26999.htm, 2009-03-23.
[3].饶秀勤.基于机器视觉的水果品质实时检测与分级生产线的关键技术研究[D]: [博士学位论文].浙江:浙江大学, 2007.
[4].宋玉伟,宋纯鹏.红外成像技术在生命科学中的应用[J].生命科学研究, 2004, 8(2): 123-124.
[5].周建民,周其显,刘燕德.红外热成像技术在农业生产中的应用[J].农机化研究, 2010, 32 (2): 1-4.
[6].刘燕德等.无损智能检测技术及应用[M].武汉,华中科技大学出版社, 2007.
[7].庞江伟,应义斌.机器视觉在水果缺陷检测中的研究现状[J].农机化研究, 2006, (9): 47-49.
[8].邹小波,赵文杰.农产品无损检测技术与数据分析[M].北京,中国轻工业出版社, 2008.
[9].田有文,王晓娟.基于高光谱图像技术的农产品品质无损检测[J].农机化研究, 2009, (10): 220-222.
[10].赵杰文,刘剑华,陈全胜.利用高光谱图像技术检测水果轻微损伤[J].农业机械学报, 2008, 39 (1): 106-109.
[11].韩平,潘立刚,马智宏等. X射线无损检测技术在农产品品质评价中的应用[J].农机化研究, 2009, (10): 6-10
[12].庞林江,王允祥,何志平.核磁共振技术在水果品质检测中的应用[J].农机化研究, 2006, (08): 176-180.
[13].徐义广,刘波,李艳红等.被动式与主动式红外热成像技术研究[J].应用光学, 2008, 29(5): 44-48.
[14].王瑞凤,杨宪江,吴伟东.发展中的红外热成像技术[J].红外与激光工程, 2008, 37(6): 699-702.
[15].戴景民,汪子君.红外热成像无损检测技术及其应用现状[J].自动化技术与应用, 2007, 36(9): 335-338.
[16].雷玉堂.红外热成像技术及在智能视频监控中的应用[J].中国公共安全, 2007, (8): 114-120.
[17]. Krishnamurthy K., Khurana H.K., Jun S., et al. Infrared Heating in Food Processing: An Overview [J]. Comprehensive Reviews in Food Science and Food Safety, 2008, (7): 2-13.
[18]. Sugiura R., Noguchi N., Ishii K., Correction of Low-altitude Thermal Images applied to estimating Soil Water Status [J]. Biosystems Engineering, 2007, 96(3): 301-313.
[19]. Jones H.G., Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling [J]. Agricultural and Forest Meteorology, 1999, (95): 139-149.
[20]. Kim Y., Ling P.P., Machine vision guided sensor positioning system for leaf temperature assessment [J]. Trans of the ASAE, 2001, 44(6): 1941-1947.
[21]. Grant O.M., Chaves M.M., Jones H.G., Optimizing thermal imaging as a technique for detecting stomatal closure induced by drought stress under greenhouse conditions [J]. Physiologia Plantarum, 2006, (127): 507-518.
[22]. M?ller M., Alchanatis V., Cohen Y., et al. Use of thermal and visible imagery for estimating cropwater status of irrigated grapevine [J]. Journal of Experimental Botany, 2007, 58(4): 827-838.
[23]. Fuchs M., Infrared measurement of canopy temperature and detection of plant water stress [J]. Theoretical and Applied Climatology, 1990, (42): 253-261.
[24].袁燕利,邢娟,李汝莘.农业机械智能化与实施精确农业[J].农业机械学报, 2001(4): 127-128.
[25]. Maas S.J., Use of remotely sensed information in agricultural crop growth models [J]. Ecological Model, 1988, (41): 247-268.
[26]. Lefebvre M., Gil S., Brunet D., et al. Computer vision and agricultural robotics for disease control: the potato operation [J]. Computer Electron Agriculture, 1993, 9(1): 85-102.
[27]. Schaefer A.L., Cook N.J., Church J.S., The use of infrared thermography as an early indicator of bovine respiratory disease complex in calves [J]. Research in Veterinary Science, 2007, 83(3): 376-384.
[28]. Chaerle L., Straeten D.V.D., Seeing is believing:imaging techniques to monitor plant health [J]. Biochemical et Biophysical Acta, 2001, 1519(3): 153-166.
[29]. Chaerle L. Presymptomatic visualization of plant-virus interactions by thermography [J]. Nature Biotechnology, 1999, (17): 813-816.
[30]. Oerke E.C., Steiner U.S., Dehne H.W., et al. Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions [J]. Journal of Experimental Botany, 2006, 57(9): 2121-2132.
[31].王瑞良.驰骋田野的农业机器人[EB/OL]. http://www.sichuandaily.com.cn/1999/ sichuandaily/199909/21/html/7012.html, 1999.
[32].徐惠荣,应义斌.红外热成像在树上柑桔识别中的应用研究[J].红外与毫米波学报, 2004, 23(5): 353-355
[33]. Ridgway C., Chambeers J., Detection of insects inside wheat kernels by NIR imaging [J]. Journal of Near Infrared Spectroscopy, 1998, 6 (2): 115-191.
[34]. Meinlschmidt P., Margner V., Thermographic techniques and adopted algorithms for automatic detection of foreign bodies in food [J]. Proceedings of SPIE, 2002, (4710): 399-406.
[35]. Ginesu G., Giusto D.D., M?rgner V., et al. Detection of Foreign Bodies in Food by Thermal Image Processing [J]. IEEE Transactions on Industrial Electronics, 2004, 51(2): 480-490.
[36]. Fito P.J., Ortola M.D., Delos Reyes R. et al. Control of citrus surface drying by image analysis of infrared thermography [J]. Journal of Food Engineering, 2004, (61):287-290.
[37]. Gottschalk K., Geyer S., Hellebrand H.J., et al. Infrared Thermography for Climate Control in Big Box Potato Store [J]. Landtechnik, 2004, (2): 96-97.
[38]. Geyer S., Gottschalk K., Infrared Thermography to Monitor Natural Ventilation during Storage of Potatoes [J]. Agricultural Engineering International: The CIGR Ejournal, 2008, (4): 1-14.
[39]. Mladenov M., Damyanov C., Atanasova S. et al. Intelligent technologies for assessment of quality and safety of food agricultural products [J]. Agricultural science and technology, 2009, (1): 38-42.
[40]. Vadivambal R., Jayas D.S., Applications of Thermal Imaging in Agriculture and Food Industry—A Review [J]. Food Bioprocess Technology, Online First?, 24 February 2010.
[41]. Gowena A.A., Tiwaria B.K., Cullenb P.J., et al. Applications of thermal imaging in food quality and safety assessment[J]. Trends in Food Science & Technology, 2010, 21(4): 190-200.
[42]. Danno A., Miyazato M., Ishiguro E., Quality evaluation of agricultural Products by infrared imaging method:Ⅲ.Maturity evaluation of fruits and vegetables [J]. Memoirs of the faculty of agriculture, Kagoshima University. 1980, (16): 157-164.
[43]. Danno A., Miyazato M., Ishiguro E., Quality evaluation of agricultural Products by infraredimaging method:Ⅰ.Grading of fruits for bruise and other surface defects [J]. Memoirs of the faculty of agriculture, Kagoshima University. 1978, (14): 123-138.
[44]. Hellebrand H.J., Linke M., Beuehe H., et al. Horticultural Products Evaluated by Thermography [J].University of Warwick. 2000, (7): 26-27.
[45].徐惠荣.基于机器视觉的树上柑桔识别方法研究[D]: [硕士学位论文].浙江:浙江大学, 2004.
[46]. Wurzbach R.N., Infrared application in farming: vision of things to come [J]. Proceeding of SPIE, 2003, (5073): 154-159.
[47]. Varith J., Hyde G.M., Baritelle A.L., et al. Non-contact bruise detection in apples by thermal imaging [J]. Innovative Food Science and Emerging Technologies, 2003, (4): 211–218.
[48]. Birla S.L., Wang S., Tang J., et al. Improving heating uniformity of fresh fruit in radio frequency treatments for pest control [J]. Postharvest Biology and Technology, 2004, (33): 205-217.
[49]. Baranowski P., Lipecki J., Mazurek W., et al. Detection of apple bruises with the use of thermography [J]. Acta Agrophysica, 2005, (125): 19-29.
[50]. Veraverbeke E.A., Verboven P., Lammertyn J, et al. Thermographic surface quality evaluation of apple [J]. Journal of Food Engineering, 2006, (77): 162-168.
[51]. Baranowski P., Lipecki J., Mazurek W, et al. Detection of watercore in‘Gloster’apple using thermography [J]. Postharvest Biol. And Technol., 2008, 47(3): 358-366.
[52]. Baranowski P., Mazurek W., Detection of physiological disorders and mechanical defects in apples using thermography [J]. Int. Agrophysics, 2009, (23): 9-17.
[53]. Baranowski P., Mazurek W., Walczak B.W., et al. Detection of early apple bruises using pulsed-phase thermography [J]. Postharvest Biology and Technology, 2009, (53): 91-100.
[54]. Miller B.K., Delwiche M.J., A color vision system for peach grading [J].Trans of the ASAE, 1989, 32(4): 1484-1490.
[55]. Wen Z., Tao Y., Fuzzy-based determination of model and parameters of dual-wavelength vision system for on-line apple sorting [J]. Optical Engineering, 1998, (37): 293-29.
[56]. Tao Y., Wen Z., Adaptive spherical image transform for high-speed fruit defect detection [J]. Transaction of the ASAE, 1998, 42 (1): 241-461.
[57]. Wen Z., Tao Y., Dual-camera NIR/MIR imaging for stem-end/calyx identification in apple defect sorting [J]. Transaction of the ASAE, 2000, 43(2): 449-452.
[58]. Cheng X., Tao Y., Chen Y R, et al. NIR/MIR dual sensor machine vision system for online apple stem-end/calyx recognition [J]. Transaction of the ASAE, 2003, 46(2): 551-558.
[59].王汝琳,王咏涛.红外检测技术[M].北京,中国轻工业出版社, 2006.
[60].新浪博客.红外热像检测服务-国内最专业的技术人员给您提供帮助[EB/OL]. http://blog.sina.com.cn/s/blog_694475a50100jol5.html, 2010-7-2.
[61].张维力,陈伟星.浅析红外热成像技术[J].金卡工程, 2002, (8): 70-73.
[62].北京华泰科捷科技有限公司.红外热成像原理[EB/OL]. http://www.bj-htkj.com/ newscontent.asp?/507.html, 2010-4-23.
[63].山东泰山网.红外辐射三个基本定律[EB/OL]. http://www.sd-taishan.gov.cn/sites/ binzhou/zouping/articles/J00000/1/1489371.aspx, 2008-4-3.
[64].敏适安防.红外基本原理介绍[EB/OL]. http://www.gzhongy.com/newsdisp_A2.asp, 2008.
[65].田裕鹏.红外检测与诊断技术[M].北京,化学工业出版社, 2006.
[66].程玉兰.红外检诊断现场实用技术[M].北京,机械工业出版社, 2002.
[67].周建民,周其显.基于主动热成像技术的苹果早期机械损伤检测[J].农机化研究, 2010, 32 (8): 162-165.
[68].田裕鹏.红外辐射成像无损检测关键技术研究[D]: [博士学位论文].江苏:南京航空航天大学, 2009.
[69].杨小林,杜来林,冯立春.红外热成像检测中的主动加热方法研究[J].激光与红外, 2007, 37(11): 1188-1191.
[70].高永毅,焦群英,唐果等.水果机械损伤研究概况及发展趋势[J].湘潭师范学院学报(自然科学版), 2002, 24(3): 26-29.
[71].果树技术网.减少果品机械损伤的技术措施[EB/OL]. http://www.guoshujishu.com/Article /lishujishu/kexueguanli/3034.html.
[72].郭道峰,王家森,刘忠齐.球面热源辐射特性的研究[J].中国医学影像技术, 2002, 18(8): 830-833.
[73]. Wang S., Tang J., Cavalieri R.P., Modeling fruit internal heating rates for hot air and hot water treatments [J]. Postharvest Biology and Technology, 2001, (22): 257–270.
[74]. Rahman, S., Food Properties Handbook. New York, CRC Press, 1995.
[75]. Mohsenin, N.N., Thermal Properties of Foods and other Agricultural Materials. New York, Gordon and Breach Science Publishers, 1980.
[76]. Varith, J., Uses of thermal properties for non-destructive assessment of apple quality [D]: [Ph.D. Dissertation]. Washington State University, WA, 2001.
[77].曹铁平.化工基础[EB/OL]. http://www.xiadoc.com/doc/9611c50be9351a1efbb233daa488 edce.html, 2010-07-16.
[78].张建涛.基于红外测温技术的工业热设备内部缺陷诊断方法[D]: [硕士学位论文].重庆:重庆大学, 2008.传热分析)
[79].匡以顺.基于红外热图像信息的铜转炉炉衬蚀损状态在线测量系统研究[D]: [硕士学位论文].江西:江西理工大学, 2006.
[80]. Bulanon D.M., Burks T.F., Alchanatis V., Image fusion of visible and thermal images for fruit detection [J]. Biosystems Engineering, 2009, (103): 12-22.
[81].黄红梅.物体内部孔洞的红外无损检测[D]: [硕士学位论文].天津:天津大学, 2006.
[82].邵凡麒,红外图像处理[D]: [硕士学位论文].江苏:南京理工大学, 2005.
[83]. (美)冈萨雷斯(Gonzalez, R C.)等著,阮秋琦等译.数字图像处理(MATLAB版)[M].电子工业出版社,北京, 2005.
[84].刘莹.红外热像仪中图像增强算法的研究[D]: [硕士学位论文].吉林:吉林大学, 2007.
[85].汤慧君,丁铁英,薛书文.图像处理在脉冲加热红外热成像定量检测中的应用[J].北方交通大学学报, 1999, 23(2): 9-11.
[86].郭师虹.空域红外图像增强方法的研究[D]: [硕士学位论文].陕西:西安建筑科技大学, 2005.
[87]. Tao Y., Wen Z., An adaptive spherical image transform for high-speed fruit defect detection. Transactions of the ASAE, 1999, 42(1): 241–246.
[88].冯斌,汪懋华.计算机视觉技术识别水果缺陷的一种新方法[J].中国农业大学学报, 2002, 7(4): 73-76.
[89]. Go′mez-Sanchis J., Molto′E., Camps-Valls G., Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits [J]. Journal of Food Engineering, 2008, (85): 191-200.
[90].楼竞,吴访升,刘栋梁.灰度直方图规定化实现方法的分析[J].江苏技术师范学院学报, 2004, 10(4): 65-69.
[91].吴铁洲,熊才权.直方图匹配图像增强技术的算法研究与实现[J].湖北工业大学报, 2005,20(2): 59-61.
[92].李学明.基于Retinex理论的图像增强算法[J].计算机应用研究,2005, (2): 235-237.
[93].雷美荣,杨进华,张金泉.基于Retinex理论的红外图像的边缘增强算法[J].长春理工大学学报(自然科学版). 2008, 31(2): 11-13.
[94]. Lei L., Zhou Y.Q., Li J.W., An investigation of Retinex algorithms for image enhancement. Journal of Electron (China) 2007, 24(5): 696-700.
[95].刘莹.红外热像仪中图像增强算法的研究[D]: [硕士学位论文].吉林:吉林大学, 2007.
[96]. Jobson D.J., Rahman Z.U., Woodell G.A., Properties and performance of a center/surround retinex. IEEE Transactions on Image Processing, 1997, 6 (3): 451-462.
[97].钟建军.视频增强Retinex算法介绍[EB/OL]. http://www.casevision.net/ViewNewas.asp? SortID=9&PID=140.
[98]. Jobson D.J., Rahman Z., Woodell G.A., A Multiscale Retinex for bridging the gap between color images and the human observation of scenes [J]. IEEE Transactions on image processing, 1997, 6(7): 965-967
[99].陈兵旗,孙明. Visual C++实用图像处理[M].清华大学出版社, 2004.
[100]. Toet A. Merging thermal and visual images by a contrast pyramid [J]. Optical Engineering, 1989, 28(7): 789-79.
[101].沈宽,文玉梅,蔡玉芳.基于脊波融合的射线图像增强算法[J].计算机工程与应用, 2010, 46(23): 170-174.
[102].强赞霞.遥感图像的融合及应用[D]: [博士学位论文].湖北:华中科技大学, 2005.
[103].刘平.图像分割阈值选取技术综述[EB/OL]. http://www.ilovematlab.cn/thread-6220-1-1.html, 2008-4-16.
[104].江明,刘辉,黄欢.图像二值化技术的研究[J].软件导刊, 2009, 8(4): 175-177.
[105].徐国保,尹怡欣,王骥等.基于融合自适应形态滤波的分水岭分割新算法[J].计算机应用研究. 2009, 26(8): 3143-3145.
[106].同武勤,凌永顺,黄超超等.数学形态学和小波变换的红外图像处理方法[J].光学精密工程, 2007, 15(1): 138-144.
[107].周道炳,朱卫纲.几种边缘检测算子的评估[J].指挥技术学院学报, 2000, 11(1): 59-63.
[108].肖强.圆锥滚子轴承视觉检测研究[D]: [硕士学位论文].江西:华东交通大学, 2007.
[2].王莉萍.水果质量快速无损检测技术的研究进展[EB/OL]. http://www.nsfc.gov.cn/Portal0/ InfoModule_375/26999.htm, 2009-03-23.
[3].饶秀勤.基于机器视觉的水果品质实时检测与分级生产线的关键技术研究[D]: [博士学位论文].浙江:浙江大学, 2007.
[4].宋玉伟,宋纯鹏.红外成像技术在生命科学中的应用[J].生命科学研究, 2004, 8(2): 123-124.
[5].周建民,周其显,刘燕德.红外热成像技术在农业生产中的应用[J].农机化研究, 2010, 32 (2): 1-4.
[6].刘燕德等.无损智能检测技术及应用[M].武汉,华中科技大学出版社, 2007.
[7].庞江伟,应义斌.机器视觉在水果缺陷检测中的研究现状[J].农机化研究, 2006, (9): 47-49.
[8].邹小波,赵文杰.农产品无损检测技术与数据分析[M].北京,中国轻工业出版社, 2008.
[9].田有文,王晓娟.基于高光谱图像技术的农产品品质无损检测[J].农机化研究, 2009, (10): 220-222.
[10].赵杰文,刘剑华,陈全胜.利用高光谱图像技术检测水果轻微损伤[J].农业机械学报, 2008, 39 (1): 106-109.
[11].韩平,潘立刚,马智宏等. X射线无损检测技术在农产品品质评价中的应用[J].农机化研究, 2009, (10): 6-10
[12].庞林江,王允祥,何志平.核磁共振技术在水果品质检测中的应用[J].农机化研究, 2006, (08): 176-180.
[13].徐义广,刘波,李艳红等.被动式与主动式红外热成像技术研究[J].应用光学, 2008, 29(5): 44-48.
[14].王瑞凤,杨宪江,吴伟东.发展中的红外热成像技术[J].红外与激光工程, 2008, 37(6): 699-702.
[15].戴景民,汪子君.红外热成像无损检测技术及其应用现状[J].自动化技术与应用, 2007, 36(9): 335-338.
[16].雷玉堂.红外热成像技术及在智能视频监控中的应用[J].中国公共安全, 2007, (8): 114-120.
[17]. Krishnamurthy K., Khurana H.K., Jun S., et al. Infrared Heating in Food Processing: An Overview [J]. Comprehensive Reviews in Food Science and Food Safety, 2008, (7): 2-13.
[18]. Sugiura R., Noguchi N., Ishii K., Correction of Low-altitude Thermal Images applied to estimating Soil Water Status [J]. Biosystems Engineering, 2007, 96(3): 301-313.
[19]. Jones H.G., Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling [J]. Agricultural and Forest Meteorology, 1999, (95): 139-149.
[20]. Kim Y., Ling P.P., Machine vision guided sensor positioning system for leaf temperature assessment [J]. Trans of the ASAE, 2001, 44(6): 1941-1947.
[21]. Grant O.M., Chaves M.M., Jones H.G., Optimizing thermal imaging as a technique for detecting stomatal closure induced by drought stress under greenhouse conditions [J]. Physiologia Plantarum, 2006, (127): 507-518.
[22]. M?ller M., Alchanatis V., Cohen Y., et al. Use of thermal and visible imagery for estimating cropwater status of irrigated grapevine [J]. Journal of Experimental Botany, 2007, 58(4): 827-838.
[23]. Fuchs M., Infrared measurement of canopy temperature and detection of plant water stress [J]. Theoretical and Applied Climatology, 1990, (42): 253-261.
[24].袁燕利,邢娟,李汝莘.农业机械智能化与实施精确农业[J].农业机械学报, 2001(4): 127-128.
[25]. Maas S.J., Use of remotely sensed information in agricultural crop growth models [J]. Ecological Model, 1988, (41): 247-268.
[26]. Lefebvre M., Gil S., Brunet D., et al. Computer vision and agricultural robotics for disease control: the potato operation [J]. Computer Electron Agriculture, 1993, 9(1): 85-102.
[27]. Schaefer A.L., Cook N.J., Church J.S., The use of infrared thermography as an early indicator of bovine respiratory disease complex in calves [J]. Research in Veterinary Science, 2007, 83(3): 376-384.
[28]. Chaerle L., Straeten D.V.D., Seeing is believing:imaging techniques to monitor plant health [J]. Biochemical et Biophysical Acta, 2001, 1519(3): 153-166.
[29]. Chaerle L. Presymptomatic visualization of plant-virus interactions by thermography [J]. Nature Biotechnology, 1999, (17): 813-816.
[30]. Oerke E.C., Steiner U.S., Dehne H.W., et al. Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions [J]. Journal of Experimental Botany, 2006, 57(9): 2121-2132.
[31].王瑞良.驰骋田野的农业机器人[EB/OL]. http://www.sichuandaily.com.cn/1999/ sichuandaily/199909/21/html/7012.html, 1999.
[32].徐惠荣,应义斌.红外热成像在树上柑桔识别中的应用研究[J].红外与毫米波学报, 2004, 23(5): 353-355
[33]. Ridgway C., Chambeers J., Detection of insects inside wheat kernels by NIR imaging [J]. Journal of Near Infrared Spectroscopy, 1998, 6 (2): 115-191.
[34]. Meinlschmidt P., Margner V., Thermographic techniques and adopted algorithms for automatic detection of foreign bodies in food [J]. Proceedings of SPIE, 2002, (4710): 399-406.
[35]. Ginesu G., Giusto D.D., M?rgner V., et al. Detection of Foreign Bodies in Food by Thermal Image Processing [J]. IEEE Transactions on Industrial Electronics, 2004, 51(2): 480-490.
[36]. Fito P.J., Ortola M.D., Delos Reyes R. et al. Control of citrus surface drying by image analysis of infrared thermography [J]. Journal of Food Engineering, 2004, (61):287-290.
[37]. Gottschalk K., Geyer S., Hellebrand H.J., et al. Infrared Thermography for Climate Control in Big Box Potato Store [J]. Landtechnik, 2004, (2): 96-97.
[38]. Geyer S., Gottschalk K., Infrared Thermography to Monitor Natural Ventilation during Storage of Potatoes [J]. Agricultural Engineering International: The CIGR Ejournal, 2008, (4): 1-14.
[39]. Mladenov M., Damyanov C., Atanasova S. et al. Intelligent technologies for assessment of quality and safety of food agricultural products [J]. Agricultural science and technology, 2009, (1): 38-42.
[40]. Vadivambal R., Jayas D.S., Applications of Thermal Imaging in Agriculture and Food Industry—A Review [J]. Food Bioprocess Technology, Online First?, 24 February 2010.
[41]. Gowena A.A., Tiwaria B.K., Cullenb P.J., et al. Applications of thermal imaging in food quality and safety assessment[J]. Trends in Food Science & Technology, 2010, 21(4): 190-200.
[42]. Danno A., Miyazato M., Ishiguro E., Quality evaluation of agricultural Products by infrared imaging method:Ⅲ.Maturity evaluation of fruits and vegetables [J]. Memoirs of the faculty of agriculture, Kagoshima University. 1980, (16): 157-164.
[43]. Danno A., Miyazato M., Ishiguro E., Quality evaluation of agricultural Products by infraredimaging method:Ⅰ.Grading of fruits for bruise and other surface defects [J]. Memoirs of the faculty of agriculture, Kagoshima University. 1978, (14): 123-138.
[44]. Hellebrand H.J., Linke M., Beuehe H., et al. Horticultural Products Evaluated by Thermography [J].University of Warwick. 2000, (7): 26-27.
[45].徐惠荣.基于机器视觉的树上柑桔识别方法研究[D]: [硕士学位论文].浙江:浙江大学, 2004.
[46]. Wurzbach R.N., Infrared application in farming: vision of things to come [J]. Proceeding of SPIE, 2003, (5073): 154-159.
[47]. Varith J., Hyde G.M., Baritelle A.L., et al. Non-contact bruise detection in apples by thermal imaging [J]. Innovative Food Science and Emerging Technologies, 2003, (4): 211–218.
[48]. Birla S.L., Wang S., Tang J., et al. Improving heating uniformity of fresh fruit in radio frequency treatments for pest control [J]. Postharvest Biology and Technology, 2004, (33): 205-217.
[49]. Baranowski P., Lipecki J., Mazurek W., et al. Detection of apple bruises with the use of thermography [J]. Acta Agrophysica, 2005, (125): 19-29.
[50]. Veraverbeke E.A., Verboven P., Lammertyn J, et al. Thermographic surface quality evaluation of apple [J]. Journal of Food Engineering, 2006, (77): 162-168.
[51]. Baranowski P., Lipecki J., Mazurek W, et al. Detection of watercore in‘Gloster’apple using thermography [J]. Postharvest Biol. And Technol., 2008, 47(3): 358-366.
[52]. Baranowski P., Mazurek W., Detection of physiological disorders and mechanical defects in apples using thermography [J]. Int. Agrophysics, 2009, (23): 9-17.
[53]. Baranowski P., Mazurek W., Walczak B.W., et al. Detection of early apple bruises using pulsed-phase thermography [J]. Postharvest Biology and Technology, 2009, (53): 91-100.
[54]. Miller B.K., Delwiche M.J., A color vision system for peach grading [J].Trans of the ASAE, 1989, 32(4): 1484-1490.
[55]. Wen Z., Tao Y., Fuzzy-based determination of model and parameters of dual-wavelength vision system for on-line apple sorting [J]. Optical Engineering, 1998, (37): 293-29.
[56]. Tao Y., Wen Z., Adaptive spherical image transform for high-speed fruit defect detection [J]. Transaction of the ASAE, 1998, 42 (1): 241-461.
[57]. Wen Z., Tao Y., Dual-camera NIR/MIR imaging for stem-end/calyx identification in apple defect sorting [J]. Transaction of the ASAE, 2000, 43(2): 449-452.
[58]. Cheng X., Tao Y., Chen Y R, et al. NIR/MIR dual sensor machine vision system for online apple stem-end/calyx recognition [J]. Transaction of the ASAE, 2003, 46(2): 551-558.
[59].王汝琳,王咏涛.红外检测技术[M].北京,中国轻工业出版社, 2006.
[60].新浪博客.红外热像检测服务-国内最专业的技术人员给您提供帮助[EB/OL]. http://blog.sina.com.cn/s/blog_694475a50100jol5.html, 2010-7-2.
[61].张维力,陈伟星.浅析红外热成像技术[J].金卡工程, 2002, (8): 70-73.
[62].北京华泰科捷科技有限公司.红外热成像原理[EB/OL]. http://www.bj-htkj.com/ newscontent.asp?/507.html, 2010-4-23.
[63].山东泰山网.红外辐射三个基本定律[EB/OL]. http://www.sd-taishan.gov.cn/sites/ binzhou/zouping/articles/J00000/1/1489371.aspx, 2008-4-3.
[64].敏适安防.红外基本原理介绍[EB/OL]. http://www.gzhongy.com/newsdisp_A2.asp, 2008.
[65].田裕鹏.红外检测与诊断技术[M].北京,化学工业出版社, 2006.
[66].程玉兰.红外检诊断现场实用技术[M].北京,机械工业出版社, 2002.
[67].周建民,周其显.基于主动热成像技术的苹果早期机械损伤检测[J].农机化研究, 2010, 32 (8): 162-165.
[68].田裕鹏.红外辐射成像无损检测关键技术研究[D]: [博士学位论文].江苏:南京航空航天大学, 2009.
[69].杨小林,杜来林,冯立春.红外热成像检测中的主动加热方法研究[J].激光与红外, 2007, 37(11): 1188-1191.
[70].高永毅,焦群英,唐果等.水果机械损伤研究概况及发展趋势[J].湘潭师范学院学报(自然科学版), 2002, 24(3): 26-29.
[71].果树技术网.减少果品机械损伤的技术措施[EB/OL]. http://www.guoshujishu.com/Article /lishujishu/kexueguanli/3034.html.
[72].郭道峰,王家森,刘忠齐.球面热源辐射特性的研究[J].中国医学影像技术, 2002, 18(8): 830-833.
[73]. Wang S., Tang J., Cavalieri R.P., Modeling fruit internal heating rates for hot air and hot water treatments [J]. Postharvest Biology and Technology, 2001, (22): 257–270.
[74]. Rahman, S., Food Properties Handbook. New York, CRC Press, 1995.
[75]. Mohsenin, N.N., Thermal Properties of Foods and other Agricultural Materials. New York, Gordon and Breach Science Publishers, 1980.
[76]. Varith, J., Uses of thermal properties for non-destructive assessment of apple quality [D]: [Ph.D. Dissertation]. Washington State University, WA, 2001.
[77].曹铁平.化工基础[EB/OL]. http://www.xiadoc.com/doc/9611c50be9351a1efbb233daa488 edce.html, 2010-07-16.
[78].张建涛.基于红外测温技术的工业热设备内部缺陷诊断方法[D]: [硕士学位论文].重庆:重庆大学, 2008.传热分析)
[79].匡以顺.基于红外热图像信息的铜转炉炉衬蚀损状态在线测量系统研究[D]: [硕士学位论文].江西:江西理工大学, 2006.
[80]. Bulanon D.M., Burks T.F., Alchanatis V., Image fusion of visible and thermal images for fruit detection [J]. Biosystems Engineering, 2009, (103): 12-22.
[81].黄红梅.物体内部孔洞的红外无损检测[D]: [硕士学位论文].天津:天津大学, 2006.
[82].邵凡麒,红外图像处理[D]: [硕士学位论文].江苏:南京理工大学, 2005.
[83]. (美)冈萨雷斯(Gonzalez, R C.)等著,阮秋琦等译.数字图像处理(MATLAB版)[M].电子工业出版社,北京, 2005.
[84].刘莹.红外热像仪中图像增强算法的研究[D]: [硕士学位论文].吉林:吉林大学, 2007.
[85].汤慧君,丁铁英,薛书文.图像处理在脉冲加热红外热成像定量检测中的应用[J].北方交通大学学报, 1999, 23(2): 9-11.
[86].郭师虹.空域红外图像增强方法的研究[D]: [硕士学位论文].陕西:西安建筑科技大学, 2005.
[87]. Tao Y., Wen Z., An adaptive spherical image transform for high-speed fruit defect detection. Transactions of the ASAE, 1999, 42(1): 241–246.
[88].冯斌,汪懋华.计算机视觉技术识别水果缺陷的一种新方法[J].中国农业大学学报, 2002, 7(4): 73-76.
[89]. Go′mez-Sanchis J., Molto′E., Camps-Valls G., Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits [J]. Journal of Food Engineering, 2008, (85): 191-200.
[90].楼竞,吴访升,刘栋梁.灰度直方图规定化实现方法的分析[J].江苏技术师范学院学报, 2004, 10(4): 65-69.
[91].吴铁洲,熊才权.直方图匹配图像增强技术的算法研究与实现[J].湖北工业大学报, 2005,20(2): 59-61.
[92].李学明.基于Retinex理论的图像增强算法[J].计算机应用研究,2005, (2): 235-237.
[93].雷美荣,杨进华,张金泉.基于Retinex理论的红外图像的边缘增强算法[J].长春理工大学学报(自然科学版). 2008, 31(2): 11-13.
[94]. Lei L., Zhou Y.Q., Li J.W., An investigation of Retinex algorithms for image enhancement. Journal of Electron (China) 2007, 24(5): 696-700.
[95].刘莹.红外热像仪中图像增强算法的研究[D]: [硕士学位论文].吉林:吉林大学, 2007.
[96]. Jobson D.J., Rahman Z.U., Woodell G.A., Properties and performance of a center/surround retinex. IEEE Transactions on Image Processing, 1997, 6 (3): 451-462.
[97].钟建军.视频增强Retinex算法介绍[EB/OL]. http://www.casevision.net/ViewNewas.asp? SortID=9&PID=140.
[98]. Jobson D.J., Rahman Z., Woodell G.A., A Multiscale Retinex for bridging the gap between color images and the human observation of scenes [J]. IEEE Transactions on image processing, 1997, 6(7): 965-967
[99].陈兵旗,孙明. Visual C++实用图像处理[M].清华大学出版社, 2004.
[100]. Toet A. Merging thermal and visual images by a contrast pyramid [J]. Optical Engineering, 1989, 28(7): 789-79.
[101].沈宽,文玉梅,蔡玉芳.基于脊波融合的射线图像增强算法[J].计算机工程与应用, 2010, 46(23): 170-174.
[102].强赞霞.遥感图像的融合及应用[D]: [博士学位论文].湖北:华中科技大学, 2005.
[103].刘平.图像分割阈值选取技术综述[EB/OL]. http://www.ilovematlab.cn/thread-6220-1-1.html, 2008-4-16.
[104].江明,刘辉,黄欢.图像二值化技术的研究[J].软件导刊, 2009, 8(4): 175-177.
[105].徐国保,尹怡欣,王骥等.基于融合自适应形态滤波的分水岭分割新算法[J].计算机应用研究. 2009, 26(8): 3143-3145.
[106].同武勤,凌永顺,黄超超等.数学形态学和小波变换的红外图像处理方法[J].光学精密工程, 2007, 15(1): 138-144.
[107].周道炳,朱卫纲.几种边缘检测算子的评估[J].指挥技术学院学报, 2000, 11(1): 59-63.
[108].肖强.圆锥滚子轴承视觉检测研究[D]: [硕士学位论文].江西:华东交通大学, 2007.