微（小）型物体视觉测量与重构方法研究
|
摘要
随着计算机技术的迅速发展和视觉传感器成本的降低,视觉测量和重构技术依靠其非接触、高精度、准实时化的优点在制造和装备业的产品品质管控(Quality Control,QC)中发挥着越来越重要的作用。近些年来,加工制造方法和工艺的改进,带来了微(小)型产品(尺寸为2mm～100mm)的数量不断增加,相对应的视觉测量任务也逐渐增多并且提出了一些新的要求,如检测精度高、速度快、误判率低等。本文针对微(小)型物体视觉测量与重构中的难点,以几种微(小)型物体(电子连接器、医学牙齿模型、医用注射器、珠宝、小型非标零件)为研究对象,从二维和三维视觉测量两个方面出发,对微(小)型物体几何特征提取和识别、精密外形快速重构、三维特征测量、模型简化等具体问题进行了研究。论文的主要工作和创新点包括：
1.针对微(小)型电子产品的二维几何和瑕疵的视觉测量及检测需求,介绍了一种基于最小二乘和权重函数的几何特征(直线、圆、椭圆)鲁棒性提取算法,并以几何特征作为骨架信息完成了对平面瑕疵的定位和识别。
2.针对微(小)型电子产品的表面特征识别需求,提出了一种较为通用的仿射不变特征识别算法。该算法综合利用伪Zernike不变矩及SIFT匹配,能有效地解决发生旋转、缩放、平移等特征的识别和分类。
3.针对纹理缺乏、形状规则的全凸微(小)型物体快速三维重构需求,提出一种基于多视轮廓线的重构算法(Shape from closed contours,SFCC),能在极短的时间内(40sec以内)得到被测目标精密的mesh模型。该算法基于序列轮廓影像,通过初次切割和精密切割两个步骤直接恢复面模型。基于该算法,设计和装配了相应的硬件设备,以钻石和注射器针头为实验目标进行了测试,其中基于SFCC方法恢复的三维钻石模型量测得到的线元素和角元素的精度分别可达到0.3mmm,0.03°,满足钻石切工等级监控的需求。
4.针对复杂的不规则的微(小)型物体表面点云的拼接需求,提出一种基于旋转平台的快速拼接算法,该算法利用空间点云的柱面约束标定旋转平台,进而实现对任意角度点云的自动拼接,无需人工后处理和标志点约束,能很好的同各类三维扫描仪进行结合实现复杂物体的表面点云快速拼接,点云拼接精度同业类领先的拼接算法得到的结果相当(拼接误差为0.3mm)。
5.基于点云数据,推导了三维空间几何特征(平面、球、圆柱体)的拟合公式,针对平面特征以平面度的提取进行了测试,针对球和圆柱体一款非标零件进行测试,完成了该零件上的球、圆柱特征的提取。
6.基于三角网数据,提出了一种利用多方向断面线约束的最优格网简化算法,该算法能高效的对密集三角网数据进行简化,可分别输出四边形和三角形格网数据,通过同原始数据的比对,简化后的误差控制在0.04mm以内。并以口腔学科中牙模应力性分析为测量需求,将本算法生成的牙模格网数据导入三维有限元分析软件,成功的获取到应力性分析数据。
With the rapid development of computer technology and cost reduction of vision sensors, vision measurement and reconstruction technique have played an important role in the manufacturing and equipment industry based on its' advantage of non-contact, high-precision, real-time.
In recent years, with the improvements of the manufacturing methods and process,there has been an increasing number of micro (small) type products,(size2mm to100mm), and the corresponding visual measurement tasks are gradually increasing and put forward some new requirements, such as high precision, speed, and low false rate.
This thesis focuses on the difficulties in the vision measurement and reconstruction of the micro (small) type object. Several miniature objects (such as electronic connectors, medical dental model, medical syringes, jewelry, small non-standard parts etc.) had been taken as the research object from2/3-D aspects. Some specific issues have been studied in this thesis, such as:the geometric characteristics of the micro and small objects extraction and recognition, the rapid reconstruction of precision shape, three-dimensional characteristics measurement, model simplification. Major works and innovations of this thesis include:
1. For the vision measurement of the2-D geometry of the micro (small) type electronic products, a robust algorithm based on least squares and weighting function had been designed to extract the geometry features (line, circle and ellipse), meanwhile these features are taken as the skeleton base to locate the defects around.
2. For the feature recognition of the micro (small) type object, a common affine invariant feature recognition algorithm had been designed, which utilized pseudo-Zernike moment and SIFT matching to solve the identification and classification problem when the features had been rotated, zoomed and transferred.
3. For the fast reconstruction needs of the convex micro (small) type objects with less texture and rule shapes, an algorithm based on multi-view closed contours (Shape from closed contours, SFCC) had been proposed. The precision mesh model of the target can be obtained within a very short time (<40sec). In this algorithm, a sequence of contour was extracted from multi-view images, and then two-steps cutting was implemented to restore the surface model directly. In order to implement this algorithm, a serial hardware was designed and equipped. The diamonds are taken as the experimental objects. In the experiments, SFCC method can restore the3-D mesh quickly, and the distance precision can reach0.3(mm), the angle precision can reach0.03°, which can be used for the cutting grade for the diamond object.
4. For the needs of complex irregular micro (small) type surface point cloud registration, a register algorithm based on the rotating platform was proposed. In this algorithm, a cylinder constraint was used to calibrate the position of the rotating platform in the space coordinate. Then any point cloud on the rotating platform can be registered automatic using the rotate angle information, without manual post-processing or sign point constraint. This algorithm can be combined with many types of3-D scanner. Experiments show that the precision is the same as the leading point registration algorithm, which is less than0.3(mm).
5. Some geometric features (such as plane, sphere, and cylinder) had been extracted based on point cloud. Flatness was calculated using the plane feature. Sphere and cylindrical features of a non-standard work piece was extracted using related methods.
6. A simplification algorithm using multi-directional cross-section lines based on triangular data was proposed. This algorithm can generate simplified data efficiently and output quadrilateral and triangular grid data. Through comparison with the original triangular data, the simplified error was less than0.04(mm), while the compress ratio can be under10%. This algorithm has been used for the oral disciplines dental wax stress analysis.
1.针对微(小)型电子产品的二维几何和瑕疵的视觉测量及检测需求,介绍了一种基于最小二乘和权重函数的几何特征(直线、圆、椭圆)鲁棒性提取算法,并以几何特征作为骨架信息完成了对平面瑕疵的定位和识别。
2.针对微(小)型电子产品的表面特征识别需求,提出了一种较为通用的仿射不变特征识别算法。该算法综合利用伪Zernike不变矩及SIFT匹配,能有效地解决发生旋转、缩放、平移等特征的识别和分类。
3.针对纹理缺乏、形状规则的全凸微(小)型物体快速三维重构需求,提出一种基于多视轮廓线的重构算法(Shape from closed contours,SFCC),能在极短的时间内(40sec以内)得到被测目标精密的mesh模型。该算法基于序列轮廓影像,通过初次切割和精密切割两个步骤直接恢复面模型。基于该算法,设计和装配了相应的硬件设备,以钻石和注射器针头为实验目标进行了测试,其中基于SFCC方法恢复的三维钻石模型量测得到的线元素和角元素的精度分别可达到0.3mmm,0.03°,满足钻石切工等级监控的需求。
4.针对复杂的不规则的微(小)型物体表面点云的拼接需求,提出一种基于旋转平台的快速拼接算法,该算法利用空间点云的柱面约束标定旋转平台,进而实现对任意角度点云的自动拼接,无需人工后处理和标志点约束,能很好的同各类三维扫描仪进行结合实现复杂物体的表面点云快速拼接,点云拼接精度同业类领先的拼接算法得到的结果相当(拼接误差为0.3mm)。
5.基于点云数据,推导了三维空间几何特征(平面、球、圆柱体)的拟合公式,针对平面特征以平面度的提取进行了测试,针对球和圆柱体一款非标零件进行测试,完成了该零件上的球、圆柱特征的提取。
6.基于三角网数据,提出了一种利用多方向断面线约束的最优格网简化算法,该算法能高效的对密集三角网数据进行简化,可分别输出四边形和三角形格网数据,通过同原始数据的比对,简化后的误差控制在0.04mm以内。并以口腔学科中牙模应力性分析为测量需求,将本算法生成的牙模格网数据导入三维有限元分析软件,成功的获取到应力性分析数据。
With the rapid development of computer technology and cost reduction of vision sensors, vision measurement and reconstruction technique have played an important role in the manufacturing and equipment industry based on its' advantage of non-contact, high-precision, real-time.
In recent years, with the improvements of the manufacturing methods and process,there has been an increasing number of micro (small) type products,(size2mm to100mm), and the corresponding visual measurement tasks are gradually increasing and put forward some new requirements, such as high precision, speed, and low false rate.
This thesis focuses on the difficulties in the vision measurement and reconstruction of the micro (small) type object. Several miniature objects (such as electronic connectors, medical dental model, medical syringes, jewelry, small non-standard parts etc.) had been taken as the research object from2/3-D aspects. Some specific issues have been studied in this thesis, such as:the geometric characteristics of the micro and small objects extraction and recognition, the rapid reconstruction of precision shape, three-dimensional characteristics measurement, model simplification. Major works and innovations of this thesis include:
1. For the vision measurement of the2-D geometry of the micro (small) type electronic products, a robust algorithm based on least squares and weighting function had been designed to extract the geometry features (line, circle and ellipse), meanwhile these features are taken as the skeleton base to locate the defects around.
2. For the feature recognition of the micro (small) type object, a common affine invariant feature recognition algorithm had been designed, which utilized pseudo-Zernike moment and SIFT matching to solve the identification and classification problem when the features had been rotated, zoomed and transferred.
3. For the fast reconstruction needs of the convex micro (small) type objects with less texture and rule shapes, an algorithm based on multi-view closed contours (Shape from closed contours, SFCC) had been proposed. The precision mesh model of the target can be obtained within a very short time (<40sec). In this algorithm, a sequence of contour was extracted from multi-view images, and then two-steps cutting was implemented to restore the surface model directly. In order to implement this algorithm, a serial hardware was designed and equipped. The diamonds are taken as the experimental objects. In the experiments, SFCC method can restore the3-D mesh quickly, and the distance precision can reach0.3(mm), the angle precision can reach0.03°, which can be used for the cutting grade for the diamond object.
4. For the needs of complex irregular micro (small) type surface point cloud registration, a register algorithm based on the rotating platform was proposed. In this algorithm, a cylinder constraint was used to calibrate the position of the rotating platform in the space coordinate. Then any point cloud on the rotating platform can be registered automatic using the rotate angle information, without manual post-processing or sign point constraint. This algorithm can be combined with many types of3-D scanner. Experiments show that the precision is the same as the leading point registration algorithm, which is less than0.3(mm).
5. Some geometric features (such as plane, sphere, and cylinder) had been extracted based on point cloud. Flatness was calculated using the plane feature. Sphere and cylindrical features of a non-standard work piece was extracted using related methods.
6. A simplification algorithm using multi-directional cross-section lines based on triangular data was proposed. This algorithm can generate simplified data efficiently and output quadrilateral and triangular grid data. Through comparison with the original triangular data, the simplified error was less than0.04(mm), while the compress ratio can be under10%. This algorithm has been used for the oral disciplines dental wax stress analysis.
引文
[1]Golnabi H, Asadpour A. Design and application of industrial machine vision systems [J]. Robotics and Computer-Integrated Manufacturing,2007,23(6):630-637.
[2]McGray H, Hammill A, Bradley R, Schipper EL, Parry J-E. Weathering the storm: options for framing adaptation and development [J]. World Resources Institute, Washington DC,2007,57-67.
[3]范生宏.工业数字摄影测量中人工标志的研究与应用[D].郑州：信息工程大学,2006.
[4]冯其强.数字工业摄影测量技术研究与实践[D].郑州：解放军信息工程大学,2010.
[5]姚立新,张武学,连军莉.AOI系统在PCB中的应用[J].电子工业专用设备,2004,33(5)：25-33.
[6]杨冀丰,史建卫,王乐,白英奎,钱乙余.AOI在SMT中的应用[J].电子工业专用设备,2006,35(11)：56-63.
[7]Forsyth DA, Ponce J,林学阉,王宏.计算机视觉——一种现代方法[M].北京：电子工业出版社,2004：325.
[8]黄文清,汪亚明,周志宇.计算机视觉技术在工业领域中的应用[J].浙江工程学院学报,2002,19(2)：92-98.
[9]孙慧贤.基于纹理分析的视觉检测方法与应用研究[D].长沙：国防科学技术大学,2010.
[10]刘良江.先进电子制造生产线机器视觉检测方法与技术研究[D].长沙：湖南大学,2010.
[11]叶声华,邾继贵,王仲,杨学友.视觉检测技术及应用[J].中国工程科学,1999,1(1)：262-265.
[12]冯文灏.工业测量[M].武汉：武汉大学出版社,2004：135.
[13]Chin RT, Harlow CA. Automated visual inspection:A survey [J]. Pattern Analysis and Machine Intelligence,1982,4(6):557-573.
[14]Keyence.基恩士官方网站[EB/OL].:china.keyence.com/company/index.php,2012.
[15]Cognex康耐视官方网站[EB/OL].:www.cognex.cn,2012.
[16]NI. NI官网网站[EB/OL].:www.ni.com/vision/zhs/,2012.
[17]Halcon. Halcon官方网站[EB/OL].:www.halcon-embedded.com,2012.
[18]Steger C, Ulrich M, Wiedemann C.机器视觉算法与应用[M].北京：清华大学出版社,2008：6.
[19]Malamas EN, Petrakis EG, Zervakis M, et al.. A survey on industrial vision systems, applications and tools [J]. Image and vision computing,2003,21(2): 171-188.
[20]Hunsicker RJ, Patten J, Ledford A, et al.. Automatic vision inspection and measurement system for external screw threads [J]. Journal of Manufacturing Systems, 1994,13(5):370-384.
[21]Magee M, Seida S. An industrial model based computer vision system [J]. Journal of Manufacturing Systems,1995,14(3):169-186.
[22]Jimenez A, Ceres R, Pons J. A vision system based on a laser range-finder applied to robotic fruit harvesting [J]. Machine Vision and Applications,2000,11(6): 321-339.
[23]Gadelmawla E. Computer vision algorithms for measurement and inspection of spur gears [J]. Measurement,2011,44(9):1669-1678.
[24]Andersson T, Thurley MJ, Carlson JE. A machine vision system for estimation of size distributions by weight of limestone particles [J]. Minerals Engineering,2012,25(1):38-46.
[25]Ker J-I, Wang Y, Li M-N. A migration strategy for automating a vision-based concentricity inspection station [J]. Robotics and Computer-Integrated Manufacturing,2012,29(3):88-94.
[26]Labuguen R, Volante E, Causo A, et al. Automated fish fry counting and schooling behavior analysis using computer vision[A]. In:IEEE. Signal Processing and its Applications[C].2012:255-260.
[27]Jahanshahi MR, Masri SF. Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures [J]. Automation in Construction,2012,22:567-576.
[28]Aydogmus 0, Talu MF. A vision-based measurement installation for programmable logic controllers [J]. Measurement,2012,45(5):1098-1104.
[29]Cootes T, Page G, Jackson C, Taylor C. Statistical grey-level models for object location and identification [J]. Image and Vision Computing,1996,14(8):533-540.
[30]Bahlmann C, Heidemann G, Ritter H. Artificial neural networks for automated quality control of textile seams [J]. Pattern recognition,1999,32(6):1049-1060.
[31]Tsai D-M, Chen J-J, Chen J-F. A vision system for surface roughness assessment using neural networks [J]. The International Journal of Advanced Manufacturing Technology,1998,14(6):412-422.
[32]Tsai D-M, Hsieh C-Y. Automated surface inspection for directional textures [J]. Image and Vision Computing,1999,18(1):49-62.
[33]Larrain R, Schaefer D, Reed J. Use of digital images to estimate CIE color coordinates of beef [J]. Food Research International,2008,41(4):380-385.
[34]Sun X, Chen K, Berg E, Magolski J. Predicting fresh beef color grade using machine vision imaging and support vector machine (SVM) analysis [J]. Journal of Animal and Veterinary Advances,2011,10(12):1504-1511.
[35]Lu J, Tan J, Shatadal P, Gerrard D. Evaluation of pork color by using computer vision [J]. Meat Science,2000,56(1):57-60.
[36]Tan F, Morgan M, Ludas L, Forrest J, Gerrard D. Assessment of fresh pork color with color machine vision [J]. Journal of animal science,2000,78(12):3078-3085.
[37]0'sullivan M, Byrne D, Martens H, et al. Evaluation of pork colour:prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis [J]. Meat Science,2003,65(2):909-918.
[38]Yagiz Y, Balaban MO, Kristinsson HG, et al. Comparison of Minolta colorimeter and machine vision system in measuring colour of irradiated Atlantic salmon [J]. Journal of the Science of Food and Agriculture,2009,89(4):728-730.
[39]Quevedo R, Aguilera J, Pedreschi F. Color of salmon fillets by computer vision and sensory panel [J]. Food and Bioprocess Technology,2010,3(5):637-643.
[40]Misimi E, Mathiassen J, Erikson U. Computer Vision-Based Sorting of Atlantic Salmon (Salmo salar) Fillets According to Their Color Level [J]. Journal of food science,2007,72(1):S030-S035.
[41]FERNANDEZ-VAZQUEZ R, STINCO CM, et al. Visual and instrumental evaluation of orange juice color:A consumers'preferenc study [J]. Journal of Sensory Studies,2011,26(6):436-444.
[42]Martin MLG-M, Ji W, Luo R, et al. Measuring colour appearance of red wines [J]. Food quality and preference,2007,18(6):862-871.
[43]Michalos G, Makris S, Eytan A, et al. Robot Path Correction Using Stereo Vision System [J]. Procedia CIRP 2012,3:352-357.
[44]Jeng J, Mau T, Leu S. Gap inspection and alignment using a vision technique for laser butt joint welding [J]. The International Journal of Advanced Manufacturing Technology,2000,16(3):212-216.
[45]Moreira M, Fiesler E, Pante G. Image classification for the quality control of watches [J]. Journal of Intelligent and Fuzzy Systems,1999,7(2):151-158.
[46]Ahmad R, Tichadou S, Hascoet J-Y. New computer vision based Snakes and Ladders algorithm for the safe trajectory of two axis CNC machines [J]. Computer-Aided Design, 2012,44(5):355-366.
[47]Cappelleri DJ, Piazza G, Kumar V. A two dimensional vision-based force sensor for microrobotic applications [J]. Sensors and Actuators A:Physical,2011,171(2): 340-351.
[48]张进.微型零件高精度影像测量系统中关键技术研究[D].天津：天津大学,2010.
[49]刘斌.微小三维尺寸自动光学检测系统的关键技术研究[D].天津：天津大学,2010.
[50]唐启敬,赵铁石,边辉,等.基于双目视觉的复合肥颗粒检测系统[J].农业机械学报,2012,43(7)：206-211.
[51]江健武,刘亚文,唐敏,等.基于线结构光视觉方法的绝缘子防污参数测量[J].测绘信息与工程,2012,37(4)：41-52.
[52]赵勇.基于激光图像的轮对踏面磨耗动态测量[J].激光与红外,2012,42(7)：743-749.
[53]吴彰良,刘洁,孙长库.基于计算机视觉的油封尺寸检测系统设计[J].计算机测量与控制,2012,20(5)：1196-1204.
[54]陈曼龙.基于机器视觉的锥螺纹参数测量方法[J].计算机测量与控制,2012,20(5)：1166-1173.
[55]杨莉,潘丰.基于机器视觉的硬盘磁体检测系统设计[J].江南大学学报(自然科学版,2012,11(5)：505-508.
[56]王辉,雷雨春,康峰,等.基于机器视觉的樱桃外径检测[J].农业机械学报,2012,S1：246-249.
[57]范小虎,朱目成,聂诗良.发动机叶尖间隙影像测量系统的设计[J].应用光学,2012,33(4)：743-746.
[58]赵文辉,段振云,赵文珍,等.基于机器视觉的微米级2D零件自动检测系统[J].组合机床与自动化加工技术,2012,(9)：52-55.
[59]韦佳佳.名优茶机械化采摘中嫩芽识别方法的研究[D]：南京：南京林业大学,2012.
[60]张远辉,韦巍.在线角速度估计的乒乓球机器人视觉测量方法[J].浙江大学学报(工学版),2012,07：1320-1326.
[61]张爱华,李金平,林冬梅.基于视觉测量的脉搏图像三维重构[J].数据采集与处理,2012,05：570-575.
[62]王文静,徐建瑜,吕志敏,等.基于机器视觉的水下鲆鲽鱼类质量估计[J].农业工程学报,2012,16：153-157.
[63]张小锋,徐鸿钧,吴琦,等.基于双目图像的砂轮地貌检测[J].仪器仪表学报,2006，S3：2088-2089.
[64]孔凡芝.引线键合视觉检测关键技术研究[D].哈尔滨：哈尔滨工程大学,2009.
[65]熊俊涛,邹湘军,陈丽娟,等.荔枝采摘机械手果实识别与定位技术[J].江苏大学学报(自然科学版),2012,01：1-5.
[66]毕听.面向TFT-LCD制程的Mura缺陷机器视觉检测方法研究[D].上海：上海交通大学,2009.
[67]栗琳,王仲,蔡振兴,等.基于目标轮廓的附着物定位与剔除方法[J].光电工程,2012,39(5)：45-51.
[68]张艳辉,金翠云,王颖.基于圆结构光视觉三维点的孔洞缺陷识别及重构[J].北京化工大学学报(自然科学版),2012,05：113-117.
[69]董浩,荆熠,王锦平,等.基于机器视觉技术的烟用包装膜磨损程度测定方法[J].烟草科技,2012,07：9-12.
[70]张亚军,齐杏林,董成基.基于机器视觉的枪弹钢芯自动检测系统[J].兵工自动化,2012：31(7)：8-11.
[71]杨建国,肖蓉,李蓓智,等.基于机器视觉的刀具磨损检测技术[J].东华大学学报(自然科学版),2012,5：505-508+518.
[72]郭轩.彩色印刷品视觉检测相关算法研究[D].武汉：华中科技大学,2010.
[73]张习文,王晓东,罗怡,等.跨尺度微小型零件的测量与装配[J].纳米技术与精密工程,2012,10(004)：342-347.
[74]吴翔.面向再制造的机器人三维视觉检测系统研究[D].大连：大连海事大学,2011.
[75]王晓东,宋洪侠,刘超,等.基于机器视觉的微小型零件测量与装配控制[J].哈尔滨：哈尔滨工程大学学报,2011,32(9)：1117-1122.
[76]于忠党.基于支持向量机的机械零件计算机视觉检测若干关键技术的研究[D].长春：吉林大学,2009.
[77]吴俊芳.小尺寸视觉二维精密检测理论与方法[D].广州：华南理工大学,2011.
[78]黄星奕,李剑,姜松.基于计算机视觉的稻谷品种识别技术的研究[J].江苏大学学报(自然科学版),2004,02：102-104.
[79]吴继华,刘燕德,欧阳爱国.基于机器视觉的种子品种实时检测系统研究[J].传感技术学报,2005,18(4)：742-746.
[80]喻擎苍,严红滨.一种基于图像轮廓线的稻种模糊模式识别方法[J].农业工程学报2002,18(1),150-153.
[81]黄星奕,吴守一,方如明.计算机视觉在大米胚芽识别中的应用[J].农业机械学报,2000,31(1)：62-65.
[82]黄星奕,吴守一,方如明.用神经网络方法进行大米留胚率自动检测的研究[J].农业工程学报,1999,04：187-191.
[83]许俐.机器视觉测量大米留胚率的研究[J].江苏理工大学学报(自然科学版),1997,18(6)：8-11.
[84]黄星奕,吴守一,方如明.基于小波变换的稻米爆腰检测技术研究[J].农业工程学报,2004：20(6)：194-196.
[85]张家年,程秋琼,谭军.垩白对稻米吸湿产生裂纹的影响[J].中国粮油学报,2001,16(1)：40-42.
[86]尚艳芬,侯彩云,常国华.基于图像识别的黄粒米自动检测研究[J].农业工程学报,2004,20(4)：146-148.
[87]孙明,王一鸣,凌云,等.基于色调的黄粒米检测方法[J].农业机械学报,2005,36(8)：78-81.
[88]吴彦红,刘木华,杨君,等.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.
[89]Neugebauer PJ. Geometrical cloning of 3D objects via simultaneous registration of multiple range images [C]. Shape Modeling and Applications,1997 Proceedings, 1997 International Conference on; 1997:130-139.
[90]张舜德,卢秉恒,丁玉成.光学三维形面分区域测量数据的拼接研究[J].中国激光,2001，06：533-536.
[91]刘德连,张建奇.基于3点匹配的图像拼接算法[J].计算机工程,2006,13：203-205.
[92]Besl PJ, McKay ND. Method for registration of 3-D shapes [C]. Robotics-DL tentative; 1992:International Society for Optics and Photonics,586-606.
[93]Roberts LG. Machine perception of three-dimensional solids[R]:DTIC Document, 1963.
[94]Sobel I. Camera models and machine perception [R]:DTIC Document,1970.
[95]Canny J. A computational approach to edge detection [J]. Pattern Analysis and Machine Intelligence,1986, (6):679-698.
[96]雒涛,郑喜凤,丁铁夫.改进的自适应阈值Canny边缘检测[J].光电工程,2009,36(11)：106-111.
[97]陈宏希.基于边缘保持平滑滤波的Canny算子边缘检测[J].兰州交通大学学报,2006,01：86-90.
[98]丘维声.解析几何[M].北京：北京大学出版社,1988：330.
[99]HuM-K. Visual pattern recognition by moment invariants [J]. Information Theory, 1962,8(2):179-187.
[100]Zernike vF. Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode [J]. Physica,1934,1(7):689-704.
[101]Chong C-W, Raveendran P, Mukundan R. An efficient algorithm for fast computation of pseudo-Zernike moments [J]. International journal of pattern recognition and artificial intelligence,2003,17(06):1011-1123.
[102]Shen D, Ip HH. Discriminative wavelet shape descriptors for recognition of 2-D patterns [J]. Pattern recognition,1999,32(2):151-166.
[103]Teh C-H, Chin RT. On image analysis by the methods of moments [J]. Pattern Analysis and Machine Intelligence,1988,10(4):496-513.
[104]Khotanzad A, Hong YH. Invariant image recognition by Zernike moments [J]. Pattern Analysis and Machine Intelligence,1990,12(5):489-497.
[105]夏婷,周卫平,李松毅,等.一种新的Pseudo-Zernike矩的快速算法[J].电子学报,2005,33(7)：1295-1303.
[106]Lowe DG. Distinctive image features from scale-invariant keypoints [J]. International journal of computer vision,2004,60(2):91-110.
[107]Chien C, Aggarwal J. Identification of 3D objects from multiple silhouettes using quadtrees/octrees [J]. Computer Vision, Graphics, and Image Processing,1986, 36(2):256-273.
[108]Yemez Y, Sahillioglu Y. Shape from silhouette using topology-adaptive mesh deformation [J]. Pattern Recognition Letters,2009,30(13):1198-1207.
[109]Hernandez Esteban C, Schmitt F. Multi-stereo 3d object reconstruction [C]. 3D Data Processing Visualization and Transmission,2002 Proceedings First International Symposium on,2002,159-66.
[110]Matsumoto Y, Terasaki H, Sugimoto K, et al. A portable three-dimensional digitizer [C].3-D Digital Imaging and Modeling,1997 Proceedings, International Conference on Recent Advances in; 1997,197-204.
[111]Niem W. Automatic reconstruction of 3D objects using a mobile camera [J]. Image and Vision Computing,1999,17(2):125-134.
[112]Schmitt F, Yemez Y.3d color object reconstruction from 2d image sequences [C]. Image Processing,1999 ICIP 99 Proceedings 1999 International Conference on; 1999,65-69.
[113]Dyer CR. Volumetric scene reconstruction from multiple views [J]. Foundations of Image Understanding,2001,628:469-489.
[114]Slabaugh G, Schaf er R, Hans M. Image-based photo hulls [C].3D Data Processing Visualization and Transmission,2002 Proceedings First International Symposium on; 2002,704-862.
[115]Lorensen WE, Cline HE. Marching cubes:A high resolution 3D surface construction algorithm [C]. ACM Siggraph Computer Graphics; 1987,163-169.
[116]Montani C, Scateni R, Scopigno R. Discretized marching cubes [C]. Proceedings of the conference on Visualization'94; 1994:IEEE Computer Society Press; 1994, 281-287.
[117]Laurentini A. The visual hull concept for silhouette-based image understanding [J]. Pattern Analysis and Machine Intelligence,1994,16(2):150-162.
[118]Franco J-S, Lapierre M, Boyer E. Visual shapes of silhouette sets [C].3D Data Processing, Visualization, and Transmission, Third International Symposium on; 2006,397-404.
[119]Cheung GK, Baker S, Kanade T. Visual hull alignment and refinement across time: A 3d reconstruction algorithm combining shape-from-silhouette with stereo [C]. Computer Vision and Pattern Recognition,2003 Proceedings 2003 IEEE Computer Society Conference on; 2003,375-382.
[120]Matsumoto Y, Fujimura K, Kitamura T. Shape-from-silhouette/stereo and its application to 3-d digitizer [C]. Discrete Geometry for Computer Imagery; 1999: Springer; 1999,177-188.
[121]Lin H-Y, Wu J-R.3d reconstruction by combining shape from silhouette with stereo [C]. Pattern Recognition,2008 ICPR 2008 19th International Conference on; 2008,1-4.
[122]Seitz SM, Dyer CR. Photorealistic scene reconstruction by voxel coloring [J]. International Journal of Computer Vision,1999,35(2):151-173.
[123]Kutulakos KN, Seitz SM. A theory of shape by space carving [J]. International Journal of Computer Vision,2000,38(3):199-218.
[124]Mulayim AY, Yilmaz U, Atalay V. Silhouette-based 3-D model reconstruction from multiple images [J]. Systems, Man, and Cybernetics, Part B:Cybernetics,2003,33(4): 582-591.
[125]张剑清,苏国中,郑顺义,等.基于线扩散函数的高精度轮廓特征提取算法[J].武汉大学学报(信息科学版),2006,31(2)：95-104.
[126]李彩林,郭宝云,李畅.目标轮廓直线特征的高精度提取[J].测绘学报,2011,40(1)：66-70.
[127]Zheng S, Li C, Su G, Zhang J. High-accurate line feature extraction algorithm based on line diffusion function model [C]. International Symposium on Multispectral Image Processing and Pattern Recognition; 2007:International Society for Optics and Photonics; 2007,678-683.
[128]韩鹏,龚健雅,李志林,等.遥感影像空间尺度上推方法的评价[J].遥感学报,2008,12(6)：964-971.
[129]张丽,王昱.一种快速测定卫星像片分辨率的方法[J].解放军测绘研究所学报,2002,22(1)：50-52.
[130]Cheung K, Baker S, Kanade T. Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture [C]. Computer Vision and Pattern Recognition,2003 Proceedings 2003 IEEE Computer Society Conference on; 2003,77-84.
[131]Dyer CR. Volumetric scene reconstruction from multiple views. Foundations of Image Understanding[M]:America:Springer,2001:469-489.
[132]徐颖欣,李醒飞,朱嘉.远心镜头在回转体测量系统中的应用[J].传感器与微系统,2010,(1)：111-113.
[133]曲直.基于数字图像处理技术的钻石切工比例测定系统的研究和应用[D].武汉：中国地质大学,2005.
[134]Fremont V, Chellali R. Turntable-based 3D object reconstruction [C]. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems; 2004,1276-1281.
[135]Beardsley P, Torr P, Zisserman A.3D model acquisition from extended image sequences[J]. Computer Vision,1996:683-695.
[136]Fitzgibbon AW, Cross G, Zisserman A. Automatic 3D model construction for turn-table sequences[A].3D Structure from Multiple Images of Large-Scale Environments[M]. Springer,1998:155-70.
[137]Eisert P, Steinbach E, Girod B. Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views [J]. Circuits and Systems for Video Technology,2000,10(2):261-277.
[138]Otsu N. A threshold selection method from gray-level histograms [J]. Automatica,1975,11(285-296):23-27.
[139]熊邦书,余亮,欧巧凤,等.注射器针头合格性图像检测方法[J].中国生物医学工程学报,2010,29(131)：627-631.
[140]Hu G, Stockman G.3-D surface solution using structured light and constraint propagation[J]. Pattern Analysis and Machine Intelligence,1989,11(4):390-402.
[141]Zou D, Ye S, Wang C. Structured-lighting surface sensor and its calibration [J]. Optical Engineering,1995,34(10):3040-3043.
[142]Zhang L, Curless B, Seitz SM. Rapid shape acquisition using color structured light and multi-pass dynamic programming [C].3D Data Processing Visualization and Transmission,2002 Proceedings First International Symposium on; 2002:24-36.
[143]Bergmann D. New approach for automatic surface reconstruction with coded light[C]. PROCEEDINGS-SPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING; 1995:SPIE INTERNATIONAL SOCIETY FOR OPTICAL; 1995:2-8.
[144]Sato K. Range imaging based on moving pattern light and spatio-temporal matched filter [C]. Image Processing,1996 Proceedings, International Conference on; 1996: IEEE; 1996:33-36.
[145]Sansoni G, Corini S, Lazzari S, Rodella R, Docchio F. Three-dimensional imaging based on Gray-code light projection:characterization of the measuring algorithm and development of a measuring system for industrial applications [J]. Applied Optics,1997,36(19):4463-4472.
[146]魏媛.用编码结构光方法实现的物体三维重构[D].大连：大连理工大学,2005.
[147]于晓洋,吴海滨.基于Gray码和线移条纹的结构光3D测量技术[J].仪器仪表学报,2008,29(4)：701-704.
[148]郑顺义,胡华亮,徐轩.基于结构光和极线约束的三维重建[J].信息化纵横,2009,29(8)：48-51.
[149]张永军,张祖勋,张剑清.利用二维DLT及光束法平差进行数字摄像机标定[J].武汉大学学报(信息科学版),2002,27(6)：566-571.
[150]岳慧敏.基于时间相位展开的三维轮廓测量研究[D].成都：四川大学,2005.
[151]李德仁,周月琴,金为铣.摄影测量与遥感概论[M].测绘出版社;2001：45.
[152]冯文灏.近景摄影测量[M].武汉大学出版社;2002：238.
[153]戴静兰,陈志杨,叶修梓.ICP算法在点云配准中的应用[J].中国图象图形学报,2007,12(3)：517-521.
[154]马扬飚,钟约先,郑聆,等.三维数据拼接中编码标志点的设计与检测[J].清华大学学报(自然科学版),2006,02：169-171+175.
[155]Gelfand N, Ikemoto L, Rusinkiewicz S, Levoy M. Geometrically stable sampling for the ICP algorithm [C].3-D Digital Imaging and Modeling,2003 3DIM 2003 Proceedings Fourth International Conference on; 2003:IEEE; 2003:260-267.
[156]张艳丽,李晓星,杨兴,等.小型零件测量的旋转拼接[J].航天制造技术,2005，01：32-35.
[157]龙玺,钟约先,李仁举,等.结构光三维扫描测量的三维拼接技术[J].清华大学学报(自然科学版),2002,42(4)：477-480.
[158]Seitz SM, Curless B, Diebel J, Scharstein D, Szeliski R. A comparison and evaluation of multi-view stereo reconstruction algorithms [C]. Computer Vision and Pattern Recognition,2006 IEEE Computer Society Conference on; 2006:IEEE; 2006: 519-528.
[159]吴福朝.计算机视觉中的数学方法[M].北京：科学出版社,2008：283.
[160]林志雄,乐凯,胡华亮,等.结构光三维测量系统的点云精度分析[J].武汉理工大学学报,2011,33(9)：160-164.
[161]Stearns MB. Probability distribution of a network of triangles [J]. SIAM Review,1967,9(3):591-603.
[162]Lee D-T, Schachter BJ. Two algorithms for constructing a Delaunay triangulation [J]. International Journal of Computer & Information Sciences,1980, 9(3):219-242.
[163]Brassel KE, Reif D. A procedure to generate Thiessen polygons [J]. Geographical Analysis,1979,11(3):289-303.
[2]McGray H, Hammill A, Bradley R, Schipper EL, Parry J-E. Weathering the storm: options for framing adaptation and development [J]. World Resources Institute, Washington DC,2007,57-67.
[3]范生宏.工业数字摄影测量中人工标志的研究与应用[D].郑州：信息工程大学,2006.
[4]冯其强.数字工业摄影测量技术研究与实践[D].郑州：解放军信息工程大学,2010.
[5]姚立新,张武学,连军莉.AOI系统在PCB中的应用[J].电子工业专用设备,2004,33(5)：25-33.
[6]杨冀丰,史建卫,王乐,白英奎,钱乙余.AOI在SMT中的应用[J].电子工业专用设备,2006,35(11)：56-63.
[7]Forsyth DA, Ponce J,林学阉,王宏.计算机视觉——一种现代方法[M].北京：电子工业出版社,2004：325.
[8]黄文清,汪亚明,周志宇.计算机视觉技术在工业领域中的应用[J].浙江工程学院学报,2002,19(2)：92-98.
[9]孙慧贤.基于纹理分析的视觉检测方法与应用研究[D].长沙：国防科学技术大学,2010.
[10]刘良江.先进电子制造生产线机器视觉检测方法与技术研究[D].长沙：湖南大学,2010.
[11]叶声华,邾继贵,王仲,杨学友.视觉检测技术及应用[J].中国工程科学,1999,1(1)：262-265.
[12]冯文灏.工业测量[M].武汉：武汉大学出版社,2004：135.
[13]Chin RT, Harlow CA. Automated visual inspection:A survey [J]. Pattern Analysis and Machine Intelligence,1982,4(6):557-573.
[14]Keyence.基恩士官方网站[EB/OL].:china.keyence.com/company/index.php,2012.
[15]Cognex康耐视官方网站[EB/OL].:www.cognex.cn,2012.
[16]NI. NI官网网站[EB/OL].:www.ni.com/vision/zhs/,2012.
[17]Halcon. Halcon官方网站[EB/OL].:www.halcon-embedded.com,2012.
[18]Steger C, Ulrich M, Wiedemann C.机器视觉算法与应用[M].北京：清华大学出版社,2008：6.
[19]Malamas EN, Petrakis EG, Zervakis M, et al.. A survey on industrial vision systems, applications and tools [J]. Image and vision computing,2003,21(2): 171-188.
[20]Hunsicker RJ, Patten J, Ledford A, et al.. Automatic vision inspection and measurement system for external screw threads [J]. Journal of Manufacturing Systems, 1994,13(5):370-384.
[21]Magee M, Seida S. An industrial model based computer vision system [J]. Journal of Manufacturing Systems,1995,14(3):169-186.
[22]Jimenez A, Ceres R, Pons J. A vision system based on a laser range-finder applied to robotic fruit harvesting [J]. Machine Vision and Applications,2000,11(6): 321-339.
[23]Gadelmawla E. Computer vision algorithms for measurement and inspection of spur gears [J]. Measurement,2011,44(9):1669-1678.
[24]Andersson T, Thurley MJ, Carlson JE. A machine vision system for estimation of size distributions by weight of limestone particles [J]. Minerals Engineering,2012,25(1):38-46.
[25]Ker J-I, Wang Y, Li M-N. A migration strategy for automating a vision-based concentricity inspection station [J]. Robotics and Computer-Integrated Manufacturing,2012,29(3):88-94.
[26]Labuguen R, Volante E, Causo A, et al. Automated fish fry counting and schooling behavior analysis using computer vision[A]. In:IEEE. Signal Processing and its Applications[C].2012:255-260.
[27]Jahanshahi MR, Masri SF. Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures [J]. Automation in Construction,2012,22:567-576.
[28]Aydogmus 0, Talu MF. A vision-based measurement installation for programmable logic controllers [J]. Measurement,2012,45(5):1098-1104.
[29]Cootes T, Page G, Jackson C, Taylor C. Statistical grey-level models for object location and identification [J]. Image and Vision Computing,1996,14(8):533-540.
[30]Bahlmann C, Heidemann G, Ritter H. Artificial neural networks for automated quality control of textile seams [J]. Pattern recognition,1999,32(6):1049-1060.
[31]Tsai D-M, Chen J-J, Chen J-F. A vision system for surface roughness assessment using neural networks [J]. The International Journal of Advanced Manufacturing Technology,1998,14(6):412-422.
[32]Tsai D-M, Hsieh C-Y. Automated surface inspection for directional textures [J]. Image and Vision Computing,1999,18(1):49-62.
[33]Larrain R, Schaefer D, Reed J. Use of digital images to estimate CIE color coordinates of beef [J]. Food Research International,2008,41(4):380-385.
[34]Sun X, Chen K, Berg E, Magolski J. Predicting fresh beef color grade using machine vision imaging and support vector machine (SVM) analysis [J]. Journal of Animal and Veterinary Advances,2011,10(12):1504-1511.
[35]Lu J, Tan J, Shatadal P, Gerrard D. Evaluation of pork color by using computer vision [J]. Meat Science,2000,56(1):57-60.
[36]Tan F, Morgan M, Ludas L, Forrest J, Gerrard D. Assessment of fresh pork color with color machine vision [J]. Journal of animal science,2000,78(12):3078-3085.
[37]0'sullivan M, Byrne D, Martens H, et al. Evaluation of pork colour:prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis [J]. Meat Science,2003,65(2):909-918.
[38]Yagiz Y, Balaban MO, Kristinsson HG, et al. Comparison of Minolta colorimeter and machine vision system in measuring colour of irradiated Atlantic salmon [J]. Journal of the Science of Food and Agriculture,2009,89(4):728-730.
[39]Quevedo R, Aguilera J, Pedreschi F. Color of salmon fillets by computer vision and sensory panel [J]. Food and Bioprocess Technology,2010,3(5):637-643.
[40]Misimi E, Mathiassen J, Erikson U. Computer Vision-Based Sorting of Atlantic Salmon (Salmo salar) Fillets According to Their Color Level [J]. Journal of food science,2007,72(1):S030-S035.
[41]FERNANDEZ-VAZQUEZ R, STINCO CM, et al. Visual and instrumental evaluation of orange juice color:A consumers'preferenc study [J]. Journal of Sensory Studies,2011,26(6):436-444.
[42]Martin MLG-M, Ji W, Luo R, et al. Measuring colour appearance of red wines [J]. Food quality and preference,2007,18(6):862-871.
[43]Michalos G, Makris S, Eytan A, et al. Robot Path Correction Using Stereo Vision System [J]. Procedia CIRP 2012,3:352-357.
[44]Jeng J, Mau T, Leu S. Gap inspection and alignment using a vision technique for laser butt joint welding [J]. The International Journal of Advanced Manufacturing Technology,2000,16(3):212-216.
[45]Moreira M, Fiesler E, Pante G. Image classification for the quality control of watches [J]. Journal of Intelligent and Fuzzy Systems,1999,7(2):151-158.
[46]Ahmad R, Tichadou S, Hascoet J-Y. New computer vision based Snakes and Ladders algorithm for the safe trajectory of two axis CNC machines [J]. Computer-Aided Design, 2012,44(5):355-366.
[47]Cappelleri DJ, Piazza G, Kumar V. A two dimensional vision-based force sensor for microrobotic applications [J]. Sensors and Actuators A:Physical,2011,171(2): 340-351.
[48]张进.微型零件高精度影像测量系统中关键技术研究[D].天津：天津大学,2010.
[49]刘斌.微小三维尺寸自动光学检测系统的关键技术研究[D].天津：天津大学,2010.
[50]唐启敬,赵铁石,边辉,等.基于双目视觉的复合肥颗粒检测系统[J].农业机械学报,2012,43(7)：206-211.
[51]江健武,刘亚文,唐敏,等.基于线结构光视觉方法的绝缘子防污参数测量[J].测绘信息与工程,2012,37(4)：41-52.
[52]赵勇.基于激光图像的轮对踏面磨耗动态测量[J].激光与红外,2012,42(7)：743-749.
[53]吴彰良,刘洁,孙长库.基于计算机视觉的油封尺寸检测系统设计[J].计算机测量与控制,2012,20(5)：1196-1204.
[54]陈曼龙.基于机器视觉的锥螺纹参数测量方法[J].计算机测量与控制,2012,20(5)：1166-1173.
[55]杨莉,潘丰.基于机器视觉的硬盘磁体检测系统设计[J].江南大学学报(自然科学版,2012,11(5)：505-508.
[56]王辉,雷雨春,康峰,等.基于机器视觉的樱桃外径检测[J].农业机械学报,2012,S1：246-249.
[57]范小虎,朱目成,聂诗良.发动机叶尖间隙影像测量系统的设计[J].应用光学,2012,33(4)：743-746.
[58]赵文辉,段振云,赵文珍,等.基于机器视觉的微米级2D零件自动检测系统[J].组合机床与自动化加工技术,2012,(9)：52-55.
[59]韦佳佳.名优茶机械化采摘中嫩芽识别方法的研究[D]：南京：南京林业大学,2012.
[60]张远辉,韦巍.在线角速度估计的乒乓球机器人视觉测量方法[J].浙江大学学报(工学版),2012,07：1320-1326.
[61]张爱华,李金平,林冬梅.基于视觉测量的脉搏图像三维重构[J].数据采集与处理,2012,05：570-575.
[62]王文静,徐建瑜,吕志敏,等.基于机器视觉的水下鲆鲽鱼类质量估计[J].农业工程学报,2012,16：153-157.
[63]张小锋,徐鸿钧,吴琦,等.基于双目图像的砂轮地貌检测[J].仪器仪表学报,2006，S3：2088-2089.
[64]孔凡芝.引线键合视觉检测关键技术研究[D].哈尔滨：哈尔滨工程大学,2009.
[65]熊俊涛,邹湘军,陈丽娟,等.荔枝采摘机械手果实识别与定位技术[J].江苏大学学报(自然科学版),2012,01：1-5.
[66]毕听.面向TFT-LCD制程的Mura缺陷机器视觉检测方法研究[D].上海：上海交通大学,2009.
[67]栗琳,王仲,蔡振兴,等.基于目标轮廓的附着物定位与剔除方法[J].光电工程,2012,39(5)：45-51.
[68]张艳辉,金翠云,王颖.基于圆结构光视觉三维点的孔洞缺陷识别及重构[J].北京化工大学学报(自然科学版),2012,05：113-117.
[69]董浩,荆熠,王锦平,等.基于机器视觉技术的烟用包装膜磨损程度测定方法[J].烟草科技,2012,07：9-12.
[70]张亚军,齐杏林,董成基.基于机器视觉的枪弹钢芯自动检测系统[J].兵工自动化,2012：31(7)：8-11.
[71]杨建国,肖蓉,李蓓智,等.基于机器视觉的刀具磨损检测技术[J].东华大学学报(自然科学版),2012,5：505-508+518.
[72]郭轩.彩色印刷品视觉检测相关算法研究[D].武汉：华中科技大学,2010.
[73]张习文,王晓东,罗怡,等.跨尺度微小型零件的测量与装配[J].纳米技术与精密工程,2012,10(004)：342-347.
[74]吴翔.面向再制造的机器人三维视觉检测系统研究[D].大连：大连海事大学,2011.
[75]王晓东,宋洪侠,刘超,等.基于机器视觉的微小型零件测量与装配控制[J].哈尔滨：哈尔滨工程大学学报,2011,32(9)：1117-1122.
[76]于忠党.基于支持向量机的机械零件计算机视觉检测若干关键技术的研究[D].长春：吉林大学,2009.
[77]吴俊芳.小尺寸视觉二维精密检测理论与方法[D].广州：华南理工大学,2011.
[78]黄星奕,李剑,姜松.基于计算机视觉的稻谷品种识别技术的研究[J].江苏大学学报(自然科学版),2004,02：102-104.
[79]吴继华,刘燕德,欧阳爱国.基于机器视觉的种子品种实时检测系统研究[J].传感技术学报,2005,18(4)：742-746.
[80]喻擎苍,严红滨.一种基于图像轮廓线的稻种模糊模式识别方法[J].农业工程学报2002,18(1),150-153.
[81]黄星奕,吴守一,方如明.计算机视觉在大米胚芽识别中的应用[J].农业机械学报,2000,31(1)：62-65.
[82]黄星奕,吴守一,方如明.用神经网络方法进行大米留胚率自动检测的研究[J].农业工程学报,1999,04：187-191.
[83]许俐.机器视觉测量大米留胚率的研究[J].江苏理工大学学报(自然科学版),1997,18(6)：8-11.
[84]黄星奕,吴守一,方如明.基于小波变换的稻米爆腰检测技术研究[J].农业工程学报,2004：20(6)：194-196.
[85]张家年,程秋琼,谭军.垩白对稻米吸湿产生裂纹的影响[J].中国粮油学报,2001,16(1)：40-42.
[86]尚艳芬,侯彩云,常国华.基于图像识别的黄粒米自动检测研究[J].农业工程学报,2004,20(4)：146-148.
[87]孙明,王一鸣,凌云,等.基于色调的黄粒米检测方法[J].农业机械学报,2005,36(8)：78-81.
[88]吴彦红,刘木华,杨君,等.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.
[89]Neugebauer PJ. Geometrical cloning of 3D objects via simultaneous registration of multiple range images [C]. Shape Modeling and Applications,1997 Proceedings, 1997 International Conference on; 1997:130-139.
[90]张舜德,卢秉恒,丁玉成.光学三维形面分区域测量数据的拼接研究[J].中国激光,2001，06：533-536.
[91]刘德连,张建奇.基于3点匹配的图像拼接算法[J].计算机工程,2006,13：203-205.
[92]Besl PJ, McKay ND. Method for registration of 3-D shapes [C]. Robotics-DL tentative; 1992:International Society for Optics and Photonics,586-606.
[93]Roberts LG. Machine perception of three-dimensional solids[R]:DTIC Document, 1963.
[94]Sobel I. Camera models and machine perception [R]:DTIC Document,1970.
[95]Canny J. A computational approach to edge detection [J]. Pattern Analysis and Machine Intelligence,1986, (6):679-698.
[96]雒涛,郑喜凤,丁铁夫.改进的自适应阈值Canny边缘检测[J].光电工程,2009,36(11)：106-111.
[97]陈宏希.基于边缘保持平滑滤波的Canny算子边缘检测[J].兰州交通大学学报,2006,01：86-90.
[98]丘维声.解析几何[M].北京：北京大学出版社,1988：330.
[99]HuM-K. Visual pattern recognition by moment invariants [J]. Information Theory, 1962,8(2):179-187.
[100]Zernike vF. Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode [J]. Physica,1934,1(7):689-704.
[101]Chong C-W, Raveendran P, Mukundan R. An efficient algorithm for fast computation of pseudo-Zernike moments [J]. International journal of pattern recognition and artificial intelligence,2003,17(06):1011-1123.
[102]Shen D, Ip HH. Discriminative wavelet shape descriptors for recognition of 2-D patterns [J]. Pattern recognition,1999,32(2):151-166.
[103]Teh C-H, Chin RT. On image analysis by the methods of moments [J]. Pattern Analysis and Machine Intelligence,1988,10(4):496-513.
[104]Khotanzad A, Hong YH. Invariant image recognition by Zernike moments [J]. Pattern Analysis and Machine Intelligence,1990,12(5):489-497.
[105]夏婷,周卫平,李松毅,等.一种新的Pseudo-Zernike矩的快速算法[J].电子学报,2005,33(7)：1295-1303.
[106]Lowe DG. Distinctive image features from scale-invariant keypoints [J]. International journal of computer vision,2004,60(2):91-110.
[107]Chien C, Aggarwal J. Identification of 3D objects from multiple silhouettes using quadtrees/octrees [J]. Computer Vision, Graphics, and Image Processing,1986, 36(2):256-273.
[108]Yemez Y, Sahillioglu Y. Shape from silhouette using topology-adaptive mesh deformation [J]. Pattern Recognition Letters,2009,30(13):1198-1207.
[109]Hernandez Esteban C, Schmitt F. Multi-stereo 3d object reconstruction [C]. 3D Data Processing Visualization and Transmission,2002 Proceedings First International Symposium on,2002,159-66.
[110]Matsumoto Y, Terasaki H, Sugimoto K, et al. A portable three-dimensional digitizer [C].3-D Digital Imaging and Modeling,1997 Proceedings, International Conference on Recent Advances in; 1997,197-204.
[111]Niem W. Automatic reconstruction of 3D objects using a mobile camera [J]. Image and Vision Computing,1999,17(2):125-134.
[112]Schmitt F, Yemez Y.3d color object reconstruction from 2d image sequences [C]. Image Processing,1999 ICIP 99 Proceedings 1999 International Conference on; 1999,65-69.
[113]Dyer CR. Volumetric scene reconstruction from multiple views [J]. Foundations of Image Understanding,2001,628:469-489.
[114]Slabaugh G, Schaf er R, Hans M. Image-based photo hulls [C].3D Data Processing Visualization and Transmission,2002 Proceedings First International Symposium on; 2002,704-862.
[115]Lorensen WE, Cline HE. Marching cubes:A high resolution 3D surface construction algorithm [C]. ACM Siggraph Computer Graphics; 1987,163-169.
[116]Montani C, Scateni R, Scopigno R. Discretized marching cubes [C]. Proceedings of the conference on Visualization'94; 1994:IEEE Computer Society Press; 1994, 281-287.
[117]Laurentini A. The visual hull concept for silhouette-based image understanding [J]. Pattern Analysis and Machine Intelligence,1994,16(2):150-162.
[118]Franco J-S, Lapierre M, Boyer E. Visual shapes of silhouette sets [C].3D Data Processing, Visualization, and Transmission, Third International Symposium on; 2006,397-404.
[119]Cheung GK, Baker S, Kanade T. Visual hull alignment and refinement across time: A 3d reconstruction algorithm combining shape-from-silhouette with stereo [C]. Computer Vision and Pattern Recognition,2003 Proceedings 2003 IEEE Computer Society Conference on; 2003,375-382.
[120]Matsumoto Y, Fujimura K, Kitamura T. Shape-from-silhouette/stereo and its application to 3-d digitizer [C]. Discrete Geometry for Computer Imagery; 1999: Springer; 1999,177-188.
[121]Lin H-Y, Wu J-R.3d reconstruction by combining shape from silhouette with stereo [C]. Pattern Recognition,2008 ICPR 2008 19th International Conference on; 2008,1-4.
[122]Seitz SM, Dyer CR. Photorealistic scene reconstruction by voxel coloring [J]. International Journal of Computer Vision,1999,35(2):151-173.
[123]Kutulakos KN, Seitz SM. A theory of shape by space carving [J]. International Journal of Computer Vision,2000,38(3):199-218.
[124]Mulayim AY, Yilmaz U, Atalay V. Silhouette-based 3-D model reconstruction from multiple images [J]. Systems, Man, and Cybernetics, Part B:Cybernetics,2003,33(4): 582-591.
[125]张剑清,苏国中,郑顺义,等.基于线扩散函数的高精度轮廓特征提取算法[J].武汉大学学报(信息科学版),2006,31(2)：95-104.
[126]李彩林,郭宝云,李畅.目标轮廓直线特征的高精度提取[J].测绘学报,2011,40(1)：66-70.
[127]Zheng S, Li C, Su G, Zhang J. High-accurate line feature extraction algorithm based on line diffusion function model [C]. International Symposium on Multispectral Image Processing and Pattern Recognition; 2007:International Society for Optics and Photonics; 2007,678-683.
[128]韩鹏,龚健雅,李志林,等.遥感影像空间尺度上推方法的评价[J].遥感学报,2008,12(6)：964-971.
[129]张丽,王昱.一种快速测定卫星像片分辨率的方法[J].解放军测绘研究所学报,2002,22(1)：50-52.
[130]Cheung K, Baker S, Kanade T. Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture [C]. Computer Vision and Pattern Recognition,2003 Proceedings 2003 IEEE Computer Society Conference on; 2003,77-84.
[131]Dyer CR. Volumetric scene reconstruction from multiple views. Foundations of Image Understanding[M]:America:Springer,2001:469-489.
[132]徐颖欣,李醒飞,朱嘉.远心镜头在回转体测量系统中的应用[J].传感器与微系统,2010,(1)：111-113.
[133]曲直.基于数字图像处理技术的钻石切工比例测定系统的研究和应用[D].武汉：中国地质大学,2005.
[134]Fremont V, Chellali R. Turntable-based 3D object reconstruction [C]. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems; 2004,1276-1281.
[135]Beardsley P, Torr P, Zisserman A.3D model acquisition from extended image sequences[J]. Computer Vision,1996:683-695.
[136]Fitzgibbon AW, Cross G, Zisserman A. Automatic 3D model construction for turn-table sequences[A].3D Structure from Multiple Images of Large-Scale Environments[M]. Springer,1998:155-70.
[137]Eisert P, Steinbach E, Girod B. Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views [J]. Circuits and Systems for Video Technology,2000,10(2):261-277.
[138]Otsu N. A threshold selection method from gray-level histograms [J]. Automatica,1975,11(285-296):23-27.
[139]熊邦书,余亮,欧巧凤,等.注射器针头合格性图像检测方法[J].中国生物医学工程学报,2010,29(131)：627-631.
[140]Hu G, Stockman G.3-D surface solution using structured light and constraint propagation[J]. Pattern Analysis and Machine Intelligence,1989,11(4):390-402.
[141]Zou D, Ye S, Wang C. Structured-lighting surface sensor and its calibration [J]. Optical Engineering,1995,34(10):3040-3043.
[142]Zhang L, Curless B, Seitz SM. Rapid shape acquisition using color structured light and multi-pass dynamic programming [C].3D Data Processing Visualization and Transmission,2002 Proceedings First International Symposium on; 2002:24-36.
[143]Bergmann D. New approach for automatic surface reconstruction with coded light[C]. PROCEEDINGS-SPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING; 1995:SPIE INTERNATIONAL SOCIETY FOR OPTICAL; 1995:2-8.
[144]Sato K. Range imaging based on moving pattern light and spatio-temporal matched filter [C]. Image Processing,1996 Proceedings, International Conference on; 1996: IEEE; 1996:33-36.
[145]Sansoni G, Corini S, Lazzari S, Rodella R, Docchio F. Three-dimensional imaging based on Gray-code light projection:characterization of the measuring algorithm and development of a measuring system for industrial applications [J]. Applied Optics,1997,36(19):4463-4472.
[146]魏媛.用编码结构光方法实现的物体三维重构[D].大连：大连理工大学,2005.
[147]于晓洋,吴海滨.基于Gray码和线移条纹的结构光3D测量技术[J].仪器仪表学报,2008,29(4)：701-704.
[148]郑顺义,胡华亮,徐轩.基于结构光和极线约束的三维重建[J].信息化纵横,2009,29(8)：48-51.
[149]张永军,张祖勋,张剑清.利用二维DLT及光束法平差进行数字摄像机标定[J].武汉大学学报(信息科学版),2002,27(6)：566-571.
[150]岳慧敏.基于时间相位展开的三维轮廓测量研究[D].成都：四川大学,2005.
[151]李德仁,周月琴,金为铣.摄影测量与遥感概论[M].测绘出版社;2001：45.
[152]冯文灏.近景摄影测量[M].武汉大学出版社;2002：238.
[153]戴静兰,陈志杨,叶修梓.ICP算法在点云配准中的应用[J].中国图象图形学报,2007,12(3)：517-521.
[154]马扬飚,钟约先,郑聆,等.三维数据拼接中编码标志点的设计与检测[J].清华大学学报(自然科学版),2006,02：169-171+175.
[155]Gelfand N, Ikemoto L, Rusinkiewicz S, Levoy M. Geometrically stable sampling for the ICP algorithm [C].3-D Digital Imaging and Modeling,2003 3DIM 2003 Proceedings Fourth International Conference on; 2003:IEEE; 2003:260-267.
[156]张艳丽,李晓星,杨兴,等.小型零件测量的旋转拼接[J].航天制造技术,2005，01：32-35.
[157]龙玺,钟约先,李仁举,等.结构光三维扫描测量的三维拼接技术[J].清华大学学报(自然科学版),2002,42(4)：477-480.
[158]Seitz SM, Curless B, Diebel J, Scharstein D, Szeliski R. A comparison and evaluation of multi-view stereo reconstruction algorithms [C]. Computer Vision and Pattern Recognition,2006 IEEE Computer Society Conference on; 2006:IEEE; 2006: 519-528.
[159]吴福朝.计算机视觉中的数学方法[M].北京：科学出版社,2008：283.
[160]林志雄,乐凯,胡华亮,等.结构光三维测量系统的点云精度分析[J].武汉理工大学学报,2011,33(9)：160-164.
[161]Stearns MB. Probability distribution of a network of triangles [J]. SIAM Review,1967,9(3):591-603.
[162]Lee D-T, Schachter BJ. Two algorithms for constructing a Delaunay triangulation [J]. International Journal of Computer & Information Sciences,1980, 9(3):219-242.
[163]Brassel KE, Reif D. A procedure to generate Thiessen polygons [J]. Geographical Analysis,1979,11(3):289-303.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |