基于活动轮廓模型的图像特征提取方法研究
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摘要
轮廓提取是计算机视觉的重要研究课题，其在虚拟现实、自控车辆、机器人环境分析、监控系统中的物体跟踪及识别、生物医学图像处理、工业在线自动检测等方面有着广泛的应用前景。在制造领域，通过对实物或模型的测量构造物体的几何模型，进而用于改进设计与制造，又称为反求工程技术。反求工程技术广泛应用于科学研究、工程技术与生物医学等领域，以解决或辅助解决几何尺寸度量、变形测量、振型测试、损伤测定、产品质量监控、实物仿形、CAD／CAM以及医学诊断等诸多问题。在用于轮廓提取的技术中，可变形活动轮廓是目前国内外研究的热点。
可变形活动轮廓模型的提出给传统的计算机视觉理论及应用研究带来了新的观点和思维方式，并已发展成为计算机视觉与模式识别中最为活跃和成功的研究领域之一。因此，基于可变形活动轮廓模型的图像处理技术研究在科学研究和工程应用中有着重要的意义。本文选题于国家自然科学基金资助项目“由一般光照条件下的图像生成实体形态的理论与方法研究”(编号：59975057)，系统的分析了国内外关于可变形模型理论与应用研究的基础上，提出了一种基于动态规划法的B样条主动轮廓模型，作为应用例之一，利用它进行人脸图像的处理和提取断层图像数据建立三维模型。其主要工作和所得到的结论如下：
(1)从几何模型、尺度空间、算法实现等三个方面对可变形活动轮廓模型的理论研究与发展进行了综述；对经典的可变形活动轮廓模型的物理本质和具体求解过程进行了探讨；指出了经典可变形活动轮廓模型理论上存在的问题及进一步研究的方向。
(2)对活动轮廓模型的尺度空间进行了改进，提出了基于多尺度和多分辨率的可变形活动轮廓模型，该模型能有效的防止变形活动轮廓模型收敛于局部极小值，并且提高了变形活动轮廓模型的捕捉能力，由于低分辨率时所需控制点少所以收敛速度快。
(3)提出了一种基于动态规划法的B样条可变形活动轮廓模型。B样条方法是当前自由曲线曲面造型最为常用的方法，动态规划为解决离散最优化问题的有效手段。该模型采用控制点控制的三次B样条曲线段来表示活动轮廓，然后将控制点代入动态规划法求解，从而实现对图像的轮廓提取。该模型结合了B样条方
广东工业大学工学硕士学位论文
法和动态规划法的优点，可以快速稳定地收敛到目标轮廓，得到的B样条轮廓有
利于进一步的表面重建处理。
Contour extraction is an important problem in computer vision, and can be used extensively in many fields such as virtual reality, autonomous guided vehicles, robot environment analysis, object tracking and recognition in monitor system, biology medical image processing, industry online automatic checking and reverse engineering etc. In the field of manufacture, the geometric models are constructed through measuring them, and then they will be used to ameliorate the design and manufacture, this process is called reverse engineering. Reverse engineering is used widely in the field of science study, technology, biomedicine etc, and is used for settling the problem of geometric size measurement, transmogrification measurement, scathe measurement, surveilling the quality of manufacture, profile modeling, CAD/CAM, and physic diagnosis etc. In these techniques, the deformable active model is a hot point research.
The deformable active models bring a new viewpoint to traditional computer version, and become one of the most active and successful fields in computer vision and pattern recognition. Therefore, the research of image manipulation based on the deformable active models is important signification of theory and application. In this dissertation, based on the systematically analysis of theory and application research on The deformable active models, a new B-spline deformable active models based on the DP approaches is proposed, and then it is applied to the human face detection and 3D reconstruction based on layer data. The main work and conclusion in this dissertation are as follows:
The research, development and application of deformable active contour model is reviewed from geometry representation, scale-spaces and optimization method; the physical essence and the idiographic calculating process of the classical deformable active contour model are also discussed; and the existing problems and possible future research orientations are
presented.
The scale-spaces of deformable active contour model has been improved, and a new model based on the MS-MR is presented, which can avoid the local minimum effectively and enforce the capture ability of the deformed active contour model. The convergence speed also has been increased because of few control points.
A B-spline deformed active contour model based on DP algorithm is presented. B-spline is the most popular method to present free-form curve and surface, and DP algorithm is the effective approach for solving discrete optimization problems. In this model, the deformed active contour is represented by the cubic B-spline curve segments which are restricted by the control points, and the control points are applied to the calculation of DP algorithm, in the end the contour of image is found. Experiment results show that this model could effectively combine the merits of B-spline and DP algorithm, yielding stable, accurate and faster convergence, and its result is favorable for surface reconstruction.
可变形活动轮廓模型的提出给传统的计算机视觉理论及应用研究带来了新的观点和思维方式，并已发展成为计算机视觉与模式识别中最为活跃和成功的研究领域之一。因此，基于可变形活动轮廓模型的图像处理技术研究在科学研究和工程应用中有着重要的意义。本文选题于国家自然科学基金资助项目“由一般光照条件下的图像生成实体形态的理论与方法研究”(编号：59975057)，系统的分析了国内外关于可变形模型理论与应用研究的基础上，提出了一种基于动态规划法的B样条主动轮廓模型，作为应用例之一，利用它进行人脸图像的处理和提取断层图像数据建立三维模型。其主要工作和所得到的结论如下：
(1)从几何模型、尺度空间、算法实现等三个方面对可变形活动轮廓模型的理论研究与发展进行了综述；对经典的可变形活动轮廓模型的物理本质和具体求解过程进行了探讨；指出了经典可变形活动轮廓模型理论上存在的问题及进一步研究的方向。
(2)对活动轮廓模型的尺度空间进行了改进，提出了基于多尺度和多分辨率的可变形活动轮廓模型，该模型能有效的防止变形活动轮廓模型收敛于局部极小值，并且提高了变形活动轮廓模型的捕捉能力，由于低分辨率时所需控制点少所以收敛速度快。
(3)提出了一种基于动态规划法的B样条可变形活动轮廓模型。B样条方法是当前自由曲线曲面造型最为常用的方法，动态规划为解决离散最优化问题的有效手段。该模型采用控制点控制的三次B样条曲线段来表示活动轮廓，然后将控制点代入动态规划法求解，从而实现对图像的轮廓提取。该模型结合了B样条方
广东工业大学工学硕士学位论文
法和动态规划法的优点，可以快速稳定地收敛到目标轮廓，得到的B样条轮廓有
利于进一步的表面重建处理。
Contour extraction is an important problem in computer vision, and can be used extensively in many fields such as virtual reality, autonomous guided vehicles, robot environment analysis, object tracking and recognition in monitor system, biology medical image processing, industry online automatic checking and reverse engineering etc. In the field of manufacture, the geometric models are constructed through measuring them, and then they will be used to ameliorate the design and manufacture, this process is called reverse engineering. Reverse engineering is used widely in the field of science study, technology, biomedicine etc, and is used for settling the problem of geometric size measurement, transmogrification measurement, scathe measurement, surveilling the quality of manufacture, profile modeling, CAD/CAM, and physic diagnosis etc. In these techniques, the deformable active model is a hot point research.
The deformable active models bring a new viewpoint to traditional computer version, and become one of the most active and successful fields in computer vision and pattern recognition. Therefore, the research of image manipulation based on the deformable active models is important signification of theory and application. In this dissertation, based on the systematically analysis of theory and application research on The deformable active models, a new B-spline deformable active models based on the DP approaches is proposed, and then it is applied to the human face detection and 3D reconstruction based on layer data. The main work and conclusion in this dissertation are as follows:
The research, development and application of deformable active contour model is reviewed from geometry representation, scale-spaces and optimization method; the physical essence and the idiographic calculating process of the classical deformable active contour model are also discussed; and the existing problems and possible future research orientations are
presented.
The scale-spaces of deformable active contour model has been improved, and a new model based on the MS-MR is presented, which can avoid the local minimum effectively and enforce the capture ability of the deformed active contour model. The convergence speed also has been increased because of few control points.
A B-spline deformed active contour model based on DP algorithm is presented. B-spline is the most popular method to present free-form curve and surface, and DP algorithm is the effective approach for solving discrete optimization problems. In this model, the deformed active contour is represented by the cubic B-spline curve segments which are restricted by the control points, and the control points are applied to the calculation of DP algorithm, in the end the contour of image is found. Experiment results show that this model could effectively combine the merits of B-spline and DP algorithm, yielding stable, accurate and faster convergence, and its result is favorable for surface reconstruction.
引文
[1] Mclnerney T, Terzopoulos D. Deformable models in medical image analysis:a survey. Medical Image Analysis, 1996, 1(2): 91-108
[2] 史文中，朱长青，王昱．从遥感影像中提取道路特征的方法综述与展望．测绘学报，2001，30(3)：257-262
[3] Wang Y, Teoh EK. Lane detection using B-snake. IEEE International Conference on Information, Intelligence and Systems, Washington DC, 1999: 438-443
[4] 夏利民，谷士文，曾三友．基于形变模型由立体序列图象恢复物体的形状．计算机工程，2001，27(10)：22-23
[5] 李培华，张田文．主动轮廓线模型(蛇模型)综述．软件学报，2000，11(6)：751-757
[6] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. International Journal of computer vision, 1988, 1(4):321-331
[7] Singh A, Kurowski L, Chiu MY. Cardiac MR image segmentation using deformable models. In Biomedical Image Processing and Biomedical Visualization, 1993, volume 1905 of SPIE proceeding, 8-28
[8] Xu C, Prince JL. Snakes, shape, and gradient vector flow. IEEE Transaction on Image Processing, 1998, 7(3): 359-369
[9] Lu Aidong, Tang Long, Xu Yuhua etc. Segmentation of ultrasound images with interactive B-spline snakes and its application. Journal of Software, 2001, 12(12): 1760-1768
[10] Hjelmas E, Low B K. Face detection:A survey. Computer Vision and Image Understanding, 2001, 83(3): 236-274
[11] Huang CL, Chen CW. Human facial feature extraction for face interpretation and recognition. Pattern Recognition, 1992, 25(12): 1435-1444
[12] Coots TF, Taylor CJ, Lanitis A. Active shape models—— 'smart snakes' . in Proceedings of British Machine Vision Conference, Berlin, Germany, 1992:266-275
[13] 刘少创，林宗坚．航空影象分割的snake方法．武汉测绘科技大学学报，1995，20(1)：7-11
[14] Cohen LD, Cohen I. Finite element methods for active contour models and balloons for 2-D and 3-d images. IEEE Transaction on Pattern Analysis and Machine
Intelligence, 1993, 15(11): 1131-1147
[15] 张朝宗，郭志平，吕军震工业CT研究进展概况 工业CT研究及理论
[16] Gibson S, Mirtich B. A survey of deformable modeling in computer graphics. Technical Report, No. TR97-19, Department of Computer Science, University of Toronto, Canada, 1997
[17] Cohen LD. On active contour models and balloons. CVGIP: Image Understanding, 1991, 53(2): 211-218
[18] Staib LH, Duncan JS. Boundary finding with parametrically deformable models. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1992, 14(11): 1061-1075
[19] Menet S, Saint-Marc P, Medioni G. B-snakes: implementation and application to stereo. In: Proceedings of image understanding workshop. Pittsburgh: IEEE Computer Society Press, 1990, 720-726
[20] 黄海斌，戚飞虎，赵春雪。基于多尺度图像的主动轮廓线模型。计算机研究与发展，2000，10：(1240-1245)。
[21] Julia Anne Schnabel. Multi-Scale active Shape Description in Medical Imaging. 1997, 11.
[22] 夏利民，谷士文，沈新权。基于活动轮廓的多分辨率自适应图像分割。小型微型计算机系统，2001，2：(161-164)。
[23] Rückert D. Segmentation and tracking in cardiovascular MR images using geometrically deformable models and templates. PhD thesis, London, University of London, 1997
[24] McInerkey T, Terzopoulos D. Topologically adaptable snakes. Proceedings of the Fifth International Conference on Computer Vision, Cambridge, USA, 1995: 840-845
[25] Amini A, Weymouth T, Jain R. Using dynamic programming for solving variational problems in vision. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1990, 12(9): 855-867
[26] Williams DJ, Shah M. A fast algorithm for active contours and curvature estimation. CVGIP: Image Understanding, 1992, 55(1): 14-26
[27] Xu Chengyang. Deformable models with application to human cerebral cortex reconstruction from magnetic resonance images. PhD thesis, Baltimore, Maryland, the Johns Hopkins University, 2000
[28] Laik F, Chin R T. Deformable contours: modeling and extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1995, 17(11): 1084-1090
[29] 马然，张兆阳，安平。基于活动轮廓模型的DP修正算法。上海大学学报，1999，9：(437—440)。
[30] Menet S, Saint-Marc P, Medioni G. B-snakes: implementation and application to stereo. In: Proceedings of image understanding workshop. Pittsburgh: IEEE Computer Society Press, 1990, 720-726
[31] Wang M, Evans J, Hassebrook L, et al. A multistage optimal active contour model. IEEE Transaction on Image Process, 1996, 5 (11): 1586-1591
[32] Barrels, Wolberg G, Shin SY. Scattered data interpolation with multilevel B-splines. IEEE Transactions on Visualization and Computer Graphics, 3(3): 1-17
[33] Chen, chin, D. Deformable curve and surface finite-elements for free- form shape design. Computer Graphics, 1991, 25(4): 257-266
[34] Szeliski R. Bayesian modeling of uncertainty in low-level vision. International Journal of computer vision, 1990, 5(3): 271-301
[35] 朱心雄等。自由曲线曲面造型技术。第一版。北京：科学出版社，2000，87-132。
[36] Patrick Brigger, Associate Member, B-Spline: A Flexible Tool for Parametric Contour Detection IEEE Transaction on Image Processing, 2000,9(9):1484-1496
[37] 吴涛，刘金义。基于轮廓线的三维重建方法的研究。抚顺石油学院学报，1999，03
[38] Max Mignotte, Jean Meunier. AN UMSUPERVISED MULTISCALE APPROACH FOR THE DYNAMIC CONTOUR-BASED BOUNDRY DETECTION ISSUE IN ULTRASOUND IMAGE. DIRO, 1995:165-173
[39] Babaud J, Witkin A P, Baudin Metal. Uniqueness of the Gaussian kernel for scale-space filtering. IEEE Trans on Pattern Analogy & Machine Intelligence, 1986,8(1):26-33
[40] 杨杨，张田文。基于多分辨率方法的主动轮廓线跟踪算法。计算机学报，1998，21(3)：210-216
[41] Bergholm F. Edge focusing. IEEE Trans on Pattern Analogy & Machine Intelligence, 1987,9(6):726-741
[42] B. Shahraray and D. Anderson,"Optimal estimation of contour properties by cross-validated regularization," IEEE Trans. Pattern Anal. Machine Intell, vol. 11 no. 6, pp. 660-610, june 1989
[43] 潘金贵。现代计算机常用数据结构和算法。第一版。南京：南京大学出版社，1992年
[44] 傅清祥，王晓东。算法与数据结构。第一版。北京：电子工业出版社，1998年
[45] 张莹。运筹学基础。第一版。北京：清华大学出版社，1995
[46] Sharon Curtis. Dynamic programming: A Different perspective
[47] Gunn S R, Nixon M S. Global and local contours for head boundary extraction. International Journal of Computer Vision, 1998, 30(1):43-54
[48] Huang C L, Chen C W. Human facial feature extraction for face interpretation and recognition. Pattern Recognition, 1992, 25(120:1435-1444
[49] Lam K M, Yah H. Location head boundary by snakes. In: International Symposium on Speech, Image Processing and Neural Networks[c], Hongkong, 1994:17-20
[50] 黄福珍，苏剑波，席裕庚。基于活动轮廓模型的人脸轮廓提取方法。中国图像图形学报，2003，8(5)：546-550
[51] 梁远蕾，王宗彦。基于断层数据的三维重构技术。华北工学院学报，2002，23(1)：132-137
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[54] 王宗彦，梁远蕾，李奇敏等。断层数据三维重构技术的研究进展。工程图学学报，2002，1(1)：125-130
[55] 秦绪佳，纪凤欣，吴良武等．由基于轮廓重建的表面模型构建实体几何模型．机械设计与研究，2001，17(2)：15-17
[56] 普雷帕拉塔，沙莫斯著，庄心谷译．计算几何导论．北京：科学出版社，1990
[2] 史文中，朱长青，王昱．从遥感影像中提取道路特征的方法综述与展望．测绘学报，2001，30(3)：257-262
[3] Wang Y, Teoh EK. Lane detection using B-snake. IEEE International Conference on Information, Intelligence and Systems, Washington DC, 1999: 438-443
[4] 夏利民，谷士文，曾三友．基于形变模型由立体序列图象恢复物体的形状．计算机工程，2001，27(10)：22-23
[5] 李培华，张田文．主动轮廓线模型(蛇模型)综述．软件学报，2000，11(6)：751-757
[6] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. International Journal of computer vision, 1988, 1(4):321-331
[7] Singh A, Kurowski L, Chiu MY. Cardiac MR image segmentation using deformable models. In Biomedical Image Processing and Biomedical Visualization, 1993, volume 1905 of SPIE proceeding, 8-28
[8] Xu C, Prince JL. Snakes, shape, and gradient vector flow. IEEE Transaction on Image Processing, 1998, 7(3): 359-369
[9] Lu Aidong, Tang Long, Xu Yuhua etc. Segmentation of ultrasound images with interactive B-spline snakes and its application. Journal of Software, 2001, 12(12): 1760-1768
[10] Hjelmas E, Low B K. Face detection:A survey. Computer Vision and Image Understanding, 2001, 83(3): 236-274
[11] Huang CL, Chen CW. Human facial feature extraction for face interpretation and recognition. Pattern Recognition, 1992, 25(12): 1435-1444
[12] Coots TF, Taylor CJ, Lanitis A. Active shape models—— 'smart snakes' . in Proceedings of British Machine Vision Conference, Berlin, Germany, 1992:266-275
[13] 刘少创，林宗坚．航空影象分割的snake方法．武汉测绘科技大学学报，1995，20(1)：7-11
[14] Cohen LD, Cohen I. Finite element methods for active contour models and balloons for 2-D and 3-d images. IEEE Transaction on Pattern Analysis and Machine
Intelligence, 1993, 15(11): 1131-1147
[15] 张朝宗，郭志平，吕军震工业CT研究进展概况 工业CT研究及理论
[16] Gibson S, Mirtich B. A survey of deformable modeling in computer graphics. Technical Report, No. TR97-19, Department of Computer Science, University of Toronto, Canada, 1997
[17] Cohen LD. On active contour models and balloons. CVGIP: Image Understanding, 1991, 53(2): 211-218
[18] Staib LH, Duncan JS. Boundary finding with parametrically deformable models. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1992, 14(11): 1061-1075
[19] Menet S, Saint-Marc P, Medioni G. B-snakes: implementation and application to stereo. In: Proceedings of image understanding workshop. Pittsburgh: IEEE Computer Society Press, 1990, 720-726
[20] 黄海斌，戚飞虎，赵春雪。基于多尺度图像的主动轮廓线模型。计算机研究与发展，2000，10：(1240-1245)。
[21] Julia Anne Schnabel. Multi-Scale active Shape Description in Medical Imaging. 1997, 11.
[22] 夏利民，谷士文，沈新权。基于活动轮廓的多分辨率自适应图像分割。小型微型计算机系统，2001，2：(161-164)。
[23] Rückert D. Segmentation and tracking in cardiovascular MR images using geometrically deformable models and templates. PhD thesis, London, University of London, 1997
[24] McInerkey T, Terzopoulos D. Topologically adaptable snakes. Proceedings of the Fifth International Conference on Computer Vision, Cambridge, USA, 1995: 840-845
[25] Amini A, Weymouth T, Jain R. Using dynamic programming for solving variational problems in vision. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1990, 12(9): 855-867
[26] Williams DJ, Shah M. A fast algorithm for active contours and curvature estimation. CVGIP: Image Understanding, 1992, 55(1): 14-26
[27] Xu Chengyang. Deformable models with application to human cerebral cortex reconstruction from magnetic resonance images. PhD thesis, Baltimore, Maryland, the Johns Hopkins University, 2000
[28] Laik F, Chin R T. Deformable contours: modeling and extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1995, 17(11): 1084-1090
[29] 马然，张兆阳，安平。基于活动轮廓模型的DP修正算法。上海大学学报，1999，9：(437—440)。
[30] Menet S, Saint-Marc P, Medioni G. B-snakes: implementation and application to stereo. In: Proceedings of image understanding workshop. Pittsburgh: IEEE Computer Society Press, 1990, 720-726
[31] Wang M, Evans J, Hassebrook L, et al. A multistage optimal active contour model. IEEE Transaction on Image Process, 1996, 5 (11): 1586-1591
[32] Barrels, Wolberg G, Shin SY. Scattered data interpolation with multilevel B-splines. IEEE Transactions on Visualization and Computer Graphics, 3(3): 1-17
[33] Chen, chin, D. Deformable curve and surface finite-elements for free- form shape design. Computer Graphics, 1991, 25(4): 257-266
[34] Szeliski R. Bayesian modeling of uncertainty in low-level vision. International Journal of computer vision, 1990, 5(3): 271-301
[35] 朱心雄等。自由曲线曲面造型技术。第一版。北京：科学出版社，2000，87-132。
[36] Patrick Brigger, Associate Member, B-Spline: A Flexible Tool for Parametric Contour Detection IEEE Transaction on Image Processing, 2000,9(9):1484-1496
[37] 吴涛，刘金义。基于轮廓线的三维重建方法的研究。抚顺石油学院学报，1999，03
[38] Max Mignotte, Jean Meunier. AN UMSUPERVISED MULTISCALE APPROACH FOR THE DYNAMIC CONTOUR-BASED BOUNDRY DETECTION ISSUE IN ULTRASOUND IMAGE. DIRO, 1995:165-173
[39] Babaud J, Witkin A P, Baudin Metal. Uniqueness of the Gaussian kernel for scale-space filtering. IEEE Trans on Pattern Analogy & Machine Intelligence, 1986,8(1):26-33
[40] 杨杨，张田文。基于多分辨率方法的主动轮廓线跟踪算法。计算机学报，1998，21(3)：210-216
[41] Bergholm F. Edge focusing. IEEE Trans on Pattern Analogy & Machine Intelligence, 1987,9(6):726-741
[42] B. Shahraray and D. Anderson,"Optimal estimation of contour properties by cross-validated regularization," IEEE Trans. Pattern Anal. Machine Intell, vol. 11 no. 6, pp. 660-610, june 1989
[43] 潘金贵。现代计算机常用数据结构和算法。第一版。南京：南京大学出版社，1992年
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