图像分割若干理论方法及应用研究
|
摘要
图像分割是将图像划分成多个具有相似特征的区域，并提出感兴趣目标的技术与过程，图像分割是图像理解和图像识别的前提，也可以把它看作为图像理解与识别的初级阶段。图像分割的大致发展过程为：早期的基于灰度和梯度的经典分割技术(如阈值法，边缘和区域技术)，八十年代的活动轮廓模型(如参数活动轮廓模型和几何活动轮廓模型)，最近的结合形状等先验知识的分割方法(如活动形状模型和活动表面模型)。从图像分割的发展过程可以看出：所使用的先验信息越来越多，所具有的智能化程度越来越高，分割能力越来越强。本文主要利用灰度、形状、纹理和运动等信息研究了几种图像分割方法。本文首先分析一种经典的基于灰度与边缘信息的图像分割方法——图像二值化，然后详细讨论形状先验知识与活动轮廓模型相结合的方法和纹理分割方法，最后利用运动信息对序列图像进行跟踪。本文试图将各种信息(如灰度、形状、纹理、运动等)综合在一个统一的图像分割框架下，使它具有较高的智能化程度，从而能够解决一些复杂的图像分割问题。为了验证本文方法的有效性和实用性，本文方法被应用于左心室核磁共振图像分割。本文所做的主要工作和研究成果如下：
(1)综合考虑了边缘信息和灰度信息，提出了一种新的双阈值二值化方法。该方法首先通过在图像边缘附近寻找种子点，然后在由封闭轮廓划分的高阈值二值图像中进行种子填充得到初步的二值化结果，最后通过低阈值二值化图像的修补得到最后的二值化结果。实验结果表明此方法能够较好地解决低对比度和目标象素灰度不均匀图像的二值化问题，与传统的二值化方法相比，也与本文类似的结合边缘信息的二值化方法相比，本文的方法具有一定的优点。
(2)给出了一种结合先验形状统计信息的Mumford-Shah模型的水平集实现方法。结合形状统计的图像分割方法主要包括先验形状模型的构造和形状能量项的构造。本文针对这两个主要方面做了如下两点工作：首先提出了一种简单可行的先验形状模型构造方法；再者则是重新构造了形状能量项，它综合考虑了全局和局部形状信息，且不含形状姿态参量，使曲面演化稳定可靠。
(3)提出了一种基于方向局部方差的快速主动纹理分割方法。首先，采用方向局部方差得到一组特征图像，为了降低计算的复杂度，通过可分离性评价准则，选出可分离性较好的四个方向上的特征图像；然后采用非线性扩散滤波平滑待分割特征图像，以改善分割效果；最后通过变差框架将特征图像结合到基于水平集的无监督分割过程中，为了提高算法的运算速度，采用了直接计算能量泛函的方法求解变差公式，而不需要求解欧拉-拉格朗日方程。合成图像和真实图像的分割结果证明：该方法具有一定的通用性和较好的性能。
(4)提出了三种基于目标轮廓的运动跟踪方法：
●提出了一种新的基于边缘形状匹配的目标跟踪方法，主要利用图像的边缘信息来构造目标的形状相似度。为了保证跟踪精度和处理目标的局部变形，本文提出了一种新的基于权重窄带的形状匹配度计算方法。
●提出了一种基于目标轮廓点匹配度的参数活动轮廓目标跟踪方法。基本思想是根据目标轮廓点构造匹配度图像，然后采用参数活动轮廓模型跟踪目标。本文构造了一种新的特征匹配方法和一种新的方向滤波方法。考虑到相邻两帧间的目标运动较小，本文将目标轮廓的演化限定在初始轮廓的窄带区域内，采用了窄带水平集的窄带构造方法。
●将基于区域的方法与基于边界的方法相结合，提出了一种两阶段目标跟踪方法。首先采用基于核的目标跟踪方法快速定位目标区域，然后采用扩散snake进一步演化目标轮廓，精确定位目标边界。在初始目标定位阶段，为了能够有效地给出目标的初始跟踪位置，用kalman滤波对初始目标位置进行预测，同时通过Bhattacharyya系数进行进一步判断。在基于轮廓的精确定位阶段，采用了颜色空间成分阵列的方法生成目标特征图像，然后在特征图像上采用扩散snake进行边界演化，同时在演化过程中，为了保证目标轮廓向好的方向演化，对演化得到的目标区域进行相似性比较。
(5)提出了两种左心室核磁共振图像的分割方法：
●针对带标记线左心室核磁共振图像的特点，提出了一种基于分级处理的自动分割方法，主要由三部分组成：首先用数学形态学的方法实现左心室的自动定位；然后用k均值聚类、模板匹配和基于骨架的心肌形状恢复方法给出左心室的内外初始轮廓线；最后用改进的水平集方法对初始轮廓线进行演化而得到最终结果。
●针对不带标记线左心室核磁共振图像外轮廓的特点，在变差框架下，通过将Mumford-Shah模型作用于目标特征空间，同时结合形状统计先验知识和边缘图像，本文提出了一种改进的结合形状统计的变差方法来分割核磁共振图像的左心室外轮廓。形状先验知识的引入较好地克服了左心室外轮廓的边界断裂现象；边缘图像可以增强弱边缘，提高分割精度：通过以心肌为目标区域建立目标特征图像，使得左心室内部区域“灰度”一致，从而方便了Mumford-Shah模型的应用。
Image segmentation is a process of dividing an image into different regions such thateach region is, but the union of any two adjacent regions is not, homogeneous. Imagesegmentation is the first step in image understanding and pattern recognition. The basicdevelopment process of the image segmentation is as follows: the early classicalsegmentation methods based on image intensity and gradient, such as thresholding,contour-based, and region-based; the eighties active contour models, such as parametricactive contours and geometric active contours; the segmentation methods with priorknowledge, such as active shape model and active appearance model. From thedevelopment process, we can observe that the intelligence and ability of the imagesegmentation become better and better. In this paper, the following information is mainlyused to segment objects, such as intensity, shape, texture and motion. We first analyze aclassical image segmentation method, image binarization, and then discuss thesegmentation methods by combining shape priors and active contours and the texturesegmentation. Finally we give some object tracking methods. According to the research ofthis paper, we want to form a union image segmentation framework with various cues(such as intensity, shape, texture, motion etc), which has a high intelligent ability in orderto solve some complex image segmentation problems. This paper also shows oneapplication, namely the left ventricle magnetic resonance (MR) image segmentation. Theprimary work and remarks of this paper are as follows:
(1) This paper presents a new double-threshold image binarization method based onthe edge and intensity information. We first find the seeds near the image edges, andpresent an edge connection method to close the image edges. Then, we use closed imageedges to partition the binarized image that is generalized using a high threshold, and obtainthe primary binarization result by filling the partitioned binarized image with the seeds.Finally, the final binarization result is obtained by remedying the primary binarizationresult with the binarized image generalized using a low threshold. Compared with theclassical binarization methods and the binarization methods similar with our method thatare based on the edge information, our method is effective on the binarization of imageswith low contrast and inhomogeneous object intensities.
(2) We propose a level set implementation of the Mumford-Shah model integratingprior shape statistical knowledge. The statistical shape based approach to the image segmentation using levcl sets mainly consists of the constructions of the prior shape modeland the shape energy term. Aiming at these two parts, we mainly do two pieces of work: (1)A simple and feasible construction method of the prior shape model is proposed, which isbased on binary images; (2) A new construction method of shape energy term is presented,which considers the global and local shape information at the same time, and withoutintroducing pose parameters makes evolving surface stable.
(3) This paper describes a fast and active texture segmentation approach based on theorientation and the local variance. First, a set of feature images are extracted using theorientation and the local variance. To reduce the computational complexity, a separabilitymeasurement method, which is used for selecting four feature images with goodseparability in four orientations, is proposed in this paper. To improve the segmentation,we adopt a nonlinear diffusion filtering to smooth the four feature images. Finally, avariational framework incorporating these features in a level set based, unsupervisedsegmentation process is adopted. To improve the computational speed, instead of solvingthe Euler-Lagrange equation, we calculate the energy, with level set representation, tosolve the variational framework. Segmentation results of various synthetic and realtextured images has demonstrated that our method has good performance and efficiency.
(4) This paper presents three object tracking methods based on object contours:
●This paper present an object tracking method using edge-based shape matching isproposed, which presents a new shape similarity measurement according to the imageedge. To guarantee the tracking precision and handle the local distortion of objects, wepresent a new shape similarity measurement method based on the edge image in theweighted narrow band.
●A parametric active contour model is presented for object tracking based on matchingdegree image of object contour points. We first construct a matching degree imageaccording to object contour points, and track the object using parametric activecontours. This paper presents a new feature matching approach and a new directionalfilter. Assuming that the motion of objects is small in this paper, we constrain themotion of object contour within the contour vicinity defined by a band, which isconstructed by the generation method of narrow band of level set. Experimental resultsdemonstrate that our method can effectively track rigid and non-rigid objects.
●This paper presents a two-stage object tracking method by combining region-basedmethod and contour-based method. First, a kernel-based method is adopted to locatethe object region. Then diffusion snake is used to evolve the object contour in order to improve the tracking precision. In the first object localization stage, the initial targetposition is predicted and evaluated by Kalman filter and Bhattacharyya coefficient,respectively. In the contour evolution stage, the active contour is evolved based on theobject feature image that is generated with the color space component array (CSCA).In the process of the evolution, similarities of the target region are compared to ensurethat the object contour evolves in the right way.
(5) This paper presents two segmentation methods of left ventricle MR images:
●According to the characteristics of tagged MR images, we introduced an automaticsegmentation method based on multistage hybrid processing. First the left ventricle islocated using morphological method, and then the inner and outer contours areinitialized using k-mean clustering, templet matching and the myocardium shaperestoration based on skeleton. At last, the initial contour lines are evolved usingimproved level set method to achieve object boundaries.
●According to the characteristics of MR images without tag lines, we present animproved shape statistics variational approach for the outer contour segmentation ofleft ventricle MR images. We use the Mumford-Shah model in an object feature spaceand incorporate the shape statistics and an edge image to the variational framework.The introduction of shape statistics can improve the segmentation with brokenboundaries. The edge image can enhance the weak boundary and thus improve thesegmentation precision. The generation of the object feature image which hashomogenous "intensities" in the left ventricle facilitates the application of theMumford-Shah model.
(1)综合考虑了边缘信息和灰度信息，提出了一种新的双阈值二值化方法。该方法首先通过在图像边缘附近寻找种子点，然后在由封闭轮廓划分的高阈值二值图像中进行种子填充得到初步的二值化结果，最后通过低阈值二值化图像的修补得到最后的二值化结果。实验结果表明此方法能够较好地解决低对比度和目标象素灰度不均匀图像的二值化问题，与传统的二值化方法相比，也与本文类似的结合边缘信息的二值化方法相比，本文的方法具有一定的优点。
(2)给出了一种结合先验形状统计信息的Mumford-Shah模型的水平集实现方法。结合形状统计的图像分割方法主要包括先验形状模型的构造和形状能量项的构造。本文针对这两个主要方面做了如下两点工作：首先提出了一种简单可行的先验形状模型构造方法；再者则是重新构造了形状能量项，它综合考虑了全局和局部形状信息，且不含形状姿态参量，使曲面演化稳定可靠。
(3)提出了一种基于方向局部方差的快速主动纹理分割方法。首先，采用方向局部方差得到一组特征图像，为了降低计算的复杂度，通过可分离性评价准则，选出可分离性较好的四个方向上的特征图像；然后采用非线性扩散滤波平滑待分割特征图像，以改善分割效果；最后通过变差框架将特征图像结合到基于水平集的无监督分割过程中，为了提高算法的运算速度，采用了直接计算能量泛函的方法求解变差公式，而不需要求解欧拉-拉格朗日方程。合成图像和真实图像的分割结果证明：该方法具有一定的通用性和较好的性能。
(4)提出了三种基于目标轮廓的运动跟踪方法：
●提出了一种新的基于边缘形状匹配的目标跟踪方法，主要利用图像的边缘信息来构造目标的形状相似度。为了保证跟踪精度和处理目标的局部变形，本文提出了一种新的基于权重窄带的形状匹配度计算方法。
●提出了一种基于目标轮廓点匹配度的参数活动轮廓目标跟踪方法。基本思想是根据目标轮廓点构造匹配度图像，然后采用参数活动轮廓模型跟踪目标。本文构造了一种新的特征匹配方法和一种新的方向滤波方法。考虑到相邻两帧间的目标运动较小，本文将目标轮廓的演化限定在初始轮廓的窄带区域内，采用了窄带水平集的窄带构造方法。
●将基于区域的方法与基于边界的方法相结合，提出了一种两阶段目标跟踪方法。首先采用基于核的目标跟踪方法快速定位目标区域，然后采用扩散snake进一步演化目标轮廓，精确定位目标边界。在初始目标定位阶段，为了能够有效地给出目标的初始跟踪位置，用kalman滤波对初始目标位置进行预测，同时通过Bhattacharyya系数进行进一步判断。在基于轮廓的精确定位阶段，采用了颜色空间成分阵列的方法生成目标特征图像，然后在特征图像上采用扩散snake进行边界演化，同时在演化过程中，为了保证目标轮廓向好的方向演化，对演化得到的目标区域进行相似性比较。
(5)提出了两种左心室核磁共振图像的分割方法：
●针对带标记线左心室核磁共振图像的特点，提出了一种基于分级处理的自动分割方法，主要由三部分组成：首先用数学形态学的方法实现左心室的自动定位；然后用k均值聚类、模板匹配和基于骨架的心肌形状恢复方法给出左心室的内外初始轮廓线；最后用改进的水平集方法对初始轮廓线进行演化而得到最终结果。
●针对不带标记线左心室核磁共振图像外轮廓的特点，在变差框架下，通过将Mumford-Shah模型作用于目标特征空间，同时结合形状统计先验知识和边缘图像，本文提出了一种改进的结合形状统计的变差方法来分割核磁共振图像的左心室外轮廓。形状先验知识的引入较好地克服了左心室外轮廓的边界断裂现象；边缘图像可以增强弱边缘，提高分割精度：通过以心肌为目标区域建立目标特征图像，使得左心室内部区域“灰度”一致，从而方便了Mumford-Shah模型的应用。
Image segmentation is a process of dividing an image into different regions such thateach region is, but the union of any two adjacent regions is not, homogeneous. Imagesegmentation is the first step in image understanding and pattern recognition. The basicdevelopment process of the image segmentation is as follows: the early classicalsegmentation methods based on image intensity and gradient, such as thresholding,contour-based, and region-based; the eighties active contour models, such as parametricactive contours and geometric active contours; the segmentation methods with priorknowledge, such as active shape model and active appearance model. From thedevelopment process, we can observe that the intelligence and ability of the imagesegmentation become better and better. In this paper, the following information is mainlyused to segment objects, such as intensity, shape, texture and motion. We first analyze aclassical image segmentation method, image binarization, and then discuss thesegmentation methods by combining shape priors and active contours and the texturesegmentation. Finally we give some object tracking methods. According to the research ofthis paper, we want to form a union image segmentation framework with various cues(such as intensity, shape, texture, motion etc), which has a high intelligent ability in orderto solve some complex image segmentation problems. This paper also shows oneapplication, namely the left ventricle magnetic resonance (MR) image segmentation. Theprimary work and remarks of this paper are as follows:
(1) This paper presents a new double-threshold image binarization method based onthe edge and intensity information. We first find the seeds near the image edges, andpresent an edge connection method to close the image edges. Then, we use closed imageedges to partition the binarized image that is generalized using a high threshold, and obtainthe primary binarization result by filling the partitioned binarized image with the seeds.Finally, the final binarization result is obtained by remedying the primary binarizationresult with the binarized image generalized using a low threshold. Compared with theclassical binarization methods and the binarization methods similar with our method thatare based on the edge information, our method is effective on the binarization of imageswith low contrast and inhomogeneous object intensities.
(2) We propose a level set implementation of the Mumford-Shah model integratingprior shape statistical knowledge. The statistical shape based approach to the image segmentation using levcl sets mainly consists of the constructions of the prior shape modeland the shape energy term. Aiming at these two parts, we mainly do two pieces of work: (1)A simple and feasible construction method of the prior shape model is proposed, which isbased on binary images; (2) A new construction method of shape energy term is presented,which considers the global and local shape information at the same time, and withoutintroducing pose parameters makes evolving surface stable.
(3) This paper describes a fast and active texture segmentation approach based on theorientation and the local variance. First, a set of feature images are extracted using theorientation and the local variance. To reduce the computational complexity, a separabilitymeasurement method, which is used for selecting four feature images with goodseparability in four orientations, is proposed in this paper. To improve the segmentation,we adopt a nonlinear diffusion filtering to smooth the four feature images. Finally, avariational framework incorporating these features in a level set based, unsupervisedsegmentation process is adopted. To improve the computational speed, instead of solvingthe Euler-Lagrange equation, we calculate the energy, with level set representation, tosolve the variational framework. Segmentation results of various synthetic and realtextured images has demonstrated that our method has good performance and efficiency.
(4) This paper presents three object tracking methods based on object contours:
●This paper present an object tracking method using edge-based shape matching isproposed, which presents a new shape similarity measurement according to the imageedge. To guarantee the tracking precision and handle the local distortion of objects, wepresent a new shape similarity measurement method based on the edge image in theweighted narrow band.
●A parametric active contour model is presented for object tracking based on matchingdegree image of object contour points. We first construct a matching degree imageaccording to object contour points, and track the object using parametric activecontours. This paper presents a new feature matching approach and a new directionalfilter. Assuming that the motion of objects is small in this paper, we constrain themotion of object contour within the contour vicinity defined by a band, which isconstructed by the generation method of narrow band of level set. Experimental resultsdemonstrate that our method can effectively track rigid and non-rigid objects.
●This paper presents a two-stage object tracking method by combining region-basedmethod and contour-based method. First, a kernel-based method is adopted to locatethe object region. Then diffusion snake is used to evolve the object contour in order to improve the tracking precision. In the first object localization stage, the initial targetposition is predicted and evaluated by Kalman filter and Bhattacharyya coefficient,respectively. In the contour evolution stage, the active contour is evolved based on theobject feature image that is generated with the color space component array (CSCA).In the process of the evolution, similarities of the target region are compared to ensurethat the object contour evolves in the right way.
(5) This paper presents two segmentation methods of left ventricle MR images:
●According to the characteristics of tagged MR images, we introduced an automaticsegmentation method based on multistage hybrid processing. First the left ventricle islocated using morphological method, and then the inner and outer contours areinitialized using k-mean clustering, templet matching and the myocardium shaperestoration based on skeleton. At last, the initial contour lines are evolved usingimproved level set method to achieve object boundaries.
●According to the characteristics of MR images without tag lines, we present animproved shape statistics variational approach for the outer contour segmentation ofleft ventricle MR images. We use the Mumford-Shah model in an object feature spaceand incorporate the shape statistics and an edge image to the variational framework.The introduction of shape statistics can improve the segmentation with brokenboundaries. The edge image can enhance the weak boundary and thus improve thesegmentation precision. The generation of the object feature image which hashomogenous "intensities" in the left ventricle facilitates the application of theMumford-Shah model.
引文
1. M.B. Stegmann, Generative interpretation of medical images. 2004, Ph.D. Thesis.
2. J. Price, et al., Human-Computer Interaction, ed. Addison-Wesley. 1994.
3. E. Backer and A. Jain, A clustering performance measure based on fuzzy set decompositioa IEEE Trans. Pattern Anal. Mach. Intell., 1981. 3(1):66-75.
4. A. Baraldi and P. Blonda, A survey of fuzzy clustering algorithms for pattern recognition-Part I and II. IEEE Trans. Syst, Man, Cybern. B, Cybern, 1999. 29(6):778-801.
5. A. Jain, R. Duin, and J. Mao, Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell., 2000. 22(1):4-37.
6. R. Xu and D. Wunsch, Survey of clustering algorithm. IEEE Transactions on Neural Networks, 2005.16(3):645-678.
7. E. Forgy, Cluster analysis of multivariate data: efficiency vs. interpretability of classifications. Biometrics, 1965. 21(3):768-780.
8. G McLachlan and T. Krishnan, The EM algorithm and extensions. 1997, New York: Wiley.
9. X. Zhuang, et al., Gaussian mixture density modeling, decomposition, and applications. IEEE Trans. Image Process., 1996. 5(9):1293-1302.
10. R. Sharan and R. Shamir. CLICK: A clustering algorithm with applications to gene expression analysis, in 8th Int. Conf. Intelligent Systems for Molecular Biology. 2000.
11. L. Hall, I. Ozyurt, and J. Bezdek, Clustering with a genetically optimized approach. IEEE Trans. Evol. Comput., 1999. 3(2):103-112.
12. F. HOppner, F. Klawonn, and R. Kruse, Fuzzy cluster analysis: methods for classification, data analysis, and image recognition. 1999: New York: Wiley.
13. T. Kohonen. The self-organizing map. in Proceedings of the Institute of Electrical and Electronics Engineers. 1990.
14. A. Ben-Hur, et al. A support vector clustering method, in 15th International Conference on Pattern Recognition. 2000.
15. M. Kass, A. Witkin, and D. Terzopoulos, Snake: active contour models. Int. J. Computer Vision, 1987.1(4):321-331.
16. L.D. Cohen, On active contour models and balloons. Computer Vision, Graphics, Image Processing: image understanding, 1991.53(2):211-218.
17. L. Axel and L. Dougherty, Heart wail motion: improved method of spatial modulation of magnetization for MR imaging. Radiology, 1989. 172(2):349-350.
18. C. Xu and J.L. Prince, Snakes, shapes and gradient vector flow. IEEE Trans. on Imaging Processing, 1998.7(3):359-369.
19. A.A. Amini, S. Tehrani, and T.E. Weymouth. Using dynamic programming for minimising the engerge of active contours in the presence of hard constraints, in Proc Second International Conference on Computer Vision. 1988.
20. D.J. Williams and M. Shah, A fast aigorithm for active contours and curvature estimation. CVGIP (Image Understanding), 1992. 55(1): 14-26.
21. V. Caselles, R. Kimmel, and G. Sapiro, Geodesic active contours. International Joumai of Computer Vision, 1997. 22(1):61-79.
22. T.F. Cootes, G..J. Edwards, and C.J. Taylor. Active appearance models, in Proc. Fifth European Conf. Computer Vision. 1998.
23. T.F. Cootes, et al., Active shape models-their training and application. Computer Vision and Image Understanding, 1995.61(1):38-59.
24. T.F. Cootes, G.J. Edwards, and C.J. Taylor, Active appearance models. IEEE Transactions on Pattem Anaiysis and Machine Intelligence, 2001.23(6):681-685.
25. A. Lanitis, C.J. Taylor, and T.F. Cootes, Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997. 19(7):742-756.
26. S.C. Mitchell, et al., Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Transactions on medical imaging, 2001.20(5):415-423.
27. B.P.F. Lelieveldt, et al. Multi-view active appearance models for consistent segmentation of multiple standard views: application to long-and short-axis cardiac MR images, in Proceedings of the 17th International Congress and Exhibition. 2003. London, United Kingdom.
28. I. Elfadel and R. Picard, Gibbs random fields, co-occurrences and texture modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994. 16(1):24-37.
29. P. Chen and T. Pavlidis, Segmentation by texture using correlation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983.5(1):64-69.
30. G Cross and A. Jain, Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983. 5(1):25-39.
31. M. Unser, Local linear transforms for texture measurements. Signal Processing, 1986.11(1):61-79.
32. D. Dunn and W.E. Higgins, Optimal Gabor filters for texture segmentation IEEE transactions on image processing, 1995. 4(7):947-964.
33. P.P. Raghu and B. Yegnanarayana, Segmentation of Gabor-filtered textures using deterministic relaxatioa IEEE Transactions on Image Processing, 1996. 5(12):1625-1636.
34. S. Arivazhagan and L. Ganesan, Texture segmentation using wavelet transform. Pattern Recognition Letters, 2003. 24(16):3197-3203.
35. A. Laine and J. Fan, Texture classification by wavelet packet signatures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993. 15(11):1186-1191.
36. J. Bigun, G.H. Granlund, and J. Wiklund, Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991.13(8):775-790.
37. N. Paragios and R. Deriche, Geodesic active regions: A new paradigm to deal with frame partition problems in computer vision Journal of Visual Communication and Image Representation, 2002.13(1/2):249-268.
38. S. Zhu, Y. Wu, and D. Mumford, Filters, random field and maximum entropy: towards a unified theory for texture modeling. International Journal of Computer Vision, 1998. 27(2): 1-20.
39. T. Brox and J. Weickert. A TV flow based local scale measure for texture discrimination, in Proc. 8th European Conference on Computer Vision. 2004. Berlin Heidelberg.
40. J. Weichert, B.M. ter Haar Romeny, and M.A. Viergever, Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Transactions on Image Processing, 1998. 7(3):398-410.
41. X.Y. Yang and J. Liu, Unsupervised texture segmentation with one-step mean shift and boundary Markov random fields. Pattern Recognition Letters, 2001. 22(10):1073-1081.
42. T. Pavlidis and S.L. Tanimoto. Texture identification by a directed split and merge procedure, in Proc. Conf. Comput. Graph., Pattern Recogn. Data Structure. 1975.
43. B. Fischer and J.M. Buhmann, Path-based clustering for grouping of smooth curves and texture segmentatioa IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003. 25(4):513-518.
44. C.W. Therrien, An estimation-theoretic approach to terrain image segmentatioa Comput. Vision, Graph., Image Processing, 1983.22(3):313-326.
45. Paragios., N. and R. Deriche, Geodesic active regions and level set methods for supervised texture segmentatioa International Journal of Computer Vision, 2002. 46(3):223-247.
46. J. Wills, S. Agarwal, and S. Belongie. What went where, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2003.
47. P. Anandan. A unified perspective on computational techniques for the measurement of visual motion, in International Conference on Computer Vision. 1987. London.
48. B. Horn and B. Schunck, Determining optical flow. Artificial Intelligence, 1981. 17:185-203.
49. B. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision, in Proc. Seventh International Joint Conference on Artificial Intelligence. 1981. Vancouver, Canada.
50. M.J. Black and P. Anandan, The robust estimation of multiple motions: parametric and piecewise smooth flow fields. Computer Vision and Image Understanding, 1996. 63(1):75-104.
51. H.H. Nagel. Extending the 'oriented smoothness constraint' into the temporal domain and the estimation of derivatives of optical flow, in ECCV. 1990. Berlin.
52. T. Brox, et al. High accuracy optical flow estimation based on a theory for warping, in Proc. 8th European Conf. Computer Vision. 2004.
53. J.Y.A. Wang and E.H. Adelson, Representing moving images with layers. IEEE Trans. Image Processing, 1994. 3(5):625-638.
54. D. Cremers and S. Soatto, Motion competition: a variational approach to piecewise parametric motion segmentatioa International Journal of Computer Vision, 2005. 62(3):249-265.
55. D. Cremers. A variational framework for image segmentation combining motion estimation and shape regularization, in IEEE Conference on Computer Vision and Pattern Recognition. 2003.
56. S. Khan and M. Shah. Object based segmentation of video using color, motion and spatial information, in CVPR. 2001.
57. C.T. Zahn, Graph-theoretic methods for detecting and describing gestalt clusters. IEEE Transactions on Computing, 1971.20:68-86.
58. J. Shi and J. Malik, Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. 22(8):888-905.
59. P.F. Felzenszwalb and D.P. Huttenlocher, Efficient graph-based image segmentation. International Journal of Computer Vision, 2004. 59(2): 167-181.
60．闫成新，桑农，张天序，基于图论的图像分割研究进展．计算机工程与应用，2006．42(5)：11-14．
61．林开颜，吴军辉，徐立鸿，彩色图像分割方法综述．中国图像图形学报，2005．10(1)：1-10．
62. H.D. Cheng, et al., Color image segmentation: advances and prospects. Pattern Recognition, 2001.34(12):2259-2281.
63. J.Q. Liu and Y.H. Yang, Multiresolution color image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994. 16(7):689-700.
64. N.R. Pal and S.K. Pal, A review on image segmentation techniques. Pattern Recognition, 1993.26(9): 1277-1294.
65. A. Hoover, et al., An experimental comparison of range image segmentation algorithms. IEEE Transactions on Pattem Analysis and Machine Intelligence, 1996. 18(7):673-689.
66. J. Min and K.W. Bowyer, Improved range image segmentation by analyzing surface fit patterns. Computer Vision and Image Understanding, 2005. 97(2):242-258.
67. S.C. Zhu and A. Yuille, Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996. 18(9):884-900.
68. J. Kim, et al. Nonpararnetric methods for image segmentation using information theory and curve evolution, in ICIP. 2002. Rochester, NY.
69. R. Vidal, Y. Ma, and S. Sastry. Generalized principal component analysis (GPCA). in CVPR. 2003.
70. J.C. Simon, O. Baret, and N. Gorski. A system for the recognition of handwritten literal amounts of checks, in Proceedings of the Conference on Document Analysis system. 1994. Kaiserslautem, Germany.
71. M. Kamel and A. Zhao, Extraction of binary character/graphics images from grayscale document images. Graph. Models Image Process, 1993.55(3):203-217.
72. M.L. Feng and Y.P. Tan. Adaptive binarization method for document image analysis, in IEEE International Conference on Multimedia and Expo (ICME). 2004.
73. B. Gatos, I. Pratikakis, and S.J. Perantonis, Adaptive degraded document image binadzation. Pattern Recognition, 2006.39(3):317-327.
74．李建华，马小妹，郭成安，基于方向图的动态阈值指纹图像二值化方法．大连理工大学学报，2002．42(5)：626-628．
75. N. Otsu, A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979.9(1):62-66.
76. J.N. Kapur, P. K. Sahoo, and A.K. Wong, A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 1985.29(8):273-285.
77. J. Kittler, J. Illingworth, and J. Foglein, Minimum error thresholding. Pattern Recognition, 1986. 19(1):41-47.
78. M. Cheriet, J. N. Said, and C.Y. Suen, A recursive thresholding technique for image segmentation. IEEE Trans Image Process, 1998.7(6):918-921.
79. J. Bernsen. Dynamic thresholding of gray-level images, in Proceeding of 8th International Conference on Pattern Recognition. 1986. Paris, France: IEEE Computer Society Press.
80. W. Niblack, An introduction to digital image processing. 1986, Englewood Cliffs, N.J.: Prentice Hall. 115-116.
81. S.D. Yanowitz and A.M. Bruckstein, A new method for image segmentation. Computer Vision, Graphics and Image Processing, 1989. 46(1):82-95.
82. J. Sauvola, et al. Adaptive document binadzation, in International Conference on Document Analysis and Recognition. 1997.
83. φ.D. Trier and A.K. Jaln, Goal-directed evaluation of binarization methods. IEEE Trans. Pattern Anal. Math. Intell., 1995.17(12):1191-1201.
84. M. Sezgin and B. Sankur, Survey over image thresholding techniques and quantitative performance evaluztion. Journal of Electronic Imaging, 2004. 13(1):146-168.
85．赵雪松，陈淑珍，综合全局二值化与边缘检测的图像分割方法．计算机辅助设计与图形学学报，2001．13(2)：118-121．
86. R. Cao, et al. Segmentation and analysis of double-sided handwritten archival documents, in Proc. Fourth MPR Int'l Workshop Document Analysis Systems. 2000.
87. I. Blayvas, A. Bruckstein, and R. Kimmel, Efficient computation of adaptive threshold surface for image binarization Pattern Recognition, 2006.39(1):89-101.
88. J. Canny, A computational approach to edge detectioa IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986. 8(6):679-698.
89. M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision - SECOND EDITION, ed. Brooks/Cole. 1999.
90. W.Y. Ma and B.S. Manjunath, Edgeflow: a technique for boundary detection and image segmentation. IEEE Transactions on Image Processing, 2000. 9(8):1375-1388.
91. D. Cremers, et al., Diffusion snakes: introducing statistical shape knowledge into the Mumford-Shah functional. International Journal of Computer Vision, 2002. 50(3):295-313.
92. D. Cremers, T. Kohlberger, and C. Schnorr, Shape statistics in kernel space for variational image segmentation Pattern Recognition, 2003. 36(9): 1929-1943.
93. M.E. Leventon, W.E.L. Grimson, and O. Faugeras. Statistical shape influence in geodesic active contours, in Proc. IEEE Conf. Comp. Vision and Patt. Recog. 2000.
94. Y. Chen, et al. On the incorporation of shape priors into geometric active contours. in Proc. IEEE Workshop Variational and Level Set Methods. 2001.
95. N. Paragios and M. Rousson. Shape priors for level set representations, in ECCV. 2002. Copenhagen, Denmark.
96. A. Tsai, et al., A shape-based approach to the segmentation of medical imagery using level sets. IEEE transactions on medical imaging, 2003. 22(2): 137-154.
97. D. Cremers and S. Soatto. A pseudo-distance for shape priors in level set segmentation, in 2nd IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision. 2003. Nice.
98. N. Paragios, M. Rousson, and V. Ramesh. Matching distance functions: a shape-to-area variational approach for global-to-local registration, in ECCV. 2002.
99. D. Mumford and J. Shah, Optimal approximation by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 1989. 42(5):577-685.
100. S. Osher and J.A. Sethian, Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulation. Comput. Phys., 1988. 79(1):12-49.
101. T.F. Chan and L.A. Vese, Active contours without edges. IEEE Transactions on Image Processing, 2001.10(2):266-277.
102. N.F. Osman, E.R. Mcveigh, and J.L. Prince, Imaging heart motion using harmonic phase MRI. IEEE Transactions on Medical Imaging, 2000. 19(3): 186-202.
103. D.M. Tsai, S.K. Wu, and M.C. Chen, Optimal Gabor filter design for texture segmentation using stochastic optimization. Image and Vision Computing, 2001. 19(5):299-316.
104．吴高洪，章毓晋，林行刚，分割双纹理图像的最佳Gabor滤波器设计方法．电子学报，2001．29(1)：48-50．
105. P. Perona and J. Malik, Scale space and edge detection using anisotropic diffusion. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1990. 12(7):629-639.
106. G. Gerig, et al., Nonlinear anisotropic filtering of MRI data. IEEE Transactions on Medical Imaging, 1992. 11 (2):221-232.
107. M. Rousson and R. Deriche. A variational framework for active and adaptative segmentation of vector valued images, in Proc. IEEE Workshop on Motion and Video Computing. 2002. Orlando, Florida.
108. M. Rousson, T. Brox, and R. Deriche. Active unsupervised texture segmentation on a diffusion based feature space, in Conference on Computer Vision and Pattern Recognition Ⅱ. 2003. Madison, Wisconsin.
109. B. Song and T. Chan, Fast algorithm for level set based optimization, in UCLA CAM Report. 2002.
110. P. Brodatz, Textures: a photographic album for artists and designers. 1966: New York: Dover.
111. R. Haralick, K. Shanmugam, and I. Dinstein, Textural features for image classification. IEEE Trans. Systems, Man, Cybernetics, 1973.3(6):610-621.
112. A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters. Pattern Recognition, 1991.24(12): 1167-1186.
113. I. Ng, T. Tan, and J. Kittler. On local linear transform and Gabor filter representation of texture, in Proc. 11th International Conference on Pattern Recognition. 1992.
114. L.A. Vese and T.F. Chan, A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision, 2002. 50(3):271-293.
115. U. Handmann, et al. Computer vision for driver assistance systems, in Proceedings SPIE. 1998.
116. S. Avidan. Support vector tracking, in Proc. IEEE Conf, on Computer Vision and Pattern Recognition. 2000. Kauai, Hawaii.
117. J. Aggarwal and Q. Cai, Human motion analysis: a review. Computer Vision and Image Understanding, 1999. 73(3):428-440.
118. D. Gavrila, The visual analysis of human movement: a survey. Computer Vision and Image Understanding, 1999. 73(1):82-98.
119. V. Kettnaker and R. Zabih. Bayesian multi-camera surveillance, in Proc. IEEE Conf, on Computer Vision and Pattern Recognition. 1999. Fort Collins, CO.
120. M. Lee, et al., A layered video object coding system using sprite and affine motion model. IEEE Transactions on Circuits Systems for Video Technology, 1997. 7(1):130-145.
121. A.D. Bue, et al. Smart cameras with real-time video object generation, in Proc. IEEE Intl. Conf, on Image Processing. 2002. Rochester, NY.
122. D. Comaniciu, V. Ramesh, and P. Meer, Kernel-based object tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003. 25(5):564-577.
123. C. Yang, R. Duraiswami, and L. Davis. Efficient mean-shift tracking via a new similarity measure, in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005.
124. Z. Zhu, Q. Ji, and K. Fujimura. Combining Kalman filtering and mean shift for real time eye tracking under active IR illumination, in ICPR. 2002.
125. C. Chang and R. Ansari, Kernel particle filter for visual tracking. IEEE Signal Processing Letters, 2005.12(3):242-245.
126. K. Deguchi, O. Kawanaka, and T. Okatani. Object tracking by the mean-shift of regional color distribution combined with the particle-filter algorithm, in ICPR. 2004.
127. F.H. Cheng and Y.L. Chen, Real time multiple objects tracking and identification based on discrete wavelet transform. Pattern Recognition, 2006. 39(6): 1126-1139.
128. D. Conte, et al., A graph-based, multi-resolution algorithm for tracking objects in presence of occlusions. Pattern Recognition, 2006. 39(4):562-572.
129. N. Peterfreund, The velocity snake: deformable contour for tracking in spatio-velocity space. Computer Vision and Image Understanding, 1999. 73(3):346-356.
130. S. Sun, D.R. Haynor, and Y. Kim, Semiautomatic video object segmentation using VSnakes. IEEE Trans. Circuits Syst. Video Technol., 2003.13(1):75-82.
131. S.C. Park, et al., Tracking non-rigid objects using probabilistic Hausdorff distance matching. Pattern Recognition, 2005.38(12):2373-2384.
132. R. Feghali and A. Mitiche, Spatiotemporal motion boundary detection and motion boundary velocity estimation for tracking moving objects with a moving camera: a level sets PDEs approach with concurrent camera motion compensatioa IEEE Transactions on Image Processing, 2004.13(11): 1473-1490.
133. N. Paragios and R. Deriche, Geodesic active regions and level set methods for motion estimation and tracking. Computer Vision and Image Understanding, 2005. 97(3):259-282.
134. N. Peterfreund, Robust tracking of position and velocity with Kalman snakes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999. 21(6):564-569.
135. A. Yilmaz, X. Li, and M. Shah, Contour-based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004. 26(11): 1531-1536.
136. D. Adalsteinsson and J.A. Sethian, A fast level set method for propagating interfaces. Journal of Computational Physics, 1995.118(2):269-277.
137. Q. Chen, et al. Level set based auto segmentation of the tagged left ventricle MR images, in Medicine Meets Virtual Reality 12. 2004: IOS Press.
138. L. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms. Physica D, 1992. 60(1-4):259-268.
139. L. O'Gorman and J.V. Nickerson, An approach to fingerprint filter desiga Pattern Recognition, 1989. 22(1):29-38.
140. L. Hong, Y.F. Wan, and A. Jain, Fingerprint image enhancement algorithm and performance evaluatioa IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998.20(8):777-789.
141. G Doretto, et al. Dynamic texture segmentation, in Proceedings of ICCV. 2003. Nice, France.
142. M. de Bruijne and M. Nielsen. Shape particle filtering for image segmentation, in MICCAL 2004. LNCS.
143. Q. Chen, et al., Shape Statistics Variational Approach for the Outer Contour Segmentation of Left Ventricle MR Images. IEEE Transactions on Information Technology in BioMedicine, 2006.10(3):588-597.
144. G.R. Bradski, Computer vision face tracking for use in a perceptual user interface. Intelligence Technology Journal, 1998.2(2): 1-15.
145. J.G. Allen, R.Y.D. Xu, and J.S. Jin. Object tracking using camshaft algorithm and multiple quantized feature spaces, in Workshop on Visual Information Processing. 2003. Sydney, Australia.
146. R.T. Collins. Mean-shift blob tracking through scale space, in CVPR. 2003.
147. J. Jia, et al. Tracking of objects in image sequences using multi-freeness mean shift algorithm, in Proceedings of the Fourth International Conference on Machine Learning and Cybernetics. 2005. Guangzhou.
148. R.E. Kalman, A new approach to linear filtering and prediction problems. Transaction of the ASME-Joumal of Basic Engineering, 1960.82(D):35-45.
149. G. Welch and G. Bishop, An introduction to the Kalmanfilter. 2004, TR95-041.
150. J. Bruce, T. Balch, and M. Veloso. Fast and cheap color image segmentation for interactive robots, in Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2000.
151. R. Vidal, Generalized principal component analysis (GPCA): an algebraic geometric approach to subspace clustering and motion segmentation. 2003, Berkeley, PhD Thesis.
152．彭宁嵩，杨杰，刘志，张凤超，Mean-Shift跟踪算法中核函数窗宽的自动选取．软件学报，2005．16(9)：1542-1550．
153．陈强，周则明，王平安，夏德深，带标记线左心室MR图象的自动分割．中国图像图形学报，2004．9(6)：666-673．
154. R.M.V. Piva and R.I. Kitney. Fuzzy-based automatic detection of tags and ventricular contours for wall motion analysis of cardiac magnetic resonance images, in Computers in Cardiology. 1995. New York, USA.
155. M.P. Jolly, N. Duta, and G. Funka-Lea. Segmentation of the left ventricle in cardiac MR images, in International Conference on Computer Vision. 2001.
156．章毓晋，图像工程上册：图像处理和分析．1992，北京：清华大学出版社．224-226．
157．边肇祺，张学工等，模式识别(第二版)．2001，北京：清华大学出版社． 234-237.
158. A. Telea and J.J. van Wijk. An augmented fast marching method for computing skeletons and centerlines, in IEEE TCVG Symposium on Visualization. 2002. Barcelona, Spain.
159. T.Y. Zhang and C.Y. Suen, A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 1984. 27(3):236-240.
160. G.S. Jiang and C.W. Shu, Efficient implementation of weighted ENO schemes. Journal of Computational Physics, 1996. 126(1):202-228.
161. D. Peng, et al., A PDE-based fast local level set method. Journal of Computational Physics, 1999. 155(2):410-438.
162. R. Malladi, J.A. Sethian, and B.C. Vemuri, Shape modeling with front propagation: a level set approach. IEEE Trans.on Pattern Analysis and Machine Intelligence, 1995.17(2): 158-175.
163. S. Kichenassamy, et al., Conformal curvatures flows: from phase transitions to active vision. Archive of Rational Mechanics and Analysis, 1996. 134(3):275-301.
164. C.G. Looney, Pattern recognition using neural network. 1997, England: Oxford University Press.
165. N. Paragios, A variational approach for the segmentation of the left ventricle in MR cardiac images. Int. J. Comput. Vis., 2002.50(3):345-362.
166. A. Taleb-Ahmed, X. Leclerc, and T.S. Michel. Semi-automatic segmentation of vessels by mathematical morphology: application in MRI. in International Conference on Image Processing. 2001. Palermo, Italy.
167．陈强，王平安，夏德深，形状统计Mumford-Shah模型的MR图像左心室外轮廓分割．计算机学报，2006．29(11)：2044-2051．
168．黄福珍，苏剑波，席裕庚，基于几何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．
169. M. Werman and D. Weinshall, Similarity and affine invariant distances between 2d point sets. IEEE Trans. Pattern Anal. Math. Intell., 1995.17(8):810-814.
170. I.L. Dryden and K.V. Mardia, Statistical shape analysis, ed. J. Wiley. 1998, Chichester.
171. I. Mikic, S. Krucinski, and J.D. Thomas, Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates. IEEE Trans on medical imaging, 1998.17(2):274-283.
172. M.B. Stegmann, R. Fisker, and B.K. Ersbφll. Extending and applying active appearance models for automated, high precision segmentation in different image modalities, in Proceedings of the 12th Scandinavian Conference on Image Analysis. 2001. Stavanger, Norway.
173. D. Cremers, M. Rousson, and R. Deriche, A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision, 2007. 72(2): 195-215.
2. J. Price, et al., Human-Computer Interaction, ed. Addison-Wesley. 1994.
3. E. Backer and A. Jain, A clustering performance measure based on fuzzy set decompositioa IEEE Trans. Pattern Anal. Mach. Intell., 1981. 3(1):66-75.
4. A. Baraldi and P. Blonda, A survey of fuzzy clustering algorithms for pattern recognition-Part I and II. IEEE Trans. Syst, Man, Cybern. B, Cybern, 1999. 29(6):778-801.
5. A. Jain, R. Duin, and J. Mao, Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell., 2000. 22(1):4-37.
6. R. Xu and D. Wunsch, Survey of clustering algorithm. IEEE Transactions on Neural Networks, 2005.16(3):645-678.
7. E. Forgy, Cluster analysis of multivariate data: efficiency vs. interpretability of classifications. Biometrics, 1965. 21(3):768-780.
8. G McLachlan and T. Krishnan, The EM algorithm and extensions. 1997, New York: Wiley.
9. X. Zhuang, et al., Gaussian mixture density modeling, decomposition, and applications. IEEE Trans. Image Process., 1996. 5(9):1293-1302.
10. R. Sharan and R. Shamir. CLICK: A clustering algorithm with applications to gene expression analysis, in 8th Int. Conf. Intelligent Systems for Molecular Biology. 2000.
11. L. Hall, I. Ozyurt, and J. Bezdek, Clustering with a genetically optimized approach. IEEE Trans. Evol. Comput., 1999. 3(2):103-112.
12. F. HOppner, F. Klawonn, and R. Kruse, Fuzzy cluster analysis: methods for classification, data analysis, and image recognition. 1999: New York: Wiley.
13. T. Kohonen. The self-organizing map. in Proceedings of the Institute of Electrical and Electronics Engineers. 1990.
14. A. Ben-Hur, et al. A support vector clustering method, in 15th International Conference on Pattern Recognition. 2000.
15. M. Kass, A. Witkin, and D. Terzopoulos, Snake: active contour models. Int. J. Computer Vision, 1987.1(4):321-331.
16. L.D. Cohen, On active contour models and balloons. Computer Vision, Graphics, Image Processing: image understanding, 1991.53(2):211-218.
17. L. Axel and L. Dougherty, Heart wail motion: improved method of spatial modulation of magnetization for MR imaging. Radiology, 1989. 172(2):349-350.
18. C. Xu and J.L. Prince, Snakes, shapes and gradient vector flow. IEEE Trans. on Imaging Processing, 1998.7(3):359-369.
19. A.A. Amini, S. Tehrani, and T.E. Weymouth. Using dynamic programming for minimising the engerge of active contours in the presence of hard constraints, in Proc Second International Conference on Computer Vision. 1988.
20. D.J. Williams and M. Shah, A fast aigorithm for active contours and curvature estimation. CVGIP (Image Understanding), 1992. 55(1): 14-26.
21. V. Caselles, R. Kimmel, and G. Sapiro, Geodesic active contours. International Joumai of Computer Vision, 1997. 22(1):61-79.
22. T.F. Cootes, G..J. Edwards, and C.J. Taylor. Active appearance models, in Proc. Fifth European Conf. Computer Vision. 1998.
23. T.F. Cootes, et al., Active shape models-their training and application. Computer Vision and Image Understanding, 1995.61(1):38-59.
24. T.F. Cootes, G.J. Edwards, and C.J. Taylor, Active appearance models. IEEE Transactions on Pattem Anaiysis and Machine Intelligence, 2001.23(6):681-685.
25. A. Lanitis, C.J. Taylor, and T.F. Cootes, Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997. 19(7):742-756.
26. S.C. Mitchell, et al., Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Transactions on medical imaging, 2001.20(5):415-423.
27. B.P.F. Lelieveldt, et al. Multi-view active appearance models for consistent segmentation of multiple standard views: application to long-and short-axis cardiac MR images, in Proceedings of the 17th International Congress and Exhibition. 2003. London, United Kingdom.
28. I. Elfadel and R. Picard, Gibbs random fields, co-occurrences and texture modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994. 16(1):24-37.
29. P. Chen and T. Pavlidis, Segmentation by texture using correlation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983.5(1):64-69.
30. G Cross and A. Jain, Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983. 5(1):25-39.
31. M. Unser, Local linear transforms for texture measurements. Signal Processing, 1986.11(1):61-79.
32. D. Dunn and W.E. Higgins, Optimal Gabor filters for texture segmentation IEEE transactions on image processing, 1995. 4(7):947-964.
33. P.P. Raghu and B. Yegnanarayana, Segmentation of Gabor-filtered textures using deterministic relaxatioa IEEE Transactions on Image Processing, 1996. 5(12):1625-1636.
34. S. Arivazhagan and L. Ganesan, Texture segmentation using wavelet transform. Pattern Recognition Letters, 2003. 24(16):3197-3203.
35. A. Laine and J. Fan, Texture classification by wavelet packet signatures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993. 15(11):1186-1191.
36. J. Bigun, G.H. Granlund, and J. Wiklund, Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991.13(8):775-790.
37. N. Paragios and R. Deriche, Geodesic active regions: A new paradigm to deal with frame partition problems in computer vision Journal of Visual Communication and Image Representation, 2002.13(1/2):249-268.
38. S. Zhu, Y. Wu, and D. Mumford, Filters, random field and maximum entropy: towards a unified theory for texture modeling. International Journal of Computer Vision, 1998. 27(2): 1-20.
39. T. Brox and J. Weickert. A TV flow based local scale measure for texture discrimination, in Proc. 8th European Conference on Computer Vision. 2004. Berlin Heidelberg.
40. J. Weichert, B.M. ter Haar Romeny, and M.A. Viergever, Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Transactions on Image Processing, 1998. 7(3):398-410.
41. X.Y. Yang and J. Liu, Unsupervised texture segmentation with one-step mean shift and boundary Markov random fields. Pattern Recognition Letters, 2001. 22(10):1073-1081.
42. T. Pavlidis and S.L. Tanimoto. Texture identification by a directed split and merge procedure, in Proc. Conf. Comput. Graph., Pattern Recogn. Data Structure. 1975.
43. B. Fischer and J.M. Buhmann, Path-based clustering for grouping of smooth curves and texture segmentatioa IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003. 25(4):513-518.
44. C.W. Therrien, An estimation-theoretic approach to terrain image segmentatioa Comput. Vision, Graph., Image Processing, 1983.22(3):313-326.
45. Paragios., N. and R. Deriche, Geodesic active regions and level set methods for supervised texture segmentatioa International Journal of Computer Vision, 2002. 46(3):223-247.
46. J. Wills, S. Agarwal, and S. Belongie. What went where, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2003.
47. P. Anandan. A unified perspective on computational techniques for the measurement of visual motion, in International Conference on Computer Vision. 1987. London.
48. B. Horn and B. Schunck, Determining optical flow. Artificial Intelligence, 1981. 17:185-203.
49. B. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision, in Proc. Seventh International Joint Conference on Artificial Intelligence. 1981. Vancouver, Canada.
50. M.J. Black and P. Anandan, The robust estimation of multiple motions: parametric and piecewise smooth flow fields. Computer Vision and Image Understanding, 1996. 63(1):75-104.
51. H.H. Nagel. Extending the 'oriented smoothness constraint' into the temporal domain and the estimation of derivatives of optical flow, in ECCV. 1990. Berlin.
52. T. Brox, et al. High accuracy optical flow estimation based on a theory for warping, in Proc. 8th European Conf. Computer Vision. 2004.
53. J.Y.A. Wang and E.H. Adelson, Representing moving images with layers. IEEE Trans. Image Processing, 1994. 3(5):625-638.
54. D. Cremers and S. Soatto, Motion competition: a variational approach to piecewise parametric motion segmentatioa International Journal of Computer Vision, 2005. 62(3):249-265.
55. D. Cremers. A variational framework for image segmentation combining motion estimation and shape regularization, in IEEE Conference on Computer Vision and Pattern Recognition. 2003.
56. S. Khan and M. Shah. Object based segmentation of video using color, motion and spatial information, in CVPR. 2001.
57. C.T. Zahn, Graph-theoretic methods for detecting and describing gestalt clusters. IEEE Transactions on Computing, 1971.20:68-86.
58. J. Shi and J. Malik, Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. 22(8):888-905.
59. P.F. Felzenszwalb and D.P. Huttenlocher, Efficient graph-based image segmentation. International Journal of Computer Vision, 2004. 59(2): 167-181.
60．闫成新，桑农，张天序，基于图论的图像分割研究进展．计算机工程与应用，2006．42(5)：11-14．
61．林开颜，吴军辉，徐立鸿，彩色图像分割方法综述．中国图像图形学报，2005．10(1)：1-10．
62. H.D. Cheng, et al., Color image segmentation: advances and prospects. Pattern Recognition, 2001.34(12):2259-2281.
63. J.Q. Liu and Y.H. Yang, Multiresolution color image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994. 16(7):689-700.
64. N.R. Pal and S.K. Pal, A review on image segmentation techniques. Pattern Recognition, 1993.26(9): 1277-1294.
65. A. Hoover, et al., An experimental comparison of range image segmentation algorithms. IEEE Transactions on Pattem Analysis and Machine Intelligence, 1996. 18(7):673-689.
66. J. Min and K.W. Bowyer, Improved range image segmentation by analyzing surface fit patterns. Computer Vision and Image Understanding, 2005. 97(2):242-258.
67. S.C. Zhu and A. Yuille, Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996. 18(9):884-900.
68. J. Kim, et al. Nonpararnetric methods for image segmentation using information theory and curve evolution, in ICIP. 2002. Rochester, NY.
69. R. Vidal, Y. Ma, and S. Sastry. Generalized principal component analysis (GPCA). in CVPR. 2003.
70. J.C. Simon, O. Baret, and N. Gorski. A system for the recognition of handwritten literal amounts of checks, in Proceedings of the Conference on Document Analysis system. 1994. Kaiserslautem, Germany.
71. M. Kamel and A. Zhao, Extraction of binary character/graphics images from grayscale document images. Graph. Models Image Process, 1993.55(3):203-217.
72. M.L. Feng and Y.P. Tan. Adaptive binarization method for document image analysis, in IEEE International Conference on Multimedia and Expo (ICME). 2004.
73. B. Gatos, I. Pratikakis, and S.J. Perantonis, Adaptive degraded document image binadzation. Pattern Recognition, 2006.39(3):317-327.
74．李建华，马小妹，郭成安，基于方向图的动态阈值指纹图像二值化方法．大连理工大学学报，2002．42(5)：626-628．
75. N. Otsu, A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979.9(1):62-66.
76. J.N. Kapur, P. K. Sahoo, and A.K. Wong, A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 1985.29(8):273-285.
77. J. Kittler, J. Illingworth, and J. Foglein, Minimum error thresholding. Pattern Recognition, 1986. 19(1):41-47.
78. M. Cheriet, J. N. Said, and C.Y. Suen, A recursive thresholding technique for image segmentation. IEEE Trans Image Process, 1998.7(6):918-921.
79. J. Bernsen. Dynamic thresholding of gray-level images, in Proceeding of 8th International Conference on Pattern Recognition. 1986. Paris, France: IEEE Computer Society Press.
80. W. Niblack, An introduction to digital image processing. 1986, Englewood Cliffs, N.J.: Prentice Hall. 115-116.
81. S.D. Yanowitz and A.M. Bruckstein, A new method for image segmentation. Computer Vision, Graphics and Image Processing, 1989. 46(1):82-95.
82. J. Sauvola, et al. Adaptive document binadzation, in International Conference on Document Analysis and Recognition. 1997.
83. φ.D. Trier and A.K. Jaln, Goal-directed evaluation of binarization methods. IEEE Trans. Pattern Anal. Math. Intell., 1995.17(12):1191-1201.
84. M. Sezgin and B. Sankur, Survey over image thresholding techniques and quantitative performance evaluztion. Journal of Electronic Imaging, 2004. 13(1):146-168.
85．赵雪松，陈淑珍，综合全局二值化与边缘检测的图像分割方法．计算机辅助设计与图形学学报，2001．13(2)：118-121．
86. R. Cao, et al. Segmentation and analysis of double-sided handwritten archival documents, in Proc. Fourth MPR Int'l Workshop Document Analysis Systems. 2000.
87. I. Blayvas, A. Bruckstein, and R. Kimmel, Efficient computation of adaptive threshold surface for image binarization Pattern Recognition, 2006.39(1):89-101.
88. J. Canny, A computational approach to edge detectioa IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986. 8(6):679-698.
89. M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision - SECOND EDITION, ed. Brooks/Cole. 1999.
90. W.Y. Ma and B.S. Manjunath, Edgeflow: a technique for boundary detection and image segmentation. IEEE Transactions on Image Processing, 2000. 9(8):1375-1388.
91. D. Cremers, et al., Diffusion snakes: introducing statistical shape knowledge into the Mumford-Shah functional. International Journal of Computer Vision, 2002. 50(3):295-313.
92. D. Cremers, T. Kohlberger, and C. Schnorr, Shape statistics in kernel space for variational image segmentation Pattern Recognition, 2003. 36(9): 1929-1943.
93. M.E. Leventon, W.E.L. Grimson, and O. Faugeras. Statistical shape influence in geodesic active contours, in Proc. IEEE Conf. Comp. Vision and Patt. Recog. 2000.
94. Y. Chen, et al. On the incorporation of shape priors into geometric active contours. in Proc. IEEE Workshop Variational and Level Set Methods. 2001.
95. N. Paragios and M. Rousson. Shape priors for level set representations, in ECCV. 2002. Copenhagen, Denmark.
96. A. Tsai, et al., A shape-based approach to the segmentation of medical imagery using level sets. IEEE transactions on medical imaging, 2003. 22(2): 137-154.
97. D. Cremers and S. Soatto. A pseudo-distance for shape priors in level set segmentation, in 2nd IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision. 2003. Nice.
98. N. Paragios, M. Rousson, and V. Ramesh. Matching distance functions: a shape-to-area variational approach for global-to-local registration, in ECCV. 2002.
99. D. Mumford and J. Shah, Optimal approximation by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 1989. 42(5):577-685.
100. S. Osher and J.A. Sethian, Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulation. Comput. Phys., 1988. 79(1):12-49.
101. T.F. Chan and L.A. Vese, Active contours without edges. IEEE Transactions on Image Processing, 2001.10(2):266-277.
102. N.F. Osman, E.R. Mcveigh, and J.L. Prince, Imaging heart motion using harmonic phase MRI. IEEE Transactions on Medical Imaging, 2000. 19(3): 186-202.
103. D.M. Tsai, S.K. Wu, and M.C. Chen, Optimal Gabor filter design for texture segmentation using stochastic optimization. Image and Vision Computing, 2001. 19(5):299-316.
104．吴高洪，章毓晋，林行刚，分割双纹理图像的最佳Gabor滤波器设计方法．电子学报，2001．29(1)：48-50．
105. P. Perona and J. Malik, Scale space and edge detection using anisotropic diffusion. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1990. 12(7):629-639.
106. G. Gerig, et al., Nonlinear anisotropic filtering of MRI data. IEEE Transactions on Medical Imaging, 1992. 11 (2):221-232.
107. M. Rousson and R. Deriche. A variational framework for active and adaptative segmentation of vector valued images, in Proc. IEEE Workshop on Motion and Video Computing. 2002. Orlando, Florida.
108. M. Rousson, T. Brox, and R. Deriche. Active unsupervised texture segmentation on a diffusion based feature space, in Conference on Computer Vision and Pattern Recognition Ⅱ. 2003. Madison, Wisconsin.
109. B. Song and T. Chan, Fast algorithm for level set based optimization, in UCLA CAM Report. 2002.
110. P. Brodatz, Textures: a photographic album for artists and designers. 1966: New York: Dover.
111. R. Haralick, K. Shanmugam, and I. Dinstein, Textural features for image classification. IEEE Trans. Systems, Man, Cybernetics, 1973.3(6):610-621.
112. A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters. Pattern Recognition, 1991.24(12): 1167-1186.
113. I. Ng, T. Tan, and J. Kittler. On local linear transform and Gabor filter representation of texture, in Proc. 11th International Conference on Pattern Recognition. 1992.
114. L.A. Vese and T.F. Chan, A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision, 2002. 50(3):271-293.
115. U. Handmann, et al. Computer vision for driver assistance systems, in Proceedings SPIE. 1998.
116. S. Avidan. Support vector tracking, in Proc. IEEE Conf, on Computer Vision and Pattern Recognition. 2000. Kauai, Hawaii.
117. J. Aggarwal and Q. Cai, Human motion analysis: a review. Computer Vision and Image Understanding, 1999. 73(3):428-440.
118. D. Gavrila, The visual analysis of human movement: a survey. Computer Vision and Image Understanding, 1999. 73(1):82-98.
119. V. Kettnaker and R. Zabih. Bayesian multi-camera surveillance, in Proc. IEEE Conf, on Computer Vision and Pattern Recognition. 1999. Fort Collins, CO.
120. M. Lee, et al., A layered video object coding system using sprite and affine motion model. IEEE Transactions on Circuits Systems for Video Technology, 1997. 7(1):130-145.
121. A.D. Bue, et al. Smart cameras with real-time video object generation, in Proc. IEEE Intl. Conf, on Image Processing. 2002. Rochester, NY.
122. D. Comaniciu, V. Ramesh, and P. Meer, Kernel-based object tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003. 25(5):564-577.
123. C. Yang, R. Duraiswami, and L. Davis. Efficient mean-shift tracking via a new similarity measure, in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005.
124. Z. Zhu, Q. Ji, and K. Fujimura. Combining Kalman filtering and mean shift for real time eye tracking under active IR illumination, in ICPR. 2002.
125. C. Chang and R. Ansari, Kernel particle filter for visual tracking. IEEE Signal Processing Letters, 2005.12(3):242-245.
126. K. Deguchi, O. Kawanaka, and T. Okatani. Object tracking by the mean-shift of regional color distribution combined with the particle-filter algorithm, in ICPR. 2004.
127. F.H. Cheng and Y.L. Chen, Real time multiple objects tracking and identification based on discrete wavelet transform. Pattern Recognition, 2006. 39(6): 1126-1139.
128. D. Conte, et al., A graph-based, multi-resolution algorithm for tracking objects in presence of occlusions. Pattern Recognition, 2006. 39(4):562-572.
129. N. Peterfreund, The velocity snake: deformable contour for tracking in spatio-velocity space. Computer Vision and Image Understanding, 1999. 73(3):346-356.
130. S. Sun, D.R. Haynor, and Y. Kim, Semiautomatic video object segmentation using VSnakes. IEEE Trans. Circuits Syst. Video Technol., 2003.13(1):75-82.
131. S.C. Park, et al., Tracking non-rigid objects using probabilistic Hausdorff distance matching. Pattern Recognition, 2005.38(12):2373-2384.
132. R. Feghali and A. Mitiche, Spatiotemporal motion boundary detection and motion boundary velocity estimation for tracking moving objects with a moving camera: a level sets PDEs approach with concurrent camera motion compensatioa IEEE Transactions on Image Processing, 2004.13(11): 1473-1490.
133. N. Paragios and R. Deriche, Geodesic active regions and level set methods for motion estimation and tracking. Computer Vision and Image Understanding, 2005. 97(3):259-282.
134. N. Peterfreund, Robust tracking of position and velocity with Kalman snakes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999. 21(6):564-569.
135. A. Yilmaz, X. Li, and M. Shah, Contour-based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004. 26(11): 1531-1536.
136. D. Adalsteinsson and J.A. Sethian, A fast level set method for propagating interfaces. Journal of Computational Physics, 1995.118(2):269-277.
137. Q. Chen, et al. Level set based auto segmentation of the tagged left ventricle MR images, in Medicine Meets Virtual Reality 12. 2004: IOS Press.
138. L. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms. Physica D, 1992. 60(1-4):259-268.
139. L. O'Gorman and J.V. Nickerson, An approach to fingerprint filter desiga Pattern Recognition, 1989. 22(1):29-38.
140. L. Hong, Y.F. Wan, and A. Jain, Fingerprint image enhancement algorithm and performance evaluatioa IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998.20(8):777-789.
141. G Doretto, et al. Dynamic texture segmentation, in Proceedings of ICCV. 2003. Nice, France.
142. M. de Bruijne and M. Nielsen. Shape particle filtering for image segmentation, in MICCAL 2004. LNCS.
143. Q. Chen, et al., Shape Statistics Variational Approach for the Outer Contour Segmentation of Left Ventricle MR Images. IEEE Transactions on Information Technology in BioMedicine, 2006.10(3):588-597.
144. G.R. Bradski, Computer vision face tracking for use in a perceptual user interface. Intelligence Technology Journal, 1998.2(2): 1-15.
145. J.G. Allen, R.Y.D. Xu, and J.S. Jin. Object tracking using camshaft algorithm and multiple quantized feature spaces, in Workshop on Visual Information Processing. 2003. Sydney, Australia.
146. R.T. Collins. Mean-shift blob tracking through scale space, in CVPR. 2003.
147. J. Jia, et al. Tracking of objects in image sequences using multi-freeness mean shift algorithm, in Proceedings of the Fourth International Conference on Machine Learning and Cybernetics. 2005. Guangzhou.
148. R.E. Kalman, A new approach to linear filtering and prediction problems. Transaction of the ASME-Joumal of Basic Engineering, 1960.82(D):35-45.
149. G. Welch and G. Bishop, An introduction to the Kalmanfilter. 2004, TR95-041.
150. J. Bruce, T. Balch, and M. Veloso. Fast and cheap color image segmentation for interactive robots, in Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2000.
151. R. Vidal, Generalized principal component analysis (GPCA): an algebraic geometric approach to subspace clustering and motion segmentation. 2003, Berkeley, PhD Thesis.
152．彭宁嵩，杨杰，刘志，张凤超，Mean-Shift跟踪算法中核函数窗宽的自动选取．软件学报，2005．16(9)：1542-1550．
153．陈强，周则明，王平安，夏德深，带标记线左心室MR图象的自动分割．中国图像图形学报，2004．9(6)：666-673．
154. R.M.V. Piva and R.I. Kitney. Fuzzy-based automatic detection of tags and ventricular contours for wall motion analysis of cardiac magnetic resonance images, in Computers in Cardiology. 1995. New York, USA.
155. M.P. Jolly, N. Duta, and G. Funka-Lea. Segmentation of the left ventricle in cardiac MR images, in International Conference on Computer Vision. 2001.
156．章毓晋，图像工程上册：图像处理和分析．1992，北京：清华大学出版社．224-226．
157．边肇祺，张学工等，模式识别(第二版)．2001，北京：清华大学出版社． 234-237.
158. A. Telea and J.J. van Wijk. An augmented fast marching method for computing skeletons and centerlines, in IEEE TCVG Symposium on Visualization. 2002. Barcelona, Spain.
159. T.Y. Zhang and C.Y. Suen, A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 1984. 27(3):236-240.
160. G.S. Jiang and C.W. Shu, Efficient implementation of weighted ENO schemes. Journal of Computational Physics, 1996. 126(1):202-228.
161. D. Peng, et al., A PDE-based fast local level set method. Journal of Computational Physics, 1999. 155(2):410-438.
162. R. Malladi, J.A. Sethian, and B.C. Vemuri, Shape modeling with front propagation: a level set approach. IEEE Trans.on Pattern Analysis and Machine Intelligence, 1995.17(2): 158-175.
163. S. Kichenassamy, et al., Conformal curvatures flows: from phase transitions to active vision. Archive of Rational Mechanics and Analysis, 1996. 134(3):275-301.
164. C.G. Looney, Pattern recognition using neural network. 1997, England: Oxford University Press.
165. N. Paragios, A variational approach for the segmentation of the left ventricle in MR cardiac images. Int. J. Comput. Vis., 2002.50(3):345-362.
166. A. Taleb-Ahmed, X. Leclerc, and T.S. Michel. Semi-automatic segmentation of vessels by mathematical morphology: application in MRI. in International Conference on Image Processing. 2001. Palermo, Italy.
167．陈强，王平安，夏德深，形状统计Mumford-Shah模型的MR图像左心室外轮廓分割．计算机学报，2006．29(11)：2044-2051．
168．黄福珍，苏剑波，席裕庚，基于几何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．
169. M. Werman and D. Weinshall, Similarity and affine invariant distances between 2d point sets. IEEE Trans. Pattern Anal. Math. Intell., 1995.17(8):810-814.
170. I.L. Dryden and K.V. Mardia, Statistical shape analysis, ed. J. Wiley. 1998, Chichester.
171. I. Mikic, S. Krucinski, and J.D. Thomas, Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates. IEEE Trans on medical imaging, 1998.17(2):274-283.
172. M.B. Stegmann, R. Fisker, and B.K. Ersbφll. Extending and applying active appearance models for automated, high precision segmentation in different image modalities, in Proceedings of the 12th Scandinavian Conference on Image Analysis. 2001. Stavanger, Norway.
173. D. Cremers, M. Rousson, and R. Deriche, A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision, 2007. 72(2): 195-215.