结合视频对象分割的形状编码体系研究
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摘要
随着多媒体技术的不断成熟,人们对多媒体信息的处理方式产生了新的、更高的要求,更加注重多媒体系统的交互性和灵活性。传统的视频编解码技术是基于帧的,而MPEG-4编解码标准所采用的方法是基于对象的,就是对视频划分为不同的、连续运动的视频对象,对每个对象单独进行编码。每个对象都包括三种信息:形状信息、纹理信息和运动信息。对视频对象的编解码就需要对这三种信息进行编解码。其中MPEG-4中的核心技术之一就是形状编码部分,正因为采用了形状编码技术,MPEG-4才能够提供与众不同的能力——基于内容的交互性。
目前,国内外对MPEG-4中的视频对象分割和形状编码研究都是相互独立的,没有考虑两者之间的关联性。因此,本文在国内外现有形状编码研究成果基础上,对视频对象分割和形状编码彼此的结合做了深入研究,主要工作包括:
1、研究了基于形变模型的图象分割技术,对参数形变模型和几何形变模型技术做了分析比较,并深入研究了一种几何形变模型技术——水平集方法。
2、提出了基于水平集的运动视频对象分割方法。根据运动视频系列的特点,通过相邻帧的亮度差得到初始轮廓,以此轮廓为初始零水平集,采用窄带水平集方法,分割出运动对象。
3、提出了新的基于基准线的形状编码算法,针对现有基准算法中距离集和拐点采样算法的不足,提出了能更好的适应各种边界走向的新算法。
4、提出了结合视频对象分割的形状编码新体系,现有MPEG-4中,视频分割到形状编码的整个中间过程造成了一定的时间花费与误差,提出了在视频分割后,直接进行形状编码的新思想,可以有效的避免从分割到编码的中间过程造成的时间花费与误差。
Along with the unceasing maturity of multimedia technique, people have advanced new and higher requirements for the processing mode of multimedia and paid more attention to the interactivity and flexibility. The conventional video coding/decoding technique adopts frame-based coding technique, while MPEG-4 coding/decoding standard adopts object-based coding technique. MPEG-4 standard divides video into different、consecutively moving video objects and codes every object separately. Each object includes three kinds of information: the shape information、the texture information and movement information. The three kinds of information of every video object are needed to code. One of the core techniques in MPEG-4 is the part of shape coding. By reason of having adopted the shape coding technique, MPEG-4 has the larruping ability—the content-based interactivity.
The research on the video object segmentation and the shape coding is always isolated. Based on the progress of the research on the shape coding, the combination of the video segmentation and the shape coding is researched carefully. The main work includes:
1、The image segmentation technology based on Active contour models is researched. The parameter models and geometrical models is analysed and compared, and a geometrical models, level sets methods, is researched especially.
2、A moving object segmentation algorithm based on the level set methods is proposed. Take the luminance difference as the primary zero level set, and segment the object by narrow band algorithm.
3、A new shape coding based on the base-line is proposed. Since the shortness of the current algorithm, proposes a new algorithm which can adapt to any direction of the outline, as well as a new decode method.
4、A new system which is called shape coding system combined with the object segmentation is proposed. In MPEG-4, the middle process results in too more time cost and errors, so proposes a new system, which coding shape jusr after the object segmentation. The new system can save the time and the errors.
目前,国内外对MPEG-4中的视频对象分割和形状编码研究都是相互独立的,没有考虑两者之间的关联性。因此,本文在国内外现有形状编码研究成果基础上,对视频对象分割和形状编码彼此的结合做了深入研究,主要工作包括:
1、研究了基于形变模型的图象分割技术,对参数形变模型和几何形变模型技术做了分析比较,并深入研究了一种几何形变模型技术——水平集方法。
2、提出了基于水平集的运动视频对象分割方法。根据运动视频系列的特点,通过相邻帧的亮度差得到初始轮廓,以此轮廓为初始零水平集,采用窄带水平集方法,分割出运动对象。
3、提出了新的基于基准线的形状编码算法,针对现有基准算法中距离集和拐点采样算法的不足,提出了能更好的适应各种边界走向的新算法。
4、提出了结合视频对象分割的形状编码新体系,现有MPEG-4中,视频分割到形状编码的整个中间过程造成了一定的时间花费与误差,提出了在视频分割后,直接进行形状编码的新思想,可以有效的避免从分割到编码的中间过程造成的时间花费与误差。
Along with the unceasing maturity of multimedia technique, people have advanced new and higher requirements for the processing mode of multimedia and paid more attention to the interactivity and flexibility. The conventional video coding/decoding technique adopts frame-based coding technique, while MPEG-4 coding/decoding standard adopts object-based coding technique. MPEG-4 standard divides video into different、consecutively moving video objects and codes every object separately. Each object includes three kinds of information: the shape information、the texture information and movement information. The three kinds of information of every video object are needed to code. One of the core techniques in MPEG-4 is the part of shape coding. By reason of having adopted the shape coding technique, MPEG-4 has the larruping ability—the content-based interactivity.
The research on the video object segmentation and the shape coding is always isolated. Based on the progress of the research on the shape coding, the combination of the video segmentation and the shape coding is researched carefully. The main work includes:
1、The image segmentation technology based on Active contour models is researched. The parameter models and geometrical models is analysed and compared, and a geometrical models, level sets methods, is researched especially.
2、A moving object segmentation algorithm based on the level set methods is proposed. Take the luminance difference as the primary zero level set, and segment the object by narrow band algorithm.
3、A new shape coding based on the base-line is proposed. Since the shortness of the current algorithm, proposes a new algorithm which can adapt to any direction of the outline, as well as a new decode method.
4、A new system which is called shape coding system combined with the object segmentation is proposed. In MPEG-4, the middle process results in too more time cost and errors, so proposes a new system, which coding shape jusr after the object segmentation. The new system can save the time and the errors.
引文
[1] 钟玉琢,王琪,贺玉文.基于对象的多媒体数据压缩编码国际标准——MPEG-4 及其校验模型.北京:科学出版社,2000.
[2] 毕厚杰 主编.新一代视频压缩编码标准——H.264/AVC.北京:人民邮电出版社,2005.5
[3] Shi Hwa Lee,Dar-Sung Cho, et al.binary shape coding using baseline-based method. IEEE TRANSACYION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, 1(9),FEBRUARY 1999:44-58
[4] Paulo Nunes, Ferran Marques, Fernando, et al. A contour-based approach to binary shape coding using a multiple grid chain code. Signal Processing Image Communication 15(2000):585-599
[5] Jae-Won Chung, Jin-hak Lee,Joo-hee Moon, et al. A new vertex-based binary shape coder for high coding efficiency.Signal Processing Image Communication 15(2000):665-684
[6] Huitao Luo. Image-Dependent Shape Coding and Representation.IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, 3(15),MARCH,2005:345-354
[7] 刘党辉,沈兰荪.视频运动对象分割技术的研究.电路与系统学报,3(7),2002:78-85
[8] Meier Thomas, Ngan King N. Automatic Segmentation of Moving objects for video object plane generation. IEEE TRANSACTION ON CIRCUITS AND SYSTEMS FOR VIDEO TEVHNOLOGY, 1998, 8(5):525-538
[9] Nguyen H T, Worring M, Dev A. Detection of moving objects in video using a robust motion similarity measure. IEEE TRANSACTION OM IMAGE PROCESSIG, 2000, 9(1):137-141
[10] Bors Adrian G, Pitas Ioannis. Prediction and Tracking of Moving Objects in Image Sequence. IEEE TRANSACTION ON IMAGE PROCESSING, 2000, 9(8):1441-1445
[11] 李天庆,张毅,刘志等. Snake 模型综述. 计算机工程. VOL.31,NO.9,2005:1-3
[12] 朱辉,李在铭,蔡毅.基于运动窗生成的时空视频分割.电子学报,3(32),2004:480-484
[13] 张丽飞,王东峰,时永刚等. 基于形变模型的图像分割技术综述. 电子与信息学报,2003,3(25):395-403
[14] M.Kass, A.Witkin, and D.Terzopoulos. Snakes: Active contour models. International Journal ofComputer Vision, vol.1, 1988:321-331
[15] S.Osher and J.A.Setthian. Fronts Propagating with Curvature-dependent Speed: Algorithms based on Hamiton Jacobi Formulations. J. of Computational Physics, 1988(79):12-49
[16] R.Malladi, J.A.Sethian, and B.C.Vemuri. Shape Modeling with Front Propagation: A Level Set Approach. IEEE Transactions on PAMI, 1995(17):158-176
[17] D.Adalsteunsson and J.A.Sethian. A fast level set method for propagating interfaces. Jour.Comp.Phys,1995(118):269-277
[18] D.Adalsteunsson and J.A.Sethian. The Fast Construction of Extension Velocities in level set methods. Journal of Computational Physics. 1999(148):2-22
[19] J.A.Sethian. Evolution, Implementation, and Application of Level Set and Fast Marching Methods for advancing Fronts. Journal of Computational Physics 169. 2001:503-555
[20] Liyuan Li, Maylor K.H.Leung. Integrating intensity and texture differences for robust change detection. IEEE TRANSACTION ON IMAGE PROCESSING, 2002, 11(2):105-112.
[21] 杨莉, 杨新. 基于多运动方法的多运动目标分割. 上海交通大学学报. Vol.38 No.5, May 2004:714-717
[22] Octavian Soldea, Gershon, Ehud Rivlin. Global Segmentation and Curvature Analysis of Volumetric Data Sets Using Trivariate B-Spline Functions. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.28, NO.2, FEB 2006:265-278
[23] Dejun Wang, Jiali Zhao, et al. Level Set Methods, Distance Function and Image Segmentation. In: Proceedings of the 17th International Conference on Pattern Recognition,Volume 2, 23-26 Aug. 2004:110 - 115
[24] Jiangwen Deng, H.T. Tsui. A Fast Level Set Method for Segmentation of Low Contrast Noisy Biomedical Images. Pattern Recognition Letters 23,2002:161-169
[25] Aaron E.Lefohn, Joe M.Kniss, Charles D.Hansen, et al. A streaming Narrow-Band Algorithm: Interactive Computation and Visualization of Level Sets. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL.10, NO.4, JULY/AUGUST 2004:422-433
[26] Meihe Xu, Paul M.Thompson, Arthur W.Toga. An Adaptive Level Set Segmentation on A Triangulated Mesh. IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL.23, NO.2,FEBRUARY 2004:191-201
[27] Song Gao, Tien D.Bui. Image Segmentation and Selective Smoothing by Using Mumford-Shah Model. IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL.14, NO.10, OCTOBER 2005:1537-1549
[28] Marcelo Bertalmio, Guillemo Sapiro, Gregory Randall. Region Tracking and Level-Sets Methods. IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL.18, NO.5, MAY 1999:448-451
[29] Jiayong Yan, Tian-ge Zhuang, et al. Lymph Node Segmentation from CT Images Using Fast Marching Method. Computerized Medical Imaging and Graphics 28,2004:33-38
[30] Jiayong Yan, Tian-ge Zhuang. Applying Improved Fast Marching Method to Endocardial Boundary Detection in Echocardiographic Images. Pattern Recognition Letters 24, 2003:2777-2784
[31] J.A.Sethian and D.Adalsteinsson. An Overview of Level Set Methods for Etching, Deposition, and Lithography Development. IEEE TRANSACTIONS ON SEMICONDUTOR MANUFACTURING, VOL.10, NO.1, FEBRUARY 1997:167-184
[32] Yaakov Tsaig, Amir Averbuch. Automatic Segmentation of Moving Objects in Video Sequences: A Region Labeling Approach. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL.12,NO.7,JULY 2002:597-612
[33] Shijun Sun, David R.Haynor, Yongmin Kim. Semiautomatic Video Object Segmentation Using VSnakes. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL.13,NO.1,JANUARY 2003:75-82
[34] Jong Il Kim,Alen C.Bovik,Brian L.Evans. Generalized Predictive Binary Shape Coding Using Polygon Approximation.Signal 15、Processing: Image Communication 15(2000): 643-663
[35] Shinya Kadono,Choong Seng Boon, Minoru Etoh. Motion Compensation Method for Moving Pictures with Binary Shape.Signal Processing:Image Communication16(2000)295-306.
[36] Chung-Bin Wu, Chin-Yuan Yao, Bin-Da Liu, et al. Dct-Based Adaptive Thresholding Algorithm for Binary Motion Estimation.IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL,15,NO.5,MAY,2005:694 -703
[37] Kun-Bin Lee, Jih-Yiing Lin, Chein-Wei Jen. A Multisymbol Context-Based Arithmetic Coding Arichitecture for MPEG-4 Shape Coding.IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL,15,NO.2,FEBRUARY,2005:283-295
[38] Haohong Wang,Guido M.Schuster,K.Katsaggelos,et al.An Efficient Rate-Distorion Optimal Shape Coding Approach Utilizing a Skeleton-Based Decomposition.IEEE TRANSACTION ON IMAGE PROCESSING, VOL,12,NO.10,OCTOBER 2003:1181-1193
[39] Zhenzhong Chen,King Ngi Ngan. Joint Texture-Shape Optimization for MPEG-4 Multiple Video Objects.IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL,15,NO.9,SEPTEMBER 2005:1170-1174
[40] Noel Brady, Frank Bossen. Shape Compression of Moving objects.Signal Processing:Image Communication 15(2000):601-617
[41] Noel Brady, Frank Bossen. Shape compression of moving objects using context-based arithmetic encoding. Signal Processing: Image Communication 15(2000):601-617
[42] 卢官明. 一种用于 MPEG-4 形状编码的快速运动估计算法. 电子与信息学报. VOL.25,NO.9, 2003:1225-1229
[43] Corinne Le Buhan Jordan, Touradj Ebrahimi, and Murat Kunt. Progressive Content-based Shape Compression for Retrieval of Binary Images. COMPUTER VISION AND IMAGE UNDERSTANDING. VOL.71,NO.2, 1998:198-212
[44] Fabian W.Meier, Guido M.Schuster, and Aggelos K.Katsaggelos. A mathematical model for shape coding with B-splines. Signal Processing: Image Communication 2000:685-701
[45] 贾振堂,韩艳芳,贺贵明. 一种二值图像下的收缩型活动轮廓及其应用. 小型微型计算机系统. VOL.25, NO.3,2004:463-466
[46] 杨勇,黄波,王桥等. 一种基于时空信息的 VOP 分割算法. 电路与系统学报. VOL.6,NO.2,2001:50-54
[47] Yu-Pao Tsai, Chih-Chuan Lai, Yi-Ping Hung, etal. A Bayesuan Approach to Video Object Segmentation via Merging 3-D Watershed Volumes. IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL,15,NO.1,JANUARY 2005:175-180
[48] Changick Kim and Jenq-Neng Hwang. Fast and Automatic Video Object Segmentation and Tracking for Content-Based Applications. IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL,12,NO.2,FEB 2002:122-129
[49] Abdol-Reza Mansouri, Amar Mitiche, Carlos Vazquez. Multiregion competition: A level set extension of region competition to multiple region image partitioning. Computer Vision and ImageUnderstanding. 2006:137-150
[2] 毕厚杰 主编.新一代视频压缩编码标准——H.264/AVC.北京:人民邮电出版社,2005.5
[3] Shi Hwa Lee,Dar-Sung Cho, et al.binary shape coding using baseline-based method. IEEE TRANSACYION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, 1(9),FEBRUARY 1999:44-58
[4] Paulo Nunes, Ferran Marques, Fernando, et al. A contour-based approach to binary shape coding using a multiple grid chain code. Signal Processing Image Communication 15(2000):585-599
[5] Jae-Won Chung, Jin-hak Lee,Joo-hee Moon, et al. A new vertex-based binary shape coder for high coding efficiency.Signal Processing Image Communication 15(2000):665-684
[6] Huitao Luo. Image-Dependent Shape Coding and Representation.IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, 3(15),MARCH,2005:345-354
[7] 刘党辉,沈兰荪.视频运动对象分割技术的研究.电路与系统学报,3(7),2002:78-85
[8] Meier Thomas, Ngan King N. Automatic Segmentation of Moving objects for video object plane generation. IEEE TRANSACTION ON CIRCUITS AND SYSTEMS FOR VIDEO TEVHNOLOGY, 1998, 8(5):525-538
[9] Nguyen H T, Worring M, Dev A. Detection of moving objects in video using a robust motion similarity measure. IEEE TRANSACTION OM IMAGE PROCESSIG, 2000, 9(1):137-141
[10] Bors Adrian G, Pitas Ioannis. Prediction and Tracking of Moving Objects in Image Sequence. IEEE TRANSACTION ON IMAGE PROCESSING, 2000, 9(8):1441-1445
[11] 李天庆,张毅,刘志等. Snake 模型综述. 计算机工程. VOL.31,NO.9,2005:1-3
[12] 朱辉,李在铭,蔡毅.基于运动窗生成的时空视频分割.电子学报,3(32),2004:480-484
[13] 张丽飞,王东峰,时永刚等. 基于形变模型的图像分割技术综述. 电子与信息学报,2003,3(25):395-403
[14] M.Kass, A.Witkin, and D.Terzopoulos. Snakes: Active contour models. International Journal ofComputer Vision, vol.1, 1988:321-331
[15] S.Osher and J.A.Setthian. Fronts Propagating with Curvature-dependent Speed: Algorithms based on Hamiton Jacobi Formulations. J. of Computational Physics, 1988(79):12-49
[16] R.Malladi, J.A.Sethian, and B.C.Vemuri. Shape Modeling with Front Propagation: A Level Set Approach. IEEE Transactions on PAMI, 1995(17):158-176
[17] D.Adalsteunsson and J.A.Sethian. A fast level set method for propagating interfaces. Jour.Comp.Phys,1995(118):269-277
[18] D.Adalsteunsson and J.A.Sethian. The Fast Construction of Extension Velocities in level set methods. Journal of Computational Physics. 1999(148):2-22
[19] J.A.Sethian. Evolution, Implementation, and Application of Level Set and Fast Marching Methods for advancing Fronts. Journal of Computational Physics 169. 2001:503-555
[20] Liyuan Li, Maylor K.H.Leung. Integrating intensity and texture differences for robust change detection. IEEE TRANSACTION ON IMAGE PROCESSING, 2002, 11(2):105-112.
[21] 杨莉, 杨新. 基于多运动方法的多运动目标分割. 上海交通大学学报. Vol.38 No.5, May 2004:714-717
[22] Octavian Soldea, Gershon, Ehud Rivlin. Global Segmentation and Curvature Analysis of Volumetric Data Sets Using Trivariate B-Spline Functions. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.28, NO.2, FEB 2006:265-278
[23] Dejun Wang, Jiali Zhao, et al. Level Set Methods, Distance Function and Image Segmentation. In: Proceedings of the 17th International Conference on Pattern Recognition,Volume 2, 23-26 Aug. 2004:110 - 115
[24] Jiangwen Deng, H.T. Tsui. A Fast Level Set Method for Segmentation of Low Contrast Noisy Biomedical Images. Pattern Recognition Letters 23,2002:161-169
[25] Aaron E.Lefohn, Joe M.Kniss, Charles D.Hansen, et al. A streaming Narrow-Band Algorithm: Interactive Computation and Visualization of Level Sets. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL.10, NO.4, JULY/AUGUST 2004:422-433
[26] Meihe Xu, Paul M.Thompson, Arthur W.Toga. An Adaptive Level Set Segmentation on A Triangulated Mesh. IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL.23, NO.2,FEBRUARY 2004:191-201
[27] Song Gao, Tien D.Bui. Image Segmentation and Selective Smoothing by Using Mumford-Shah Model. IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL.14, NO.10, OCTOBER 2005:1537-1549
[28] Marcelo Bertalmio, Guillemo Sapiro, Gregory Randall. Region Tracking and Level-Sets Methods. IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL.18, NO.5, MAY 1999:448-451
[29] Jiayong Yan, Tian-ge Zhuang, et al. Lymph Node Segmentation from CT Images Using Fast Marching Method. Computerized Medical Imaging and Graphics 28,2004:33-38
[30] Jiayong Yan, Tian-ge Zhuang. Applying Improved Fast Marching Method to Endocardial Boundary Detection in Echocardiographic Images. Pattern Recognition Letters 24, 2003:2777-2784
[31] J.A.Sethian and D.Adalsteinsson. An Overview of Level Set Methods for Etching, Deposition, and Lithography Development. IEEE TRANSACTIONS ON SEMICONDUTOR MANUFACTURING, VOL.10, NO.1, FEBRUARY 1997:167-184
[32] Yaakov Tsaig, Amir Averbuch. Automatic Segmentation of Moving Objects in Video Sequences: A Region Labeling Approach. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL.12,NO.7,JULY 2002:597-612
[33] Shijun Sun, David R.Haynor, Yongmin Kim. Semiautomatic Video Object Segmentation Using VSnakes. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL.13,NO.1,JANUARY 2003:75-82
[34] Jong Il Kim,Alen C.Bovik,Brian L.Evans. Generalized Predictive Binary Shape Coding Using Polygon Approximation.Signal 15、Processing: Image Communication 15(2000): 643-663
[35] Shinya Kadono,Choong Seng Boon, Minoru Etoh. Motion Compensation Method for Moving Pictures with Binary Shape.Signal Processing:Image Communication16(2000)295-306.
[36] Chung-Bin Wu, Chin-Yuan Yao, Bin-Da Liu, et al. Dct-Based Adaptive Thresholding Algorithm for Binary Motion Estimation.IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL,15,NO.5,MAY,2005:694 -703
[37] Kun-Bin Lee, Jih-Yiing Lin, Chein-Wei Jen. A Multisymbol Context-Based Arithmetic Coding Arichitecture for MPEG-4 Shape Coding.IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL,15,NO.2,FEBRUARY,2005:283-295
[38] Haohong Wang,Guido M.Schuster,K.Katsaggelos,et al.An Efficient Rate-Distorion Optimal Shape Coding Approach Utilizing a Skeleton-Based Decomposition.IEEE TRANSACTION ON IMAGE PROCESSING, VOL,12,NO.10,OCTOBER 2003:1181-1193
[39] Zhenzhong Chen,King Ngi Ngan. Joint Texture-Shape Optimization for MPEG-4 Multiple Video Objects.IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL,15,NO.9,SEPTEMBER 2005:1170-1174
[40] Noel Brady, Frank Bossen. Shape Compression of Moving objects.Signal Processing:Image Communication 15(2000):601-617
[41] Noel Brady, Frank Bossen. Shape compression of moving objects using context-based arithmetic encoding. Signal Processing: Image Communication 15(2000):601-617
[42] 卢官明. 一种用于 MPEG-4 形状编码的快速运动估计算法. 电子与信息学报. VOL.25,NO.9, 2003:1225-1229
[43] Corinne Le Buhan Jordan, Touradj Ebrahimi, and Murat Kunt. Progressive Content-based Shape Compression for Retrieval of Binary Images. COMPUTER VISION AND IMAGE UNDERSTANDING. VOL.71,NO.2, 1998:198-212
[44] Fabian W.Meier, Guido M.Schuster, and Aggelos K.Katsaggelos. A mathematical model for shape coding with B-splines. Signal Processing: Image Communication 2000:685-701
[45] 贾振堂,韩艳芳,贺贵明. 一种二值图像下的收缩型活动轮廓及其应用. 小型微型计算机系统. VOL.25, NO.3,2004:463-466
[46] 杨勇,黄波,王桥等. 一种基于时空信息的 VOP 分割算法. 电路与系统学报. VOL.6,NO.2,2001:50-54
[47] Yu-Pao Tsai, Chih-Chuan Lai, Yi-Ping Hung, etal. A Bayesuan Approach to Video Object Segmentation via Merging 3-D Watershed Volumes. IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL,15,NO.1,JANUARY 2005:175-180
[48] Changick Kim and Jenq-Neng Hwang. Fast and Automatic Video Object Segmentation and Tracking for Content-Based Applications. IEEE TRANSACTION ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL,12,NO.2,FEB 2002:122-129
[49] Abdol-Reza Mansouri, Amar Mitiche, Carlos Vazquez. Multiregion competition: A level set extension of region competition to multiple region image partitioning. Computer Vision and ImageUnderstanding. 2006:137-150