视频对象分割算法研究
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摘要
视频对象分割是MPEG-4基于内容的编码系统中的关键技术之一，此外，它在基于内容的检索、对象识别、对象跟踪、视频电话、视频购物、视频监控、电视特技制作和交互式操作的多媒体中也有重要的应用。不仅如此，许多机器视觉问题都要借助视频对象分割技术才能完成。因此，视频对象分割技术具有重要的研究意义和应用价值，它是当前多媒体技术研究中的热点和难点之一。
本论文正是针对上述问题，对视频对象分割算法问题作了创新性和探索性研究。主要内容为：
1．首先分析和研究了视频对象分割所涉及的主要理论和方法。主要分析了模糊聚类理论和模糊C均值聚类方法，概率统计学中的贝叶斯(Bayes)分类器、最大后验概率模型和马尔可夫(Markov)随机场模型，形态学的膨胀、腐蚀以及开闭运算方面的理论和方法，神经网络方面的神经元模型、神经网络分类器和BP训练算法，全局运动估计中的六参数仿射运动模型、Gauss-Newton迭代法和Levenberg-Marquardet优化方法。
2．在视频对象分割中，为了得到准确的视频对象轮廓，常用视频对象的边缘对所得到的对象轮廓进行校正。因此图像边缘提取对于视频对象分割非常重要。初步分析了克隆技术，建议了一种克隆算法，将它用于彩色图像边缘检测，建立了一种边缘检测模型，并联合使用边缘连接算法给出一种彩色图像边缘提取的新方法。在图像边缘提取中，阈值的选取是关键技术之一，分析了人类视觉系统特性，建立了视觉系统的数学模型，给出一种边缘检测中基于人类视觉特性的自动阈值选取方法。论文提出的边缘检测方法同其他算法相比，能得到更连续的边缘和更少的过检测点。
3．分析和研究了基于时间信息或空间信息的自动视频对象分割算法的特点和优缺点，针对这两种类型的算法都不能充分地利用视频序列时间和空间信息的问题，提出一种有效的时空联合分割算法。首先输入一组视频序列帧，选择第一帧作为参考帧，求出其它各帧同参考帧的帧差，将得到的一组帧差求最大值获得初始帧差图像，利用模糊C均值聚类算法和遗传优化方法对帧差图像进行聚类分析，得到初始帧差模板。利用分层的块匹配算法进行运动矢量估计，由运动矢量获得初始运动模板，然后对帧差和运动模板求或，得到粗糙的视频对象模板。由于模板中存在许多空洞和噪声点，对模板进行填充和去噪。基于模板和运动窗技术获得对象的运动窗口，在运动窗内利用边缘提取算法获得视频对象的边缘，基于对象边缘对模板进行轮廓校正。用原始视频数据对校正后的模板赋值，得到初始视频对象。最后通过运动估计和背景注册信息完成视频对象的跟踪。仿真结果同其它相关算法的结果进行了比较，证明算法可以获得满意的分割和跟踪效果。
4．研究了帧差分割算法存在的问题和背景差分割算法的优点。提出一种基于背景构建的自动视频对象分割算法。首先对多帧视频片段作统计性分析，基于帧差和运动信息提取多组关键帧，基于这些关键帧获得各组的关键背景，利用这些关键背景和背景预测方法得到视频序列的全景图像。输入视频序列帧，求出各帧同全景图像的背景差图像，利用BP神经网络分类器对背景差图像进行分类，通过分类得到各帧的对象模板，利用模板填充、局部噪声去除和局部平滑算法对模板进行后续处理，最后利用各帧的原始图像数据对模板赋值，提取出各帧视频对象。对于具有动态背景的视频序列，利用全局运动估计和补偿技术对摄像机的运动进行补偿，对补偿后的视频序列基于求多帧中值的方法得到每帧的背景。利用背景差算法进行视频对象的分割和跟踪。仿真结果和其它相关算法的分割结果进行了主客观两方面的比较，验证了本章算法的有效性。
5．研究和分析了一些典型的半自动视频对象分割方法，这些算法中大部分需要过多的人工参与，大大降低了算法的速度和可操作性，不利于算法的有效执行。针对这种情况，提出一种联合背景构建和运动估计技术的半自动视频对象分割算法。首先在GUI中通过鼠标描绘出视频对象的大致边界，基于对象的边缘信息对轮廓进行校正，然后利用轮廓填充和平滑算法得到初始帧对象模板。基于模板得到初始帧视频对象和初始背景。利用背景差算法得到后续帧对象模板，利用运动估计对模板进行校正，根据相邻帧的对象模板对背景更新，最后通过背景的不断更新和背景差算法完成后续帧对象跟踪。仿真结果和其它相关算法的分割结果进行了主客观两方面的比较，验证了本章算法的有效性。
Video object segmentation is one of the most important techniques for MPEG-4 content-based coding scheme, which has many important applications in content-base index, object recognition, object tracking, video phone, video conference and interactive multimedia. Many problems of computer vision are solved by using the techniques of video object segmentation. So the study on video object segmentation techniques has important significance and application value, which is one of the most difficult problems in the multimedia techniques.
In this thesis, the novel methods are explored and investigated for video object segmentation. The main results are as follows:
1.The related theories and methods of video object segmentation have been analyzed. Firstly, the theory of mode clustering and the fuzzy c-means clustering method are studied, the Bayes classifier, the maximum a posteriori probability model and Markov random field are discussed, secondly, the morphological dilation, erosion and open-close operations are introduced, the nerve cell model, neural network classifier and BP training algorithm are analyzed, finally, the six-parameter affine motion model of global motion estimation, the Gauss-Newton iteration method and the Levenberg-Marquardet optimization method are studied.
2.The edges of objects are used to correct the contours in video object segmentation, in order to obtain the precise contours of video objects, so edge extracton is very important in video object segmentation. We analyze clone technique, and suggest a clone algorithm, then apply the algorithm to edge extraction of color images, finally, we propose an effective edge extraction algorithm by integrating edge connection method. Automatic threshold choice is difficult and important to color image edge detection. After human visual properties have been analyzed, an algorithm of automatic threshold choice in image edge detection is proposed. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods
3.The video object segmentation based on temporal or spatial information is researched, then an effective video object segmentation algorithm is suggested. Firstly, a group of pictures are inputted, the initial frame difference image is obtained by employing the frame difference information, and the initial frame difference mask is obtained by integrating fuzzy c-means clustering and genetic algorithm. The initial motion mask is obtained by using motion estimation. Secondly, the coarse initial object mask is obtained by employing the initial frame difference and motion mask. The mask is filled and corrected by using the edges of objects in the motion window. An initial video object is obtained by using the mask and the original image data. Finally the object tracking is executed by using motion estimation and background information. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods
4.After the frame difference method and the background difference one have been researched, an algorithm of automatic video object segmentation based on background constructing is suggested. A group of key background images are obtained by employing many extracted key frames based on motion information, and the background of video sequence is obtained by employing these key background images. After the background difference images have been obtained, the object masks are obtained by using BP neural network classifier. The masks are improved by using the post-processing procedures, such as mask filling, noise elimination and contour smoothing. The video objects are extracted by using the processed masks. For the video sequence with motion background, the global motion estimation and compensation is used, the background is constructed by the median of a group of compensated video frames. Finally, the object segmentation and tracking is executed by employing the background difference method. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods.
5.A lot of semiautomatic video object segmentation methods are analyzed, an algorithm of semiautomatic video object segmentation by integrating background constructing and motion estimation is suggested. Firstly, a coarse contour of the object is obtained by employing manual method; the contour is corrected by employing edges of the object. Secondly, the corrected contour is filled and processed by employing smoothing method, and the initial object and the background are obtained by using the processed contour. The object masks of subsequence frames are obtained by employing background difference algorithm, and the masks are corrected by using motion estimation. Finally, the object tracking is executed by employing background updating and background difference method. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods.
本论文正是针对上述问题，对视频对象分割算法问题作了创新性和探索性研究。主要内容为：
1．首先分析和研究了视频对象分割所涉及的主要理论和方法。主要分析了模糊聚类理论和模糊C均值聚类方法，概率统计学中的贝叶斯(Bayes)分类器、最大后验概率模型和马尔可夫(Markov)随机场模型，形态学的膨胀、腐蚀以及开闭运算方面的理论和方法，神经网络方面的神经元模型、神经网络分类器和BP训练算法，全局运动估计中的六参数仿射运动模型、Gauss-Newton迭代法和Levenberg-Marquardet优化方法。
2．在视频对象分割中，为了得到准确的视频对象轮廓，常用视频对象的边缘对所得到的对象轮廓进行校正。因此图像边缘提取对于视频对象分割非常重要。初步分析了克隆技术，建议了一种克隆算法，将它用于彩色图像边缘检测，建立了一种边缘检测模型，并联合使用边缘连接算法给出一种彩色图像边缘提取的新方法。在图像边缘提取中，阈值的选取是关键技术之一，分析了人类视觉系统特性，建立了视觉系统的数学模型，给出一种边缘检测中基于人类视觉特性的自动阈值选取方法。论文提出的边缘检测方法同其他算法相比，能得到更连续的边缘和更少的过检测点。
3．分析和研究了基于时间信息或空间信息的自动视频对象分割算法的特点和优缺点，针对这两种类型的算法都不能充分地利用视频序列时间和空间信息的问题，提出一种有效的时空联合分割算法。首先输入一组视频序列帧，选择第一帧作为参考帧，求出其它各帧同参考帧的帧差，将得到的一组帧差求最大值获得初始帧差图像，利用模糊C均值聚类算法和遗传优化方法对帧差图像进行聚类分析，得到初始帧差模板。利用分层的块匹配算法进行运动矢量估计，由运动矢量获得初始运动模板，然后对帧差和运动模板求或，得到粗糙的视频对象模板。由于模板中存在许多空洞和噪声点，对模板进行填充和去噪。基于模板和运动窗技术获得对象的运动窗口，在运动窗内利用边缘提取算法获得视频对象的边缘，基于对象边缘对模板进行轮廓校正。用原始视频数据对校正后的模板赋值，得到初始视频对象。最后通过运动估计和背景注册信息完成视频对象的跟踪。仿真结果同其它相关算法的结果进行了比较，证明算法可以获得满意的分割和跟踪效果。
4．研究了帧差分割算法存在的问题和背景差分割算法的优点。提出一种基于背景构建的自动视频对象分割算法。首先对多帧视频片段作统计性分析，基于帧差和运动信息提取多组关键帧，基于这些关键帧获得各组的关键背景，利用这些关键背景和背景预测方法得到视频序列的全景图像。输入视频序列帧，求出各帧同全景图像的背景差图像，利用BP神经网络分类器对背景差图像进行分类，通过分类得到各帧的对象模板，利用模板填充、局部噪声去除和局部平滑算法对模板进行后续处理，最后利用各帧的原始图像数据对模板赋值，提取出各帧视频对象。对于具有动态背景的视频序列，利用全局运动估计和补偿技术对摄像机的运动进行补偿，对补偿后的视频序列基于求多帧中值的方法得到每帧的背景。利用背景差算法进行视频对象的分割和跟踪。仿真结果和其它相关算法的分割结果进行了主客观两方面的比较，验证了本章算法的有效性。
5．研究和分析了一些典型的半自动视频对象分割方法，这些算法中大部分需要过多的人工参与，大大降低了算法的速度和可操作性，不利于算法的有效执行。针对这种情况，提出一种联合背景构建和运动估计技术的半自动视频对象分割算法。首先在GUI中通过鼠标描绘出视频对象的大致边界，基于对象的边缘信息对轮廓进行校正，然后利用轮廓填充和平滑算法得到初始帧对象模板。基于模板得到初始帧视频对象和初始背景。利用背景差算法得到后续帧对象模板，利用运动估计对模板进行校正，根据相邻帧的对象模板对背景更新，最后通过背景的不断更新和背景差算法完成后续帧对象跟踪。仿真结果和其它相关算法的分割结果进行了主客观两方面的比较，验证了本章算法的有效性。
Video object segmentation is one of the most important techniques for MPEG-4 content-based coding scheme, which has many important applications in content-base index, object recognition, object tracking, video phone, video conference and interactive multimedia. Many problems of computer vision are solved by using the techniques of video object segmentation. So the study on video object segmentation techniques has important significance and application value, which is one of the most difficult problems in the multimedia techniques.
In this thesis, the novel methods are explored and investigated for video object segmentation. The main results are as follows:
1.The related theories and methods of video object segmentation have been analyzed. Firstly, the theory of mode clustering and the fuzzy c-means clustering method are studied, the Bayes classifier, the maximum a posteriori probability model and Markov random field are discussed, secondly, the morphological dilation, erosion and open-close operations are introduced, the nerve cell model, neural network classifier and BP training algorithm are analyzed, finally, the six-parameter affine motion model of global motion estimation, the Gauss-Newton iteration method and the Levenberg-Marquardet optimization method are studied.
2.The edges of objects are used to correct the contours in video object segmentation, in order to obtain the precise contours of video objects, so edge extracton is very important in video object segmentation. We analyze clone technique, and suggest a clone algorithm, then apply the algorithm to edge extraction of color images, finally, we propose an effective edge extraction algorithm by integrating edge connection method. Automatic threshold choice is difficult and important to color image edge detection. After human visual properties have been analyzed, an algorithm of automatic threshold choice in image edge detection is proposed. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods
3.The video object segmentation based on temporal or spatial information is researched, then an effective video object segmentation algorithm is suggested. Firstly, a group of pictures are inputted, the initial frame difference image is obtained by employing the frame difference information, and the initial frame difference mask is obtained by integrating fuzzy c-means clustering and genetic algorithm. The initial motion mask is obtained by using motion estimation. Secondly, the coarse initial object mask is obtained by employing the initial frame difference and motion mask. The mask is filled and corrected by using the edges of objects in the motion window. An initial video object is obtained by using the mask and the original image data. Finally the object tracking is executed by using motion estimation and background information. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods
4.After the frame difference method and the background difference one have been researched, an algorithm of automatic video object segmentation based on background constructing is suggested. A group of key background images are obtained by employing many extracted key frames based on motion information, and the background of video sequence is obtained by employing these key background images. After the background difference images have been obtained, the object masks are obtained by using BP neural network classifier. The masks are improved by using the post-processing procedures, such as mask filling, noise elimination and contour smoothing. The video objects are extracted by using the processed masks. For the video sequence with motion background, the global motion estimation and compensation is used, the background is constructed by the median of a group of compensated video frames. Finally, the object segmentation and tracking is executed by employing the background difference method. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods.
5.A lot of semiautomatic video object segmentation methods are analyzed, an algorithm of semiautomatic video object segmentation by integrating background constructing and motion estimation is suggested. Firstly, a coarse contour of the object is obtained by employing manual method; the contour is corrected by employing edges of the object. Secondly, the corrected contour is filled and processed by employing smoothing method, and the initial object and the background are obtained by using the processed contour. The object masks of subsequence frames are obtained by employing background difference algorithm, and the masks are corrected by using motion estimation. Finally, the object tracking is executed by employing background updating and background difference method. Experiments demonstrate the effectiveness of our algorithm in comparison with other related methods.
引文
[1]Thomas S. The MPEG-4 video standard verification model. IEEE On Circuits and Systems for Video Technology, 1997,7(1): 19-31
[2]Lee S W, Choi J G, Kim S D. Automatic segmentation of moving objects for video object plane generation. IEEE On Circuits and Systems for Video Technology, 1997,7:279-286
[3]Neri, Colonnese S, Russo G, etal. Automatic moving object and background separation. Signal Processing, 1998,66:219-232
[4]Mech R, Wollborn M. A noise robust method for segmentation of moving objects in video sequences. IEEE 1997 International Conference on Acoustics, Speech and Signal Processing(ICAS SP' 97), 1997,Vol.4:2657-2660
[5]Ju G, Jongwon K. Fast and robust moving object segmentation technique for MPEG-4 object-based coding and functionality. The International Society for Optical Engineering, 1998,3561:106-117
[6]Shao-Yi C, Yu-Wen H, Bing-Yu H. Fast video segmentation algorithm with shadow cancellation, global motion compensation, and adaptive threshold techniques. IEEE Transactions on Multimedia,2004,6(5):732-748
[7]Shao-Yi C, Shyh-Yih M, Liang-Gee C. Efficient moving object segmentation algorithm using background registration technique. IEEE On Circuits and Systems for Video Technology, 2002,12(7):577-586
[8]Shao-Yi C, Shyh-Yih M, Liang-Gee C. A hybrid morphology processing units architecture for real-time video segmentation systems. 2001 IEEE International Symposium on Circuits and Systems,Vol.5:275-278
[9]Thomas M, King N. N. Automatic segmentation of moving objects for video object plane generation. IEEE On Circuits and Systems for Video Technology, 1998,8(5):525-538
[10]Thomas M, King N. N. Video segmentation for content-based coding. IEEE On Circuits and Systems for Video Technology, 1999,9(8): 1190-1203
[11]Munchurl K, Jinwoong K. Moving video object segmentation using statistical hypothesis testing. Electronics Letters, 2000,36(2): 128-129
[12]赵明，李娜，陈纯．采用统计推断的自动视频对象分割．计算机辅助设计与图形学学 报，2003，15(3)：318-333
[13]Jinhui Pan, Chia-Wen Lin, Chuang Gu. A robust video object segmentation scheme with prestored background information. 2002 IEEE International Symposium on Circuits and Systems(ISCAS'02), Vol.3:803-806
[14]Cavallaro A, Touradj E. Accurate video object segmentation through change detection. 2002 International Conference on Multimedia and Expo,Vol. 1:445-448
[15]Dirk F. Automatic video-object segmentation employing multi-sprites with constrained delay. 2006 International Conference on Consumer Electronics,Vol. 1:479-480
[16]Alice C, Laurent B, Jean-Marc C. Robust fast extraction of video objects combining frame differences and adaptive reference image. 2001 International Conference on Image Processing, Vol.2:785-788
[17]Dirk F, Wolfgang E. Video-object segmentation using multi-sprite background subtraction. 2004 International Conference on Multimedia and Expo,Vol. 1:343-346
[18]李晓春，孙培懋，金国藩等．复杂场景中动目标图象分割的一种快速算法．清华大学学报，1997，37(8)：19-21
[19]吴思，张勇东，林守勋等．动态场景视频序列中的前景区域自动提取．计算机辅助设计与图形学学报，2005，17(2)：359—363
[20]Anastacios D, Nikolaos D, Stefanos K A neural network based scheme for unsupervised video object segmentation. 1998 International Conference on Image Processing, Vol.2:632-636
[21]Stephanie J B,Michel B, Gilles A. Region-based active contours for video object segmentation with camera compensation. 2001 International Conference on Image Processing,Vol.2:61-64
[22]Jianping F, David K Y Y. Automatic Image Segmentation by Integrating Color-Edge Extraction and Seeded Region Growing. IEEE Transaction on Image Processing, 2001,10(10): 1454-1466
[23]Tanchroen D, Jitapunkul S, Chompun S. Spatial segmentation based on modified morphological tools. 2001 International Conference on Information Technology Coding and Computing,Vol. 1:478-482
[24]Stephanie J B,Michel B, Gilles A. Video object segmentation using eulerian region-based active contours. 2001 Eighth IEEE International Conference on Computer Vision(ICCV 2001), Vol. 1:353-360
[25]Muriel G, Michel B. Combining shape prior and statistical features for active contour segmentation. IEEE Tranctions on Circuits and Systems for Video Technology,2004, 14(5):726-734
[26] Precioso E, Barland M. B-Spline active contours for fast video segmentation. 2001 International Conference on Image Processing,Vol.2:777-780
[27] Jae G C, SiWoong L, SeongDae K. Spatio-Temporal video segmentation using a joint similarity measure. IEEE Transactions on Circuits and Systems for Video Technology, 1997, 7(2):279-286
[28] Demin W. Unsupervised video segmentation based on watersheds and temporal tracking. IEEE Transactions on Circuits and Systems for Video Technology, 1998, 8(5):539-546
[29] Yaakov T, Amir A. Automatic segmentation of moving objects in video sequences: a region labeling approach. IEEE Transactions on Circuits and Systems for Video Technology,2002, 12(7):597-612
[30] TsungHan T, ChungYuan L. Video segmentation using multiscale feature extraction and fusion. 2005 IEEE International Symposium on Circuits and Systems,Vol.4:3223-3226
[31] Zhou J Y, Ong E P, Ko C C. Video object segmentation and tracking for content-based video coding. 2000 IEEE International Conference on Multimedia and Expo,Vol.3:1555-1558
[32] Kun Z, Qionghai D, Jiang W. Fast tracking of semantic video object based on motion prediction and subregion extraction. 2002 International Conference on Image Processing,Vol.3:621 -624
[33] Chew K T, Mohammed G. Using non-linear diffusion and motion information for video segmentation. 2002 International Conference on Image Processing,Vol.2:769-772
[34] Hamid M R, Baloch A, Bilal A, et al. Object segmentation using feature based conditional morphology. 2003 International Conference on Image Analysis and Processing,Vol. 1:548-553
[35] Thomas M, King N. Video object plane segmentation using a morphological motion filter and hausdorff object tracking. 1998 International Conference on Image Processing,Vol.2:652-656
[36] Xingrong Z, Xianwu H. Video object segmentation based on HOS and multi-resolution watershed. 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, Vol. 1:274-277
[37] ShaoYi C, YuWen H. Predictive watershed: a fast watershed algorithm for video segmentation. IEEE Transactions on Circuits and Systems for Video Technology,2003, 13(5):453-461
[38] Gerald K, Joachim W. A tensor-driven active contour model for moving object segmentation. 2001 International Conference on Image Processing, Vol.2:81-84
[39] Muriel G, Michel B. Video segmentation using active contours on a group of pictures. 2002 International Conference on Image Processing, Vol.2:81-84
[40] Andrea C, Touradj E. Accurate video object segmentation through change detection. 2002 IEEE International Conference on Multimedia and Expo, Vol.1:445-448
[41] Munchurl K, Jae G C. A vop generation tool: automatic segmentation of moving objects in image sequences based on spatio-temporal information. IEEE Transactions on Circuits and Systems for Video Technology, 1999, 9(8): 1216-1226
[42] Ong E P, Tye B J. An efficient video object segmentation scheme. 2002 IEEE International Conference on Acoustics, Speech and Signal Processing, Vol.4:3361-3364
[43] KaiKuang M, HaiYun W. Region-based nonparametric optical flow segmentation with pre-clustering and post-clustering. 2002 IEEE International Conference on Multimedia and Expo,Vol.2:201-204
[44] Huang B, Yong Y. Video segmentation based-on fuzzy clustering and multi-features. 2002 6th International Conference on Signal Processing, Vol.2:977-980
[45] Wei W, King N N. Multiple feature clustering algorithm for automatic video object segmentation. 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.3:625-628
[46] Anastasios D, Nikolaos D. An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture. IEEE Transactions on Neural Networks, 2003, 14(3):616-630
[47] Ioannis P, Hendriks E A. Video segmentation by MAP labeling of watershed segments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001,23(3):326-332
[48] ChionTing MingShen H. Segmentation of non-rigid object in a non-parametric MAP framework. 2003 International Conference on Image Processing, Vol. 1:997-1000
[49] Vasileios M, Ioannis K. Video object segmentation using bayes-based temporal tracking and trajectory-based region merging. IEEE Transactions on Circuits and Systems for Video Technology,2004, 14(6):782-795
[50] Sohaib K, Mubarak S. Object based segmentation of video using color, motion and spatial information. 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.2:746-751
[51] Yang W, KiaFock L. Spatiotemporal video segmentation based on graphical models. IEEE Transactions on Image Processing, 2005, 14(7):937-947
[52] Jian Z, Xiaoping Z. Video object segmentation and tracking using probabilistic fuzzy c-means. 2005 IEEE Workshop on Machine Learning for Signal Processing, Vol. 1:201-206
[53]Joannis K, Michael G S. Spatiotemporal segmentation and tracking of objects for visualization of videoconference image sequences. IEEE Transactions on Circuits and Systems for Video Technology,2000, 10(8): 1388-1402
[54]Wei W, King N N. Integration of motion and image features for automatic video object segmentation. 2004 International Conference on Image Processing, Vol. 1:361-364
[55]Scbastien V, Isabelle M. Active mesh for video segmentation and objects tracking. 2001 International Conference on Image Processing, Vol.2:77-80
[56]Alfied Y, Wael B. On Reducing the size of structured meshes with a novel video object extraction algorithm. 2003 IEEE Canadian Conference on Electrical and Computer Engineering,Vol. 2:1179-1182
[57]Falah E A, Baozong Y. Moving object segmentation from video sequences: an edge approach. 2003 4th EURASIP Conference focused on Video/Image Processing and Multimedia Communications, Vol. 1:193-199
[58]Changick K, JenqNeng H. A fast and robust moving object segmentation in video sequences. 1999 International Conference on Image Processing, Vol.2:131-134
[59]Yah L, Wen G, et al. Automatic video segmentation using a novel background model.2002 IEEE International Symposium on Circuits and Systems, Vol.3:807-810
[60]吴思，林守勋，张勇东．基于动态背景构建的视频运动对象自动分割．计算机学报，2005，28(8)：1386-1392
[61]Rong S, Xiaofeng L. Efficient spatiotemporal segmentation and video object generation for highway surveillance video. 2002 International Conference on Communication, Circuits and Systems and West Sino Exposition, Vol. 1:580-584
[62]Lijie L, Guoliang F. Combined key-frame extraction and object-based video segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2005, 15(7):869-884
[63]Changick K, JenqNeng H. Object-based video abstraction for video surveillance systems. IEEE Transactions on Circuits and Systems for Video Technology,2002, 12(12): 1128-1138
[64]Ibrahim K, JenqNeng H. A framework for fully automatic moving video-object segmentation based on graph partitioning. 2004 International Symposium on Circuits and Systems, Vol.3:845-848
[65]Vigus S A, Bull D R, et al. Video object tracking using region split and merge and a kalman filter tracking algorithm. Proc. 2001 International Conference on Image Processing,Vol. 1:650-653
[66]Hongqiang B, Zhaoyang Z. Object-based video segmentation using spatio-temporal energy. 2004 7th International Conference on Signal Processing, Vol.2:1260-1263
[67]YuPao T, ChihChuan L. A Bayesian approach to video object segmentation via merging 3-D watershed volumes. IEEE Transactions on Circuits and Systems for Video Technology,2005, 15(1):175-180
[68]Stephan H, Hubert M. A video segmentation algorithm for hierarchal object Representations and Its Implementation. IEEE Transactions on Circuits and Systems for Video Technology, 1999, 9(8): 1204-1215
[69]Di Z, ShihFu C. An integrated approach for content-based video object segmentation and retrieval. IEEE Transactions on Circuits and Systems for Video Technology,1999, 9(8):1259-1268
[70]季白杨，陈纯，钱英．视频分割技术的发展．计算机研究与发展，2001，38(1)：36—42
[71]Core experiment on multifunctional and ad aspects of MPEG4 video. ISO/IEC JTC1/SC29/WG11/MPEG98/n2176, 1998
[72]Kim M,Choi J G, Lee M H. User-assisted segmentation for moving objects of interest. ISO/IECJTC1/SC29/WG11/MPEG97/m2803,1997
[73]Kim M,Choi J G, Lee M H. User-assisted video object segmentation by multiple object tracking with a graphical user interface. ISO/IECTC1/SC29/WG11/MPEG98/m3349,1998
[74]Kim M, Jeon J G, Kwak J. User.s guide for a user-assisted video object segmentation tool. ISO/IECJTC1/SC29/WG11MPEG98/m3935, 1998
[75]Colonnese S, Russo G. User interactions modes in semiautomatic segmentation: development of a flexible graphical user interface in Java. ISO/IEC JTC1/SC29/WG11 MPEG98/m3320, 1998
[76]Neri A, Colonnese S, Russo G. Video sequence segmentation for object-based coders using higher order statistics.
[77]Marques F, Molina C. Object tacking for content-based functionalities. 1997 SPIE Visual Communication Image Processing, Vol. 1:190-198
[78]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，2004，32(3)：480-484
[79]宋立锋，韦岗，王群生．基于模板匹配的视频对象分割．电子学报，2002，30(7)：1075-1078
[80]朱辉．面向对象生成的视频分割技术研究：[博士学位论文]，成都：电子科技大学，2002
[81]边肇祺，张学工．模式识别．北京：清华大学出版社，2002，23-30
[82]沈清，汤霖．模式识别导论．长沙：国防科技大学出版社，1993，53-62
[83]薛景浩，章毓晋，林行刚．基于特征散度的图像聚类分割．模式识别与人工智能，1998，11(4)：462-467
[84]李春华，杨戍，刘少亭．基于遗传算法的截集FCM灰度图像分割方法研究．西安科技大学学报，2006，26(1)：85-88
[85]王耀南，李树涛，毛建旭．计算机图像处理与识别技术．北京：高等教育出版社，2006，179—181
[86]刘新春．面向MPEG-4的视频分割算法研究：[博士学位论文]，北京：中国科学院，2000
[87]Geman S. Stochastic relaxation, Gibbs distributions, and Bayesian restoration of images.IEEE Trans. Pattern Anal. Machine Intell., 1984, 6(6):721-741
[88]唐常春，吕宏伯，黄铮等编著．数学形态学方法及其应用．北京：科学出版社，1990，10—22
[89]贺玉文，杨士强，钟玉琢．全局运动估计中特征点选取和鲁棒性分析．计算机学报，2001，24(3)：236—241
[90]袁亚湘，孙文瑜．最优化理论与方法．北京：科学出版社，2003，154—164
[91]Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6):679-698
[92]Xiaopeng Wang, Jinwen Luo. Edge detection based on regulated morphological gradient. 2004 International Conference on Computational Electromagnetics and Its Applications, Vol. 1:419-422
[93]Mallat S, Zhong S. Characterization of signals from multiscale edges. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1992, 14(7):710-732
[94]Hanmandlu M, See J, Vasikarla S. Fuzzy edge detector using entropy optimization. 2004 International Conference on Information Technology, Vol. 1:665-670
[95]张文华，朱红英，黄祥辉．克隆和动物克隆．动物学杂志，1999，34(3)：48-51
[96]李洁，高新波，焦利成．基于克隆算法的网络结构聚类新算法．电子学报，2004，32(7)：1195-1199
[97]张其善，金明录．信号复制生成理论及应用．北京：人民邮电出版社，2001，3—4
[98]程正兴．小波分析与应用．西安：西安交通大学出版社，2000，96—100
[99]Saitoh F. Extraction of disconnected closed curve using genetic algorithm with perceptive grouping factors. 1997 IEEE International Workshop on Robot and Human Communication,Vol. 1:254-259
[100]Bhandarkar S M, Hui Zhang. Image segmentation using evolutionary computation. IEEE Transaction on Evolutionary Computation, 1999, 3(1): 1-21
[101]Ferwerda J A. Element of Early Vision for Computer Graphics. IEEE Computer Graphics and Applications, 2001,21(5):22-33
[102]刘文耀．光电图像处理．北京：电子工业出版社，2002，50-52
[103]Gang L, Haralick R M. Two practical issues in canny's edge detector implementation. 2000 International Conference on Pattern Recognition,Vol. 1:676-678
[104]Bezdek J C. Pattern Recognition with fuzzy objective function algorithm. Plenum Press, New York, 1981,103-115
[105]Cheng H D, Chen Y H. Fuzzy partition of two-dimensional histogram and its application to thresholding. Pattern Recognition, 1999, 32(1):825-843
[106]Srinivas M, Pamaik L M. Adaptive probabilities of crossover and mutations in Gas. IEEE Transactions on SMC, 1994,24(4):656:667
[107]张光玉，龚光珍，朱维乐．一种新的基于克隆算法的彩色图像边缘提取算法．电子学报，2006，34(4)：702—707
[108]Changick K, Jenq N H.Fast and automatic video segmentation and tracking for content-based applications. IEEE Transactions on. Circuits and Systems for Video Technology,2002, 12(2):122-129
[109]ISO/IEC WG11 MPEG Video Group, MPEG-4 Video Verification Model Version 15.0. Mauimeeting of ISO/IEC/JTC1/SC29/WG11, MPEG98/N3093, Hawaii,America, 1999
[110]Konrad J, Dufaux F. Improved global motion estimation. N3, Meeting of ISO/IEC/SC29/WG11, No. MPEG97/M 3096, San Jose, 1998
[111]Chuang G, Ming C L.Semiautomatic segmentation and tracking of semantic video objects.IEEE Transactions on Circuits and Systems for Video Technology, 1998, 8(5):572-584
[112]Jungeun L, Jong B R. Semi-automatic video segmentation for object tracking. 2001 International Conference on Image Processing,Vol. 1: 81-84
[113]Chuang Gu; Ming C L. Semantic video object segmentation and tracking using mathematical morphology and perspective motion model. 1997 International Conference on Image Processing,Vol. 1:514-517
[114]Robert C, Touradj E, Mural K. Video segmentation based on multiple features for interactive multimedia applications. EEE Transactions on Circuits and Systems for Video Technology, 1998, 8(5):562-571
[115]陈韩锋，戚飞虎．结合多种语义信息的半自动视频对象分割．电子学报，2002， 30(12):2012-2015
[116]Core experiment on multifunctional and ad aspects of MPEG4 video, ISO/IEC JTC1/SC29/WG11/MPEG98/n2176,1998
[117]Kim M,Choi J G, Lee M H. User-assisted segmentation for moving objects of interest. ISO/IEC JTC1/SC29/WG11/MPEG97/m2803,1997
[118]Kim M,Choi J G, Lee M H, User-assisted video object segmentation by multiple object tracking with a graphical user interface.ISO/IECTC1/SC29/WG11/MPEG98/m3349, 1998
[119]Lu Z; Pearlman W A. Semi-automatic semantic video object extraction by active contour model. 2000 IEEE International Conference on Multimedia and Expo,Vol. 1:645-648
[120]Candemir T; A. Murat T, A. Tanju E. Semi-automatic video object segmentation in the presence of occlusion. IEEE Transactions on Circuits and Systems for Video Technology, 2000, 10(4):624-629
[121]崔屹．数学形态学方法和应用．北京：科学出版社，2000，29—40
[122]黄波，杨勇，王桥等．一种基于时空联合的视频分割算法．电子学报，2001，29(11)：1491-1494
[2]Lee S W, Choi J G, Kim S D. Automatic segmentation of moving objects for video object plane generation. IEEE On Circuits and Systems for Video Technology, 1997,7:279-286
[3]Neri, Colonnese S, Russo G, etal. Automatic moving object and background separation. Signal Processing, 1998,66:219-232
[4]Mech R, Wollborn M. A noise robust method for segmentation of moving objects in video sequences. IEEE 1997 International Conference on Acoustics, Speech and Signal Processing(ICAS SP' 97), 1997,Vol.4:2657-2660
[5]Ju G, Jongwon K. Fast and robust moving object segmentation technique for MPEG-4 object-based coding and functionality. The International Society for Optical Engineering, 1998,3561:106-117
[6]Shao-Yi C, Yu-Wen H, Bing-Yu H. Fast video segmentation algorithm with shadow cancellation, global motion compensation, and adaptive threshold techniques. IEEE Transactions on Multimedia,2004,6(5):732-748
[7]Shao-Yi C, Shyh-Yih M, Liang-Gee C. Efficient moving object segmentation algorithm using background registration technique. IEEE On Circuits and Systems for Video Technology, 2002,12(7):577-586
[8]Shao-Yi C, Shyh-Yih M, Liang-Gee C. A hybrid morphology processing units architecture for real-time video segmentation systems. 2001 IEEE International Symposium on Circuits and Systems,Vol.5:275-278
[9]Thomas M, King N. N. Automatic segmentation of moving objects for video object plane generation. IEEE On Circuits and Systems for Video Technology, 1998,8(5):525-538
[10]Thomas M, King N. N. Video segmentation for content-based coding. IEEE On Circuits and Systems for Video Technology, 1999,9(8): 1190-1203
[11]Munchurl K, Jinwoong K. Moving video object segmentation using statistical hypothesis testing. Electronics Letters, 2000,36(2): 128-129
[12]赵明，李娜，陈纯．采用统计推断的自动视频对象分割．计算机辅助设计与图形学学 报，2003，15(3)：318-333
[13]Jinhui Pan, Chia-Wen Lin, Chuang Gu. A robust video object segmentation scheme with prestored background information. 2002 IEEE International Symposium on Circuits and Systems(ISCAS'02), Vol.3:803-806
[14]Cavallaro A, Touradj E. Accurate video object segmentation through change detection. 2002 International Conference on Multimedia and Expo,Vol. 1:445-448
[15]Dirk F. Automatic video-object segmentation employing multi-sprites with constrained delay. 2006 International Conference on Consumer Electronics,Vol. 1:479-480
[16]Alice C, Laurent B, Jean-Marc C. Robust fast extraction of video objects combining frame differences and adaptive reference image. 2001 International Conference on Image Processing, Vol.2:785-788
[17]Dirk F, Wolfgang E. Video-object segmentation using multi-sprite background subtraction. 2004 International Conference on Multimedia and Expo,Vol. 1:343-346
[18]李晓春，孙培懋，金国藩等．复杂场景中动目标图象分割的一种快速算法．清华大学学报，1997，37(8)：19-21
[19]吴思，张勇东，林守勋等．动态场景视频序列中的前景区域自动提取．计算机辅助设计与图形学学报，2005，17(2)：359—363
[20]Anastacios D, Nikolaos D, Stefanos K A neural network based scheme for unsupervised video object segmentation. 1998 International Conference on Image Processing, Vol.2:632-636
[21]Stephanie J B,Michel B, Gilles A. Region-based active contours for video object segmentation with camera compensation. 2001 International Conference on Image Processing,Vol.2:61-64
[22]Jianping F, David K Y Y. Automatic Image Segmentation by Integrating Color-Edge Extraction and Seeded Region Growing. IEEE Transaction on Image Processing, 2001,10(10): 1454-1466
[23]Tanchroen D, Jitapunkul S, Chompun S. Spatial segmentation based on modified morphological tools. 2001 International Conference on Information Technology Coding and Computing,Vol. 1:478-482
[24]Stephanie J B,Michel B, Gilles A. Video object segmentation using eulerian region-based active contours. 2001 Eighth IEEE International Conference on Computer Vision(ICCV 2001), Vol. 1:353-360
[25]Muriel G, Michel B. Combining shape prior and statistical features for active contour segmentation. IEEE Tranctions on Circuits and Systems for Video Technology,2004, 14(5):726-734
[26] Precioso E, Barland M. B-Spline active contours for fast video segmentation. 2001 International Conference on Image Processing,Vol.2:777-780
[27] Jae G C, SiWoong L, SeongDae K. Spatio-Temporal video segmentation using a joint similarity measure. IEEE Transactions on Circuits and Systems for Video Technology, 1997, 7(2):279-286
[28] Demin W. Unsupervised video segmentation based on watersheds and temporal tracking. IEEE Transactions on Circuits and Systems for Video Technology, 1998, 8(5):539-546
[29] Yaakov T, Amir A. Automatic segmentation of moving objects in video sequences: a region labeling approach. IEEE Transactions on Circuits and Systems for Video Technology,2002, 12(7):597-612
[30] TsungHan T, ChungYuan L. Video segmentation using multiscale feature extraction and fusion. 2005 IEEE International Symposium on Circuits and Systems,Vol.4:3223-3226
[31] Zhou J Y, Ong E P, Ko C C. Video object segmentation and tracking for content-based video coding. 2000 IEEE International Conference on Multimedia and Expo,Vol.3:1555-1558
[32] Kun Z, Qionghai D, Jiang W. Fast tracking of semantic video object based on motion prediction and subregion extraction. 2002 International Conference on Image Processing,Vol.3:621 -624
[33] Chew K T, Mohammed G. Using non-linear diffusion and motion information for video segmentation. 2002 International Conference on Image Processing,Vol.2:769-772
[34] Hamid M R, Baloch A, Bilal A, et al. Object segmentation using feature based conditional morphology. 2003 International Conference on Image Analysis and Processing,Vol. 1:548-553
[35] Thomas M, King N. Video object plane segmentation using a morphological motion filter and hausdorff object tracking. 1998 International Conference on Image Processing,Vol.2:652-656
[36] Xingrong Z, Xianwu H. Video object segmentation based on HOS and multi-resolution watershed. 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, Vol. 1:274-277
[37] ShaoYi C, YuWen H. Predictive watershed: a fast watershed algorithm for video segmentation. IEEE Transactions on Circuits and Systems for Video Technology,2003, 13(5):453-461
[38] Gerald K, Joachim W. A tensor-driven active contour model for moving object segmentation. 2001 International Conference on Image Processing, Vol.2:81-84
[39] Muriel G, Michel B. Video segmentation using active contours on a group of pictures. 2002 International Conference on Image Processing, Vol.2:81-84
[40] Andrea C, Touradj E. Accurate video object segmentation through change detection. 2002 IEEE International Conference on Multimedia and Expo, Vol.1:445-448
[41] Munchurl K, Jae G C. A vop generation tool: automatic segmentation of moving objects in image sequences based on spatio-temporal information. IEEE Transactions on Circuits and Systems for Video Technology, 1999, 9(8): 1216-1226
[42] Ong E P, Tye B J. An efficient video object segmentation scheme. 2002 IEEE International Conference on Acoustics, Speech and Signal Processing, Vol.4:3361-3364
[43] KaiKuang M, HaiYun W. Region-based nonparametric optical flow segmentation with pre-clustering and post-clustering. 2002 IEEE International Conference on Multimedia and Expo,Vol.2:201-204
[44] Huang B, Yong Y. Video segmentation based-on fuzzy clustering and multi-features. 2002 6th International Conference on Signal Processing, Vol.2:977-980
[45] Wei W, King N N. Multiple feature clustering algorithm for automatic video object segmentation. 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.3:625-628
[46] Anastasios D, Nikolaos D. An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture. IEEE Transactions on Neural Networks, 2003, 14(3):616-630
[47] Ioannis P, Hendriks E A. Video segmentation by MAP labeling of watershed segments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001,23(3):326-332
[48] ChionTing MingShen H. Segmentation of non-rigid object in a non-parametric MAP framework. 2003 International Conference on Image Processing, Vol. 1:997-1000
[49] Vasileios M, Ioannis K. Video object segmentation using bayes-based temporal tracking and trajectory-based region merging. IEEE Transactions on Circuits and Systems for Video Technology,2004, 14(6):782-795
[50] Sohaib K, Mubarak S. Object based segmentation of video using color, motion and spatial information. 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.2:746-751
[51] Yang W, KiaFock L. Spatiotemporal video segmentation based on graphical models. IEEE Transactions on Image Processing, 2005, 14(7):937-947
[52] Jian Z, Xiaoping Z. Video object segmentation and tracking using probabilistic fuzzy c-means. 2005 IEEE Workshop on Machine Learning for Signal Processing, Vol. 1:201-206
[53]Joannis K, Michael G S. Spatiotemporal segmentation and tracking of objects for visualization of videoconference image sequences. IEEE Transactions on Circuits and Systems for Video Technology,2000, 10(8): 1388-1402
[54]Wei W, King N N. Integration of motion and image features for automatic video object segmentation. 2004 International Conference on Image Processing, Vol. 1:361-364
[55]Scbastien V, Isabelle M. Active mesh for video segmentation and objects tracking. 2001 International Conference on Image Processing, Vol.2:77-80
[56]Alfied Y, Wael B. On Reducing the size of structured meshes with a novel video object extraction algorithm. 2003 IEEE Canadian Conference on Electrical and Computer Engineering,Vol. 2:1179-1182
[57]Falah E A, Baozong Y. Moving object segmentation from video sequences: an edge approach. 2003 4th EURASIP Conference focused on Video/Image Processing and Multimedia Communications, Vol. 1:193-199
[58]Changick K, JenqNeng H. A fast and robust moving object segmentation in video sequences. 1999 International Conference on Image Processing, Vol.2:131-134
[59]Yah L, Wen G, et al. Automatic video segmentation using a novel background model.2002 IEEE International Symposium on Circuits and Systems, Vol.3:807-810
[60]吴思，林守勋，张勇东．基于动态背景构建的视频运动对象自动分割．计算机学报，2005，28(8)：1386-1392
[61]Rong S, Xiaofeng L. Efficient spatiotemporal segmentation and video object generation for highway surveillance video. 2002 International Conference on Communication, Circuits and Systems and West Sino Exposition, Vol. 1:580-584
[62]Lijie L, Guoliang F. Combined key-frame extraction and object-based video segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2005, 15(7):869-884
[63]Changick K, JenqNeng H. Object-based video abstraction for video surveillance systems. IEEE Transactions on Circuits and Systems for Video Technology,2002, 12(12): 1128-1138
[64]Ibrahim K, JenqNeng H. A framework for fully automatic moving video-object segmentation based on graph partitioning. 2004 International Symposium on Circuits and Systems, Vol.3:845-848
[65]Vigus S A, Bull D R, et al. Video object tracking using region split and merge and a kalman filter tracking algorithm. Proc. 2001 International Conference on Image Processing,Vol. 1:650-653
[66]Hongqiang B, Zhaoyang Z. Object-based video segmentation using spatio-temporal energy. 2004 7th International Conference on Signal Processing, Vol.2:1260-1263
[67]YuPao T, ChihChuan L. A Bayesian approach to video object segmentation via merging 3-D watershed volumes. IEEE Transactions on Circuits and Systems for Video Technology,2005, 15(1):175-180
[68]Stephan H, Hubert M. A video segmentation algorithm for hierarchal object Representations and Its Implementation. IEEE Transactions on Circuits and Systems for Video Technology, 1999, 9(8): 1204-1215
[69]Di Z, ShihFu C. An integrated approach for content-based video object segmentation and retrieval. IEEE Transactions on Circuits and Systems for Video Technology,1999, 9(8):1259-1268
[70]季白杨，陈纯，钱英．视频分割技术的发展．计算机研究与发展，2001，38(1)：36—42
[71]Core experiment on multifunctional and ad aspects of MPEG4 video. ISO/IEC JTC1/SC29/WG11/MPEG98/n2176, 1998
[72]Kim M,Choi J G, Lee M H. User-assisted segmentation for moving objects of interest. ISO/IECJTC1/SC29/WG11/MPEG97/m2803,1997
[73]Kim M,Choi J G, Lee M H. User-assisted video object segmentation by multiple object tracking with a graphical user interface. ISO/IECTC1/SC29/WG11/MPEG98/m3349,1998
[74]Kim M, Jeon J G, Kwak J. User.s guide for a user-assisted video object segmentation tool. ISO/IECJTC1/SC29/WG11MPEG98/m3935, 1998
[75]Colonnese S, Russo G. User interactions modes in semiautomatic segmentation: development of a flexible graphical user interface in Java. ISO/IEC JTC1/SC29/WG11 MPEG98/m3320, 1998
[76]Neri A, Colonnese S, Russo G. Video sequence segmentation for object-based coders using higher order statistics.
[77]Marques F, Molina C. Object tacking for content-based functionalities. 1997 SPIE Visual Communication Image Processing, Vol. 1:190-198
[78]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，2004，32(3)：480-484
[79]宋立锋，韦岗，王群生．基于模板匹配的视频对象分割．电子学报，2002，30(7)：1075-1078
[80]朱辉．面向对象生成的视频分割技术研究：[博士学位论文]，成都：电子科技大学，2002
[81]边肇祺，张学工．模式识别．北京：清华大学出版社，2002，23-30
[82]沈清，汤霖．模式识别导论．长沙：国防科技大学出版社，1993，53-62
[83]薛景浩，章毓晋，林行刚．基于特征散度的图像聚类分割．模式识别与人工智能，1998，11(4)：462-467
[84]李春华，杨戍，刘少亭．基于遗传算法的截集FCM灰度图像分割方法研究．西安科技大学学报，2006，26(1)：85-88
[85]王耀南，李树涛，毛建旭．计算机图像处理与识别技术．北京：高等教育出版社，2006，179—181
[86]刘新春．面向MPEG-4的视频分割算法研究：[博士学位论文]，北京：中国科学院，2000
[87]Geman S. Stochastic relaxation, Gibbs distributions, and Bayesian restoration of images.IEEE Trans. Pattern Anal. Machine Intell., 1984, 6(6):721-741
[88]唐常春，吕宏伯，黄铮等编著．数学形态学方法及其应用．北京：科学出版社，1990，10—22
[89]贺玉文，杨士强，钟玉琢．全局运动估计中特征点选取和鲁棒性分析．计算机学报，2001，24(3)：236—241
[90]袁亚湘，孙文瑜．最优化理论与方法．北京：科学出版社，2003，154—164
[91]Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6):679-698
[92]Xiaopeng Wang, Jinwen Luo. Edge detection based on regulated morphological gradient. 2004 International Conference on Computational Electromagnetics and Its Applications, Vol. 1:419-422
[93]Mallat S, Zhong S. Characterization of signals from multiscale edges. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1992, 14(7):710-732
[94]Hanmandlu M, See J, Vasikarla S. Fuzzy edge detector using entropy optimization. 2004 International Conference on Information Technology, Vol. 1:665-670
[95]张文华，朱红英，黄祥辉．克隆和动物克隆．动物学杂志，1999，34(3)：48-51
[96]李洁，高新波，焦利成．基于克隆算法的网络结构聚类新算法．电子学报，2004，32(7)：1195-1199
[97]张其善，金明录．信号复制生成理论及应用．北京：人民邮电出版社，2001，3—4
[98]程正兴．小波分析与应用．西安：西安交通大学出版社，2000，96—100
[99]Saitoh F. Extraction of disconnected closed curve using genetic algorithm with perceptive grouping factors. 1997 IEEE International Workshop on Robot and Human Communication,Vol. 1:254-259
[100]Bhandarkar S M, Hui Zhang. Image segmentation using evolutionary computation. IEEE Transaction on Evolutionary Computation, 1999, 3(1): 1-21
[101]Ferwerda J A. Element of Early Vision for Computer Graphics. IEEE Computer Graphics and Applications, 2001,21(5):22-33
[102]刘文耀．光电图像处理．北京：电子工业出版社，2002，50-52
[103]Gang L, Haralick R M. Two practical issues in canny's edge detector implementation. 2000 International Conference on Pattern Recognition,Vol. 1:676-678
[104]Bezdek J C. Pattern Recognition with fuzzy objective function algorithm. Plenum Press, New York, 1981,103-115
[105]Cheng H D, Chen Y H. Fuzzy partition of two-dimensional histogram and its application to thresholding. Pattern Recognition, 1999, 32(1):825-843
[106]Srinivas M, Pamaik L M. Adaptive probabilities of crossover and mutations in Gas. IEEE Transactions on SMC, 1994,24(4):656:667
[107]张光玉，龚光珍，朱维乐．一种新的基于克隆算法的彩色图像边缘提取算法．电子学报，2006，34(4)：702—707
[108]Changick K, Jenq N H.Fast and automatic video segmentation and tracking for content-based applications. IEEE Transactions on. Circuits and Systems for Video Technology,2002, 12(2):122-129
[109]ISO/IEC WG11 MPEG Video Group, MPEG-4 Video Verification Model Version 15.0. Mauimeeting of ISO/IEC/JTC1/SC29/WG11, MPEG98/N3093, Hawaii,America, 1999
[110]Konrad J, Dufaux F. Improved global motion estimation. N3, Meeting of ISO/IEC/SC29/WG11, No. MPEG97/M 3096, San Jose, 1998
[111]Chuang G, Ming C L.Semiautomatic segmentation and tracking of semantic video objects.IEEE Transactions on Circuits and Systems for Video Technology, 1998, 8(5):572-584
[112]Jungeun L, Jong B R. Semi-automatic video segmentation for object tracking. 2001 International Conference on Image Processing,Vol. 1: 81-84
[113]Chuang Gu; Ming C L. Semantic video object segmentation and tracking using mathematical morphology and perspective motion model. 1997 International Conference on Image Processing,Vol. 1:514-517
[114]Robert C, Touradj E, Mural K. Video segmentation based on multiple features for interactive multimedia applications. EEE Transactions on Circuits and Systems for Video Technology, 1998, 8(5):562-571
[115]陈韩锋，戚飞虎．结合多种语义信息的半自动视频对象分割．电子学报，2002， 30(12):2012-2015
[116]Core experiment on multifunctional and ad aspects of MPEG4 video, ISO/IEC JTC1/SC29/WG11/MPEG98/n2176,1998
[117]Kim M,Choi J G, Lee M H. User-assisted segmentation for moving objects of interest. ISO/IEC JTC1/SC29/WG11/MPEG97/m2803,1997
[118]Kim M,Choi J G, Lee M H, User-assisted video object segmentation by multiple object tracking with a graphical user interface.ISO/IECTC1/SC29/WG11/MPEG98/m3349, 1998
[119]Lu Z; Pearlman W A. Semi-automatic semantic video object extraction by active contour model. 2000 IEEE International Conference on Multimedia and Expo,Vol. 1:645-648
[120]Candemir T; A. Murat T, A. Tanju E. Semi-automatic video object segmentation in the presence of occlusion. IEEE Transactions on Circuits and Systems for Video Technology, 2000, 10(4):624-629
[121]崔屹．数学形态学方法和应用．北京：科学出版社，2000，29—40
[122]黄波，杨勇，王桥等．一种基于时空联合的视频分割算法．电子学报，2001，29(11)：1491-1494