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video segmentation based on area selection

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International English Education Research 2013年第12期 168-169页

This paper presents a video motion object segmentation method based on area selection. This method uses a simple and practical space first region segmentation method, it through the motion information and space-time energy model to multiple choice of area, at lask the accurate segmentation object can be obtained throuth some post-processing technology. Experiments prove that this algorithm has good robustness.

关键词： area video segmentation The frame difference

Automatic video segmentation Algorithm by Background Model and Color Clustering

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Journal of Beijing Institute of Technology 2003年第S1期12卷 134-138页

作者：沙芸王军刘玉树 Department of Computer Science and Engineering School of Information Science and TechnologyBeijing Institute TechnologyBeijing 100081China

In order to detect the object in video efficiently, an automatic and real time video segmentation algorithm based on background model and color clustering is proposed. This algorithm consists of four phases: background restoration, moving objects extract, moving objects region clustering and post processing. The threshold of the background restoration is not given in advanced. It can be gotten automatically. And a new object region cluster algorithm based on background model and color clustering to remove significance noise is proposed. An efficient method of eliminating shadow is also used. This approach was compared with other methods on pixel error ratio. The experiment result indicates the algorithm is correct and efficient.

关键词： video segmentation background restoration object region cluster

Adaptive foreground and shadow segmentation using hidden conditional random fields

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第4期8卷 586-592页

作者： CHU Yi-ping YE Xiu-zi QIAN Jiang ZHANG Yin ZHANG San-yuan School of Computer Science State Key Lab. of CAD ＆ CG Zhejiang University Hangzhou 310027 China

video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is proposed, which models spatio-temporal constraints of video sequence. In order to improve the segmentation quality, the weights of spatio-temporal con- straints are adaptively updated by on-line learning for HCRFs. Shadows are the factors affecting segmentation quality. To separate foreground objects from the shadows they cast, linear transform for Gaussian distribution of the background is adopted to model the shadow. The experimental results demonstrated that the error ratio of our algorithm is reduced by 23% and 19% respectively, compared with the Gaussian mixture model (GMM) and spatio-temporal Markov random fields (MRFs).

关键词： video segmentation Shadow elimination Hidden conditional random fields (HCRFs) On-line learning

Efficient video cutout based on adaptive multilevel banded method

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Science China(Information Sciences) 2012年第5期55卷 1082-1092页

作者： CHEN MinGang 1,SUI BoCong 1,GAO Yan 2 & MA LiZhuang 1 1 Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China 2 Software Engineering Institute,East China Normal University,Shanghai 200062,China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Software Engineering Institute East China Normal University Shanghai China

Although many efficient image cutout approaches have been proposed,it is still a challenge to cut out foreground objects from a video clip because of the costs of time and memory consumption and the difficulty in maintaining temporal coherence for the cutout *** this paper,we propose a novel and efficient video cutout approach by extending a multilevel banded image segmentation method to video *** our method,the segmentation results are propagated frame by frame at the lowest resolution level based on optical flow,and are refined in image pyramids level by level through banded graph *** construct an adaptive narrow band to ensure that the segmentation result falls exactly into the narrow band during *** results show that the video cutout approach based on adaptive multilevel bands dramatically reduces time and memory costs,and achieves ideal segmentation results.

关键词： video object cutout multilevel band graph cuts video segmentation

video Structure Analysis

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Tsinghua Science and Technology 2007年第6期12卷 714-718页

作者：张松海张一飞陈韬 Peter M. Hall Ralph Martin Department of Computer Science and Technology Tsinghua University Department of Computer Science University of Bath Bath BA2 7AY United Kingdom School of Computer Science Cardiff University Wales CF24 3AA United Kingdom

video structure analysis is a basic requirement for most content-based video editing and processing systems. This paper presents a fast video structure analysis method based on image segmentation in each frame, with region matching between frames. The structure analysis decomposes the video into several moving objects, including information about their colors, positions, shapes, movements, and lifetimes. The method also supports user interactions to improve the results. The result shows that this method is fast and stable and can complete video analyzinq interactivelv.

关键词： video structure analysis video segmentation image segmentation region matching