In order to obtain the initial video objects from the video sequences, an improved initial video object extraction algorithm based on motion connectivity is proposed. Moving objects in video sequences are highly conne...In order to obtain the initial video objects from the video sequences, an improved initial video object extraction algorithm based on motion connectivity is proposed. Moving objects in video sequences are highly connected and structured, which makes motion connectivity an advanced feature for segmentation. Accordingly, after sharp noise elimination, the cumulated difference image, which exhibits the coherent motion of the moving object, is adaptively thresholded. Then the maximal connected region is labeled, post-processed and output as the final segmenting mask. Hence the initial video object is effectively extracted. Comparative experimental results show that the proposed algorithm extracts the initial video object automatically, promptly and properly, thereby achieving satisfactory subjective and objective performance.展开更多
Video object extraction is a key technology in content-based video coding.A novel video object extracting algorithm by two Dimensional (2-D) mesh-based motion analysis is proposed in this paper.Firstly,a 2-D mesh fitt...Video object extraction is a key technology in content-based video coding.A novel video object extracting algorithm by two Dimensional (2-D) mesh-based motion analysis is proposed in this paper.Firstly,a 2-D mesh fitting the original frame image is obtained via feature detection algorithm. Then,higher order statistics motion analysis is applied on the 2-D mesh representation to get an initial motion detection mask.After post-processing,the final segmenting mask is quickly obtained.And hence the video object is effectively extracted.Experimental results show that the proposed algorithm combines the merits of mesh-based segmenting algorithms and pixel-based segmenting algorithms,and hereby achieves satisfactory subjective and objective performance while dramatically increasing the segmenting speed.展开更多
Efficient, interactive foreground/background seg- mentation in video is of great practical importance in video editing. This paper proposes an interactive and unsupervised video object segmentation algorithm named E-G...Efficient, interactive foreground/background seg- mentation in video is of great practical importance in video editing. This paper proposes an interactive and unsupervised video object segmentation algorithm named E-GrabCut con- centrating on achieving both of the segmentation quality and time efficiency as highly demanded in the related filed. There are three features in the proposed algorithms. Firstly, we have developed a powerful, non-iterative version of the optimiza- tion process for each frame. Secondly, more user interaction in the first frame is used to improve the Gaussian Mixture Model （GMM）. Thirdly, a robust algorithm for the follow- ing frame segmentation has been developed by reusing the previous GMM. Extensive experiments demonstrate that our method outperforms the state-of-the-art video segmentation algorithm in terms of integration of time efficiency and seg- mentation quality.展开更多
基金The National Natural Science Foundation of China(No60672094)
文摘In order to obtain the initial video objects from the video sequences, an improved initial video object extraction algorithm based on motion connectivity is proposed. Moving objects in video sequences are highly connected and structured, which makes motion connectivity an advanced feature for segmentation. Accordingly, after sharp noise elimination, the cumulated difference image, which exhibits the coherent motion of the moving object, is adaptively thresholded. Then the maximal connected region is labeled, post-processed and output as the final segmenting mask. Hence the initial video object is effectively extracted. Comparative experimental results show that the proposed algorithm extracts the initial video object automatically, promptly and properly, thereby achieving satisfactory subjective and objective performance.
基金Supported by the National Natural Science Foundation of China (No.60672094).
文摘Video object extraction is a key technology in content-based video coding.A novel video object extracting algorithm by two Dimensional (2-D) mesh-based motion analysis is proposed in this paper.Firstly,a 2-D mesh fitting the original frame image is obtained via feature detection algorithm. Then,higher order statistics motion analysis is applied on the 2-D mesh representation to get an initial motion detection mask.After post-processing,the final segmenting mask is quickly obtained.And hence the video object is effectively extracted.Experimental results show that the proposed algorithm combines the merits of mesh-based segmenting algorithms and pixel-based segmenting algorithms,and hereby achieves satisfactory subjective and objective performance while dramatically increasing the segmenting speed.
文摘Efficient, interactive foreground/background seg- mentation in video is of great practical importance in video editing. This paper proposes an interactive and unsupervised video object segmentation algorithm named E-GrabCut con- centrating on achieving both of the segmentation quality and time efficiency as highly demanded in the related filed. There are three features in the proposed algorithms. Firstly, we have developed a powerful, non-iterative version of the optimiza- tion process for each frame. Secondly, more user interaction in the first frame is used to improve the Gaussian Mixture Model （GMM）. Thirdly, a robust algorithm for the follow- ing frame segmentation has been developed by reusing the previous GMM. Extensive experiments demonstrate that our method outperforms the state-of-the-art video segmentation algorithm in terms of integration of time efficiency and seg- mentation quality.
