In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture （MoCap） has been widely used in the field of large amount of motion capture data...In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture （MoCap） has been widely used in the field of large amount of motion capture databases are available and this is significant for the reuse of motion data. But due to the high degree of freedoms and high capture frequency, the dimension of the mo- tion capture data is usually very high and this will lead to a low efficiency in data processing. So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can＇t ignore. In this paper, first we lay out some problems about the key techniques in motion capture data processing. Then the existing approaches are analyzed and sum- marized. At last, some future work is proposed.展开更多
Recognizing scene information in images or has attracted much attention in computer vision or videos, such as locating the objects and answering ＂Where am research field. Many existing scene recognition methods focus...Recognizing scene information in images or has attracted much attention in computer vision or videos, such as locating the objects and answering ＂Where am research field. Many existing scene recognition methods focus on static images, and cannot achieve satisfactory results on videos which contain more complex scenes features than images. In this paper, we propose a robust movie scene recognition approach based on panoramic frame and representative feature patch. More specifically, the movie is first efficiently segmented into video shots and scenes. Secondly, we introduce a novel key-frame extraction method using panoramic frame and also a local feature extraction process is applied to get the representative feature patches （RFPs） in each video shot. Thirdly, a Latent Dirichlet Allocation （LDA） based recognition model is trained to recognize the scene within each individual video scene clip. The correlations between video clips are considered to enhance the recognition performance. When our proposed approach is implemented to recognize the scene in realistic movies, the experimental results shows that it can achieve satisfactory performance.展开更多
基金Supported by the National Natural Science Foundation of China(No.60875046)by Program for Changjiang Scholars and Innovative Research Team in University(No.IRT1109)+5 种基金the Key Project of Chinese Ministry of Education(No.209029)the Program for Liaoning Excellent Talents in University(No.LR201003)the Program for Liaoning Science and Technology Research in University(No.LS2010008,2009S008,2009S009,LS2010179)the Program for Liaoning Innovative Research Team in University(Nos.2009T005,LT2010005,LT2011018)Natural Science Foundation of Liaoning Province(201102008)by"Liaoning BaiQianWan Talents Program(2010921010,2011921009)"
文摘In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture （MoCap） has been widely used in the field of large amount of motion capture databases are available and this is significant for the reuse of motion data. But due to the high degree of freedoms and high capture frequency, the dimension of the mo- tion capture data is usually very high and this will lead to a low efficiency in data processing. So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can＇t ignore. In this paper, first we lay out some problems about the key techniques in motion capture data processing. Then the existing approaches are analyzed and sum- marized. At last, some future work is proposed.
基金supported by the National Funds for Distinguished Young Scientists of China under Grant No.60925010the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20120005130002+1 种基金the Cosponsored Project of Beijing Committee of Education,the Funds for Creative Research Groups of China under Grant No.61121001the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant No.IRT1049
文摘Recognizing scene information in images or has attracted much attention in computer vision or videos, such as locating the objects and answering ＂Where am research field. Many existing scene recognition methods focus on static images, and cannot achieve satisfactory results on videos which contain more complex scenes features than images. In this paper, we propose a robust movie scene recognition approach based on panoramic frame and representative feature patch. More specifically, the movie is first efficiently segmented into video shots and scenes. Secondly, we introduce a novel key-frame extraction method using panoramic frame and also a local feature extraction process is applied to get the representative feature patches （RFPs） in each video shot. Thirdly, a Latent Dirichlet Allocation （LDA） based recognition model is trained to recognize the scene within each individual video scene clip. The correlations between video clips are considered to enhance the recognition performance. When our proposed approach is implemented to recognize the scene in realistic movies, the experimental results shows that it can achieve satisfactory performance.
