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標題: 以移動區域為基礎之視訊物件分割
Video Objects Segmentation Based on Moving Regions
作者: 林恆毅
Lin, Heng-Yi
關鍵字: video object segmentation;視訊物件分割;watershed segmentation;Markov random field;ICM algorithm;分水嶺分割;馬可夫隨機領域;ICM演算法
出版社: 電機工程學系所
引用: [1] R. V. Babu, K. R. Ramakrishnan and S. H. Srinivasan, “Video object segmentation: a compressed domain approach,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 4, pp. 462-473, Apr. 2004. [2] J. Besag, “On the statistical analysis of dirty pictures,” J. R. Stat. Soc. B, vol. 48, no. 3, pp. 259-302, 1986. [3] V. Boskovitz and H. Guterman, “An adaptive neuro-fuzzy system for automatic image segmentation and edge detection.” IEEE Trans. Fuzzy Syst., vol. 10, no. 2, pp.247-261, Apr. 2002. [4] R. C. Gonzalez and R. E. Woods, Digital Image Processing Second Edition, Prentice Hall, 2002. [5] T. Papadimitriou, K. I. Diamantraras, M. G. Strintzis and M. Roumeliotis, “Video scene segmentation using spatial contours and 3-D robust motion estimation,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 4, pp. 485-496, Apr. 2004. [6] S. -J. Lee, C. -S Ouyang and S. -H. Du, “A neuro-fuzzy approach for segmentation of human objects in image sequences,” IEEE Trans. Syst. Man Cybernetics, part. B, vol. 33, no. 3, pp. 420-437, Jun. 2003. [7] Y. Liu and Y. F. Zheng, “Video object segmentation and tracking using psi-learning classification,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 7, pp. 885-899, Jul. 2005. [8] Y.-P. Tsai, C.-C. Lai, Y.-P. Hung, and Z.-C. Shih, “A bayesian approach to video object segmentation via merging 3-D watershed volumes,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 1, pp. 175-180, Jan. 2005 [9] Y. Tsaig and A. Averbuch, “Automatic segmentation of moving objects in video sequences: a region labeling approach,” IEEE Trans. Circuits Syst. Video Technol., vol. 12, pp. 597-612, Jul. 2002. [10] D. Wang. “A multiscale gradient algorithm for image segmentation using watersheds,” Patten Recognit., vol. 30, no. 12, pp. 2043-2052, 1997. [11] L. Vincent, and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Trans. of Pattern Anal. and Machine Intell., vol. 13, no. 6, pp. 583-598, Jun. 1991. [12] L. Vincent, "Morphological grayscale reconstruction in image analysis: applications and efficient algorithms," IEEE Trans. on Image Process., vol. 2, no. 2, pp. 176-201, Apr. 1993. [13] S. Zhu and K.-K. Ma, “A new diamond search algorithm for fast block-matching motion estimation,” IEEE Trans. Image Process., vol. 9, no. 2, pp. 287-290, Feb. 2000.

The segmentation of video objects is an important research topic in digital video processing. Due to the unreliability of object motion information and the lack of higher level guidance, video objects segmentation is still a challenging topic. Since the approach suitable for general situations is almost not feasible, most of the researches focus on the video object segmentation with constraints depending on different applications to obtain reasonable results. In the same view, we restrict video objects to foreground objects and background objects. Therefore, the purpose of this thesis is to extract foreground objects with motion and the static background objects. The method we used includes two parts: the initial segmentation and the MRF region classification. First, the watershed algorithm is used to segment the image to acquire regions with spatial coherence. Second, the Markov Random Field (MRF) approach is adopted to classify the regions into the foreground and the background regions. This approach is designed to process the QCIF or CIF video sequences in the YUV format.
其他識別: U0005-2507200616231600
Appears in Collections:電機工程學系所

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