Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/6893
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.
摘要: 
視訊物件分割在數位視訊處理中佔有重要的地位。因為擷取移動資訊的不可靠性以及缺少較高階的資訊,視訊物件分割至今仍然是一項困難的研究。由於一個全面性的解決方案難度過高,所以現今主要的研究方法都存在有部分限制的前提,而這些限制則視應用與研究目的而定。由於視訊中的移動區域多數就是我們感興趣的部分,因此在這篇論文中,我們則將視訊中的物件數目固定為兩個,分別為含移動區域的前景與靜態的背景。所以,我們的目的便在於找出視訊中具有移動量的前景,與靜止不動的背景。我們使用的方法主要分成兩個部分:初始分割和馬可夫隨機領域區塊分類。其中初始分割的部分我們利用分水嶺分割找出具空間一致性的所有區塊,而馬可夫隨機領域區塊分類則用來將初始分割的結果進一步從背景中分類出前景的部分。我們將這個方法應用在處理QCIF或CIF規格的YUV視訊。

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.
URI: http://hdl.handle.net/11455/6893
其他識別: U0005-2507200616231600
Appears in Collections:電機工程學系所

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.