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研究生:陳星全
研究生(外文):Hsing-Chuang Chen
論文名稱:利用累增資訊之影片分割演算法
論文名稱(外文):Algorithms for Unsupervised Video Segmentation based on Accumulative Information
指導教授:賴源泰
指導教授(外文):Yen-Tai Lai
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:57
中文關鍵詞:動態物件分割累增資訊影片分割
外文關鍵詞:MPEG-4moving object segmentationvideo segmentation
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隨著對於內容性應用資訊的高度需求,傳統以"張"為基礎的影片已不敷需求。新一代的多媒體應用已經朝物件影片發展,那是一種只有單一物件不含背景的影片。例如,MPEG-7已經定義出提供使用者依據物件形狀作視訊資料搜尋的標準,而在MPEG-4中也訂定了內容性應用(content-based)的功能,它將連續的影片拆分成一到數個影像物件平面(video object planes),簡稱VOP’s ,每個VOP代表各一個移動的物件,如此一來可以對他們加以重新組合成一部新的影片或者針對他們的形狀去作壓縮處理。由此可知,發展出能從一般影片擷取出物件的技術是非常重要的。
本論文中,我們由硬體設計的觀點提出一個新的分割方法,該方法提供了一個合理的流程來粹取出VOP,過程中沒有過於複雜的計算而且結果也相當令人滿意。
As the demand for content-based information retrieval goes high, traditional "frame"-based videos are not adequate. Novel multimedia applications are looking for object videos, a video sequence has only one object without background, to support flexible utilization. For instance, MPEG-7 (Moving Picture Experts Group) has defined standardized functionality that allow users to search visual content according to object shapes. Meanwhile, MPEG-4 video standard verification model includes the content-based functionality to decompose a video sequence into one or several video object planes (VOP's), so that each VOP represents one moving object, and they can be recomposed as a new video sequence or be compressed according to their shapes. Therefore, to develop the technique of extracting objects from plain videos is very important.
In this paper, we propose a new video object segmentation method with the consideration on hardware implementation. The method provides a reasonable VOP extraction procedure without complicated computation. Experimentally, This method has better results than others.
ABSTRACT
CONTENTS
LIST OF FIGURES

CHAPTER 1 Introduction………………………………………………………………………1
1.1 Content-based functionality …………………………………………………………1
1.2 Video Segmentation………………………………………………………………………2
1.3 The Motivation……………………………………………………………………………5
CHAPTER 2 Conventional Segmentation Methods …………………………………………6
2.1 Basis of Segmentation …………………………………………………………………6
2.2 Intra-frame Segmentation………………………………………………………………8
2.2.1 Histogram-based Segmentation………………………………………………………8
2.2.2 Edge-based Segmentation ……………………………………………………………9
2.2.3 Region-based Segmentation…………………………………………………………13
2.3 Inter-frame Segmentation ……………………………………………………………16
2.3.1 Motion-based Segmentation…………………………………………………………16
2.4 Object Tracking…………………………………………………………………………19
2.5 Morphological Operations ……………………………………………………………21
2.6 Other Operative Video Segmentation Methods ……………………………………23
2.6.1 Background Subtraction Method……………………………………………………23
2.6.2 Moving Connected Component Method………………………………………………24
2.6.3 Modified Watershed Algorithm ……………………………………………………25
2.6.4 Applying Canny Edge Detector ……………………………………………………26
CHAPTER 3 Design of Unsupervised Video Segmentation………………………………28
3.1 Block Diagram……………………………………………………………………………29
3.2 Temporal Segmentation…………………………………………………………………29
3.2.1 Motion Blocks…………………………………………………………………………31
3.2.2 Extraction of Motion Blocks………………………………………………………31
3.2.3 Noise Removal…………………………………………………………………………33
3.3 Spatial Segmentation …………………………………………………………………36
3.3.1 Color Degradation……………………………………………………………………36
3.3.2 Laplacian Edge Operator with Color Degradation ……………………………38
3.3.3 MB Mapping ……………………………………………………………………………39
3.3.4 Contour Corrector……………………………………………………………………39
3.3.5 VOP Extraction ………………………………………………………………………45
CHAPTER 4 Experimental Results …………………………………………………………48
4.1 The Diagram of Experiment Processes………………………………………………48
4.2 Experiment of Proposed Algorithm …………………………………………………49
CHAPTER 5 Conclusions………………………………………………………………………54
REFERENCES………………………………………………………………………………………55
References
[1] Thomas Sikora,"The MPEG-4 Video Standard Verification Model", IEEE Transactions on Circuits and Systems for Video Technology, VOL. 7, NO. 1, Feb 1997.
[2] Rafael C. Gonzalez, Richard E. Woods,"Digital Image Processing", Addison-Wesley Publishing Company, 1992.
[3] John C. Russ,"The Image Processing Handbook", 2nd edition, pp. 348-349, CRC Press Inc., 1995
[4] Thomas Meier and King N Ngan,"Automatic Segmentation of Moving Objects for Video Object Plane Generation", IEEE Transactions on Circuits and System for Video Technology, VOL. 8, NO. 5, Sep 1998.
[5] J.F. Canny,"A Computational Approach to Edge Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6) pp. 679-698, Nov 1986.
[6] L. Vincent and P. Soille,"Watersheds in digital spaces: An efficient algorithm based on immersion simulations", IEEE Trans. Pattern Anal. Machine Intell., VOL. 13, pp. 583-598, 1991.
[7] Demin Wang,"Unsupervised Video Segmentation Based on Watersheds and Temporal Tracking", IEEE Transactions on Circuits and Systems for Video Technology, VOL. 8, NO. 5, Sep 1998.
[8] Jinhui Pan, Shipeng Li, Ya-Qin Zhang,"Automatic Extraction of Moving Objects Using Multiple Features and Multiple Frames", ISCAS 2000 – IEEE International Symposium on Circuits and Systems, May 28-31, 2000, Geneva, Switzerland.
[9] Shyh-Yih Ma, Chi-Kuang Chen, Shao-Yi Chien, and Liang-Gee Chen,"Moving Object Segmentation Algorithm for Camera-on-a-Chip Systems", 10th VLSI Design/CAD Symposium, pp. 263-266, 1999.
[10] Ju Guo, Jongwon Kim, and C.-C. Jay Kuo,"Fast Video Object Segmentation Using Affine Motion and Gradient-based Color Clustering".
[11] Changick Kim and Jenq-Neng Hwang,"An Integrated Scheme for Object0based Video Abstraction".
[12] Dong Kwon Park, Ho Seok Yoon, and Chee Sun Won,"Fast Object Tracking in Digital Video"
[12] Changick Kim and Jenq-Neng Hwang,"A Fast and Robust Moving Object Segmentation in Video Sequences", IEEE international conference on Image Processing (ICIP’99), Kobe, Japan, Oct 1999.
[13] Ismail Haritaoglu, David Harwood, and Larry S. Davis,"W4: Who? When? Where? What? A real Time System for Detecting and Tracking People", in Proc. The third IEEE Intl. Conf. Automatic Face and Gesture Recognition, pp. 222-227, Los Alamitos, CA, 1998.
[14] Alexandre R.J. Francois and Gerard G. Medioni,"Adaptive Color Background Modeling for real-Time Segmentation of Video Streams".
[15] Chin-Hwa Kuo and Tay-Shen Wang,"A Real-time Segmentation Scheme for Continuous Color Images".
[16] Chia-Wen Lin, Yao-Jen Chang, Yung-Chang Chen, and Ming-Ting Sun,"Implementation of a Real Time Object-based Virtual Meeting System", IEEE, 2001.
[17] Jae-Ho Choi, Seung-Phil Lee, and Hoon-Sung Kwak,"Moving Objects Extraction for Image Sequence Analysis by Cone and Kalman Filtering".
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