近來，為了現今多媒體系統的新需求，像是人機介面的互動，MPEG-4的標準被相應地設計出來。在MPEG-4裡，為了達到那些多媒體系統的新需求的關係，串流影像可以被分成許多的影像物件層面(VOPs)。這些VOPs可以各自分開地編碼、儲存、或傳輸。由於在MPEG-4的串流影像中VOP是最基本的人機互動的單元，對於一個MPEG-4的編碼系統，如何從連續的影像中自動地或半自動地分割那些合適的VOPs已經變成一個最重要的議題，這也是這篇論文所想達到的目標。
在這篇論文中我們將在一個連續影像中發展一個能夠萃取出影像物件的技術，這技術能夠在連續的MPEG-4的測試影像中區分開兩個或多個以上的影像物件區域，並且更能夠把那些影像物件區域編碼成MPEG-4的VOPs.
首先，我們利用wavelet的一些特性來改善原本的change detection，之後我們就可以藉由改善的change detection得到一個較好的移動物件的邊緣。第二，為了能夠萃取輪廓我們使用一個以邊緣為基本的方法，這方法是使用canny edge detection和連續邊緣組成零件的邊緣標籤方法把連續的edge貼上同樣的標籤。第三，我們結合了以上兩個資訊，得到一個更為完整的輪廓邊緣。雖然我們能夠抓取了在實際輪廓邊緣位置上的邉，但是這些我們所抓取的邉通常都會產生多個缺口。因為有時候影像本身就並不含有清晰的輪廓，所以對於這些缺口我們必須找出一些方法來補足這些缺口。因此，我們提出了一個多層次的預測方法，藉由將邊緣端點往預測的方向延伸的方式，來補足這些間格在我們所抓取的不連續的邊緣上的缺口。最後，我們使用了一個簡單的連接方式來連接那些距離短小的缺口(距離=1或2)。這將會使得我們的結果更為封閉與平順。
使用許多的測試影像序列的實驗結果顯示，這個新的影像物件自動翠取演算法能夠得到精確的物件遮罩。

Recently, for the new demands of nowadays multimedia system, such as video interaction, the MPEG-4 standard has been designed. In MPEG-4, because of those new demands of nowadays multimedia system the video stream can be divided into several video object planes ( VOPs ). Those VOPs can be separately encoded, stored, or transmitted. VOP is the basic interactive unit in MPEG-4 video stream, how to automatically or semi-automatically separate appropriate VOPs from an image sequence has become one of the most important issues for an MPEG-4 system, which is also the goal of this proposal. However, MPEG-4 does not provide concrete techniques for VOP extraction. Nonetheless, it is very difficult to extract VOPs, thus the preprocessing used to decompose sequences into VOPs becomes an important issue for an MPEG-4 system, which is also the goal of this thesis.
In this thesis, we will develop techniques for segmenting images contained in an image sequence, which can separate two or more image segments ( or regions ) from MPEG-4 test image sequences, and those image segments can be coded as MPEG-4 VOPs.
First, we utilize the feature of wavelet to improve the change detection, such that we can obtain a better result of the moving object edge by improved change detection. Second, we use an edge-based method for tracking boundary which is using the canny edge detection and the connected edge component labeling to label those edges. Third, we can combine those two information to obtain a more complete boundary by extracting moving object edges. Although we catch all the edges which is detected on the location of the true boundary, it usually occurs some gaps on which we catch. Because it sometimes will not have a clear boundary, we have to find some method to complete these gaps. Therefore, we propose a multi-level prediction scheme to complete the gaps between the disjoint edges of the boundary we caught by extending the edges on the predictive direction. Final, we use a simple connecting operation for the little gaps (distance=1 or 2). That will make the result more close and smooth.
Experimental results for several test sequences show that this novel automatic video segmentation algorithm can give a more accurate object masks.

Content page
中文摘要 …………………………………………………………………... i
Abstract …………………………………………………………………... iii
List of Figures ……………………………………………………………… v
Content …………………………………………………………………... vii
Chapter 1 Introduction……………………………….................................. 1
1-1 background………………………………........................... 1 1-2 Motives and Objectives....................................................... 6
Chapter 2 Relative Works…………………………………………………. 8
2-1 Watershed method………………………………………… 8
2-1.1 Morphological watershed…………………………… 8
2-1.2 Vincent and Soille’s algorithm……………………… 10
2-1.3 Result of Watershed algorithm………….…………. 12
2-1.4 Segmentation………………………………………… 12
2-1.5 Conclusion of the watershed method………………. 13
2-2 SNAKE method..…………………………………………... 14
2-1.1 Introduction for the Active Contour Models………. 14
2-2.2 SNAKE……………………………………………….. 17
2-2.3 result…………………………………....……………. 21
2-2.4 Conclusion of the snake method……………………. 22
2-3 C.K. method………………………………………………... 23
2-3.1 Change detection…………………………………….. 23
2-3.2 kim’s method………………………………………… 23
2-3.3 Conclusion of the Kim’s method……………………. 27
Chapter 3 Propose Method…………………………………………………. 28
3-1 Wavelet-based moving object edge map………………….. 31
3-2 Edge labeling by improving connected component labeling 36
3-3 Predictive extending algorithm………………………….… 41
Chapter 4 Experiment Result…………………………………………….… 51
Chapter 5 Conclusion……………………………………………………….. 57
Reference ………………………

Reference
[1] M.Kass, A.Witkin, and D. Terzopoulos, “Snakes: Active Contour Models,” Int’l J. Computer Vision, vol. 1, no. 4, pp. 321-331, 1987.
[2] S.J. Osher and J.A. Sethian, “Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Halmiton-Jacobi Formulations,” J. Computational Physics, vol. 72, pp. 12-49, 1988.
[3] S. Geman and D. Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-741, Nov. 1984.
[4] D. Mumford and J. Shah, ”Optimal Approximation by Piexewise Smooth Functions and Associated Variational Problems,” Comm. Pure and Applied Math., vol. 42, pp. 577-684, 1989.
[5] J.Y.A.Wang and Edwand H.Adelson, “Representing Moving Images with Layers”, IEEE Transactions on Image Processing, Special Issue: Image Sequences Compression, vol.3, no.5, pp.652-638, 1994.
[6] D.W.Murray and B.F.Buxton, “Scene Segmentation from Visual Moving Using Global Optimization”, IEEE Transactions on PAMI, vol.9, no.2, March, pp.220-228, 1987.
[7] Hui Zhu and Zaiming Li, “A Video Segmentation Algorithm Based on Spatial-Temporal Information”, IEEE 2002.
[8] E.P. Ong, B.J. Tye, W.S. Lin, *M.Etoh, “An Efficient Video Object Segmentation Scheme”, IEEE 2002.
[9] Kim,C., and Hwang, J.-N.: “Fast and Automatic Video Object Segmentation and Tracking for Content-Based Applications”, IEEE Trans. Circuit Syst. Video Technol., 2002, 12, (2), pp. 122-129.
[10] Shao-Yi Chien, Yu-Wen Huang, and Liang-Gee Chen, “Predictive Watershed: A Fast Watershed Algorithm for Video Segmentation”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 13, no. 5, May 2003.
[11] Day-Fann Shen and Ming-Tsong Huang, “A Watershed-based Image Segmentation Using JND Property”, IEEE 2003.
[12] Qin Zhonguan, Mou Xuanqin, Wang Ping and Cai Yuanlong, ”an Adaptive Snake Algorithm for Contour Detection”, IEEE ICSP’02 proceedings 2002.
[13] Seok-Woo Jang, Essam A. El-Kwae, and Hyung-Il Choi, “Shaking Snakes Using Color Edge for Contour Extraction”, IEEE ICIP 2002.
[14] Yue Fu, A. Tanju Erdem, Member, IEEE, and A. Murat Tekalp, Senior Member, IEEE Transactions on Image Processing, vol. 9, no. 12, December 2000.
[15] Linda G. Shapiro and George C. Stockman, “Snakes: Active Contour Models”, Computer Vision, pp. 489-492.
[16] L. Vincent and P. Soille, “Watershed in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Machine Intell., vol. 13, pp. 583-598, June 1991.
[17] D. Hagyard, M. Razaz, and P. Atkin, “Analysis of watershed algorithms for grayscale images,” in Proc. Int. Conf. Image Processing, 1996, pp. 41-44.
[18] J.-C. Huang and W.-S. Hsieh, “Wavelet-based moving object segmentation”, IEE, Electronics letters, vol. 39, no. 19, September 2003.