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.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0623104-090434 |
| Date | 23 June 2004 |
| Creators | Lai, Yi-Tung |
| Contributors | none, Lih-Shyang Chen, Yau-Hwang Kuo, Wen-Shyong Hsieh |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0623104-090434 |
| Rights | not_available, Copyright information available at source archive |
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