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dc.contributor.authorKuo, I-Shengen_US
dc.contributor.authorChen, Ling-Hweien_US
dc.date.accessioned2014-12-08T15:15:21Z-
dc.date.available2014-12-08T15:15:21Z-
dc.date.issued2006-12-01en_US
dc.identifier.issn0218-0014en_US
dc.identifier.urihttp://dx.doi.org/10.1142/S0218001406005162en_US
dc.identifier.urihttp://hdl.handle.net/11536/11513-
dc.description.abstractThe sprite generator introduced in MPEG-4 blends frames by averaging, which will make places, that are always occupied by moving objects, look blurred. Thus, providing segmented masks for moving objects is suggested. Several researchers have employed automatic segmentation methods to produce moving object masks. Based on these masks, they used a reliability-based blending strategy to generate sprites. Since perfect segmentation is impossible, some ghost-like shadows will appear in the generated sprite. To treat this problem, in this paper, an intelligent blending strategy without needing segmentation masks is proposed. It is based on the fact that for each point in the generated sprite, the corresponding pixels in most frames belong to background and only few belong to moving objects. A counting schema is provided to make only background points participate in average blending. The experimental result shows that the visual quality of the generated sprite using the proposed blending strategy is close to that using manually segmented masks and is better than that generated by Lu-Gao-Wu method. No ghostlike shadows are produced. Furthermore, a uniform feature point extraction method is proposed to increase the precision of global motion estimation, the effectiveness of this part is presented by showing the comparison results with other existing method.en_US
dc.language.isoen_USen_US
dc.subjectsprite generationen_US
dc.subjectbackground mosaicen_US
dc.subjectfeature point extractionen_US
dc.subjectglobal motion estimationen_US
dc.subjectblendingen_US
dc.subjectMPEG-4en_US
dc.titleA high visual quality sprite generator using intelligent blending without segmentation masksen_US
dc.typeArticleen_US
dc.identifier.doi10.1142/S0218001406005162en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCEen_US
dc.citation.volume20en_US
dc.citation.issue8en_US
dc.citation.spage1139en_US
dc.citation.epage1158en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000243988600002-
dc.citation.woscount1-
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