Efficient summarization of stereoscopic video sequences

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dc.contributor.author Doulamis, ND en
dc.contributor.author Doulamis, AD en
dc.contributor.author Avrithis, YS en
dc.contributor.author Ntalianis, KS en
dc.contributor.author Kollias, SD en
dc.date.accessioned 2014-03-01T01:15:34Z
dc.date.available 2014-03-01T01:15:34Z
dc.date.issued 2000 en
dc.identifier.issn 1051-8215 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/13606
dc.subject content-based indexing and retrieval en
dc.subject stereoscopic image analysis en
dc.subject video summarization en
dc.subject.classification Engineering, Electrical & Electronic en
dc.subject.other IMAGE SEQUENCES en
dc.subject.other SEGMENTATION en
dc.subject.other TRACKING en
dc.subject.other OBJECTS en
dc.subject.other MODEL en
dc.subject.other REPRESENTATION en
dc.subject.other GENERATION en
dc.subject.other MESH en
dc.title Efficient summarization of stereoscopic video sequences en
heal.type journalArticle en
heal.identifier.primary 10.1109/76.844996 en
heal.identifier.secondary http://dx.doi.org/10.1109/76.844996 en
heal.language English en
heal.publicationDate 2000 en
heal.abstract An efficient technique for summarization of stereoscopic video sequences is presented in this paper, which extracts a small but meaningful set of video frames using a content-based sampling algorithm. The proposed video-content representation provides the capability of browsing digital stereoscopic video sequences and performing more efficient content-based queries and indexing. Each stereoscopic video sequence is first partitioned into shots by applying a shot-cut detection algorithm so that frames (or stereo pairs) of similar visual characteristics are gathered together. Each shot is then analyzed using stereo-imaging techniques, and the disparity field, occluded areas, and depth map are estimated. A multiresolution implementation of the Recursive Shortest Spanning Tree (RSST) algorithm is applied for color and depth segmentation, while fusion of color and depth segments is employed for reliable video object extraction. In particular, color segments are projected onto depth segments so that video objects on the same depth plane are retained, while at the same time accurate object boundaries are extracted. Feature vectors are then constructed using multidimensional fuzzy classification of segment features including size, location, color, and depth. Shot selection is accomplished by clustering similar shots based on the generalized Lloyd-Max algorithm, while for a given shot, key frames are extracted using an optimization method for locating frames of minimally correlated feature vectors. For efficient implementation of the latter method, a genetic algorithm is used. Experimental results are presented, which indicate the reliable performance of the proposed scheme on real-life stereoscopic video sequences. en
dc.identifier.doi 10.1109/76.844996 en
dc.identifier.isi ISI:000087613700001 en
dc.identifier.volume 10 en
dc.identifier.issue 4 en
dc.identifier.spage 501 en
dc.identifier.epage 517 en

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