Video Segementation Based On 2d Image Analysis

dc.creatorGuimaraes S.J.F.
dc.creatorCouprie M.Michel
dc.creatorde Albuquerque Araujo A.
dc.creatorLeite N.J.
dc.date2003
dc.date2015-06-30T17:28:29Z
dc.date2015-11-26T15:00:24Z
dc.date2015-06-30T17:28:29Z
dc.date2015-11-26T15:00:24Z
dc.date.accessioned2018-03-28T22:11:42Z
dc.date.available2018-03-28T22:11:42Z
dc.identifier
dc.identifierPattern Recognition Letters. , v. 24, n. 7, p. 947 - 957, 2003.
dc.identifier1678655
dc.identifier10.1016/S0167-8655(02)00218-0
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-0346036003&partnerID=40&md5=618f881a6a003f26438863b3a8f4a423
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/102160
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/102160
dc.identifier2-s2.0-0346036003
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1256158
dc.descriptionThe video segmentation problem consists in the identification of the boundary between consecutive shots. The common approach to solve this problem is based on dissimilarity measures between frames. In this work, the video segmentation problem is transformed into a problem of pattern detection, where each video event is transformed into a different pattern on a 2D image, called visual rhythm, obtained by a specific transformation. In our analysis we use topological and morphological tools to detect cuts. Also, we use discrete line analysis and max tree analysis to detect fade transitions and flashes, respectively. We present a comparative analysis of our method for cut detection with respect to some other methods, which shows the better results of our method. © 2002 Elsevier Science B.V. All rights reserved.
dc.description24
dc.description7
dc.description947
dc.description957
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dc.languageen
dc.publisher
dc.relationPattern Recognition Letters
dc.rightsfechado
dc.sourceScopus
dc.titleVideo Segementation Based On 2d Image Analysis
dc.typeArtículos de revistas


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