Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00530-005-0167-6
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dc.titleAutomatic video logo detection and removal
dc.contributor.authorYan, W.-Q.
dc.contributor.authorWang, J.
dc.contributor.authorKankanhalli, M.S.
dc.date.accessioned2013-07-04T07:43:19Z
dc.date.available2013-07-04T07:43:19Z
dc.date.issued2005
dc.identifier.citationYan, W.-Q., Wang, J., Kankanhalli, M.S. (2005). Automatic video logo detection and removal. Multimedia Systems 10 (5) : 379-391. ScholarBank@NUS Repository. https://doi.org/10.1007/s00530-005-0167-6
dc.identifier.issn09424962
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39513
dc.description.abstractMost commercial television channels use video logos, which can be considered a form of visible watermark, as a declaration of intellectual property ownership. They are also used as a symbol of authorization to rebroadcast when original logos are used in conjunction with newer logos. An unfortunate side effect of such logos is the concomitant decrease in viewing pleasure. In this paper, we use the temporal correlation of video frames to detect and remove video logos. In the video-logo-detection part, as an initial step, the logo boundary box is first located by using a distance threshold of video frames and is further refined by employing a comparison of edge lengths. Second, our proposed Bayesian classifier framework locates fragments of logos called logo-lets. In this framework, we systematically integrate the prior knowledge about the location of the video logos and their intrinsic local features to achieve a robust detection result. In our logo-removal part, after the logo region is marked, a matching technique is used to find the best replacement patch for the marked region within that video shot. This technique is found to be useful for small logos. Furthermore, we extend the image inpainting technique to videos. Unlike the use of 2D gradients in the image inpainting technique, we inpaint the logo region of video frames by using 3D gradients exploiting the temporal correlations in video. The advantage of this algorithm is that the inpainted regions are consistent with the surrounding texture and hence the result is perceptually pleasing. We present the results of our implementation and demonstrate the utility of our method for logo removal. © Springer-Verlag 2005.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s00530-005-0167-6
dc.sourceScopus
dc.subjectNeural network
dc.subjectVideo inpainting
dc.subjectVideo logo detection
dc.subjectVideo logo removal
dc.subjectVisual watermark attack
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/s00530-005-0167-6
dc.description.sourcetitleMultimedia Systems
dc.description.volume10
dc.description.issue5
dc.description.page379-391
dc.description.codenMUSYE
dc.identifier.isiut000232276500003
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