Tarek Zlitni

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In this paper, we propose a spatial temporal video-text detection technique which proceed in two principal steps: potential text region detection and a filtering process. In the first step we divide dynamically each pair of consecutive video frames into sub block in order to detect change. A significant difference between homologous blocks implies the(More)
In this paper, we propose a new approach to identify programs in TV streams. In the first step of our approach, we construct a reference catalogue for video grammars of visual jingles. In the second step, we identify programs in TV streams by examining the similarity of the video signal to the visual grammars in the catalogue. After presenting our approach,(More)
TV streams represent a principal source of multimedia information. The goal of the proposed approach is to enable a better exploitation of this source of video by multimedia services (i.e., TV-On-Demand, catch-up TV), social community, and video-sharing platforms (Vimeo, Youtube, Facebook …). In this work, we present an automatic structuring approach of TV(More)
The emergence of the TV channels number and the appearance of the Internet led to an exponential growth of the audiovisual documents. Several works were proposed in the literature for automatic video segmentation. However, the effectiveness of these works depends on video type. So, for a better quality of the segmentation, it is necessary to consider a(More)
The amount of digital video data is increasing over the world. It highlights the need for efficient algorithms that can index, retrieve and browse this data by content. This can be achieved by identifying semantic description captured automatically from video structure. Among these descriptions, text within video is considered as rich features that enable a(More)
Text in videos contains much semantic information that can be used for video indexing and browsing. In this paper, we propose a spatiotemporal video-text localization and identification approach which proceeds in two main steps: text region localization and text region identification. In the first step we detect the significant appearance of the new objects(More)
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