Nowadays, a paradigm shift is under way in the world of Digital Broadcast TV. This change, similar to that of the mobile market, promises a future where modern TV sets and settop boxes will become the merging point of TV and computers. The 'Connected TV' will allow users to access content available either from the broadcast channels or the Internet. There are already some independent attempts in realizing this concept as well as standardization efforts that aim at closing the gap between different implementations. However, more focus is placed on the integration of Internet content while the traditional broadcast part is neglected. In this work, we outline the current status and propose a framework for creating a true hybrid solution, where end users can enrich broadcast content with Internet-based enhancements so that they can enjoy improved and personalized viewing experience. In fact, this approach allows for content composition, by merging the base (broadcast) content with addon (Internet) content. This will further facilitate the opening of the TV market, the emergence of new business models and the offering of more advanced and personalized services.
A novel method for summarizing videos of gestures is presented. The gestures performed by the hands and the head are extracted through skin color segmentation and represented through Zernike moments. The gesture energy is calculated using the norms of the Zernike moments and monitored through time for local minima and maxima that indicate distinctive visual events and thus key-frames. The proposed scheme is not thresholddependent and therefore the number of extracted key-frames varies according to the complexity of gesture energy variation. The applicability of the method is verified experimentally in sign language videos.
In this paper a fully automatic scheme for embedding visually recognizable watermark patterns to video objects is proposed. The architecture consists of 3 main modules. During the first module unsupervised video object extraction is performed, by analyzing stereoscopic pairs of frames. In the second module each video object is decomposed into three levels with ten subbands, using the Shape Adaptive Discrete Wavelet Transform (SA-DWT) and three pairs of subbands are formed (HL 3 , HL 2 ), (LH 3 , LH 2 ) and (HH 3 , HH 2 ). Next Qualified Significant Wavelet Trees (QSWTs) are estimated for the specific pair of subbands with the highest energy content. QSWTs are derived from the Embedded Zerotree Wavelet (EZW) algorithm and they are high-energy paths of wavelet coefficients. Finally during the third module, visually recognizable watermark patterns are redundantly embedded to the coefficients of the highest energy QSWTs and the inverse SA-DWT is applied to provide the watermarked video object. Performance of the proposed video object watermarking system is tested under various signal distortions such as JPEG lossy compression, sharpening, blurring and adding different types of noise. Furthermore the case of transmission losses for the watermarked video objects is also investigated. Experimental results on real life video objects indicate the efficiency and robustness of the proposed scheme.
In this paper two efficient unsupervised video object segmentation approaches are proposed and thoroughly compared in terms of computational cost and quality of segmentation results. Both methods are based on the exploitation of depth information, estimated for stereoscopic pairs of frames. In particular, in both schemes an occlusion compensated disparity field is initially computed and a depth map is generated.
Then a depth segments map is produced by incorporating a modified version of the multiresolution Recursive Shortest Spanning Tree segmentation algorithm (M-RSST). Next considering the first "Constrained Fusion
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.