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Sparse representation of video data by adaptive tetrahedralizations.
In: Locally adaptive filters in image and signal processing. Berlin [u.a.]: Springer, 2010. 197-220
Natural videos are composed of a superposition of moving objects, usually resulting from anisotropic motions into different directions. By discretization with respect to time, a video may be regarded as a sequence of consecutive natural still images. Alternatively, when considering time as one dimension, a video may be viewed as a 3d scalar field. In this case, customized methods are needed for capturing both the evolution of moving contours along the time axis and the geometrical distortions of the resulting sweep surfaces. Moreover, it is desirable to work with sparse representations. Indeed, already for basic motions (e.g. rotations, translations), customized methods for the construction of well-adapted sparse video data representations are required. To this end, we propose a novel adaptive approximation algorithm for video data. The utilized nonlinear approximation scheme is based on anisotropic tetrahedralizations of the 3d video domain, whose tetrahedra are adapted locally in space (for contour-like singularities) and locally in time (for anisotropic motions). The key ingredients of our approximation method, 3AT, are adaptive thinning, a recursive pixel removal scheme, and least squares approximation by linear splines over anisotropic tetrahedralizations. The approximation algorithm 3AT yields a new concept for the compression of video data. We apply the proposed approximation method first to prototypical geometrical motions, before numerical simulations concerning one natural video are presented.
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Publikationstyp Artikel: Sammelbandbeitrag/Buchkapitel
Bandtitel Locally adaptive filters in image and signal processing
Quellenangaben Seiten: 197-220
Verlagsort Berlin [u.a.]
Institut(e) Institute of Biomathematics and Biometry (IBB)