Nonlinear approximation of spatiotemporal data using diffusion wavelets

Authors:
Marie Wild
Affiliations:
Austrian Research Centers GmbH, Video and Safety Technology, Tech Gate Vienna, Vienna, Austria
Venue:
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Year:
2007

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Abstract

We present a multiscale, graph-based approach to 3D image analysis using diffusion wavelet bases, which were presented in [1]. Diffusion wavelets allow to obtain orthonormal bases of L2 functions on graphs. This permits the study of classical wavelet algorithms (such as compression and denoising of functions in L2(Rn), n ∈ N, via nonlinear approximation) in this setting. In this paper, we describe howthis could be used in structure-preserving compression of image sequences, modelled as a whole as a weighted graph, as a first step towards structural spatiotemporal wavelet segmentation. We further discuss the possibilities for using this abstract approach in computer vision tasks.