| Paper: | IMDSP-P10.13 | ||
| Session: | Image Analysis | ||
| Time: | Thursday, May 20, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Image and Multidimensional Signal Processing: Image and Video Analysis | ||
| Title: | TEXTURE ANALYSIS USING PROBABILISTIC MODELS OF THE UNIMODAL AND MULTIMODAL STATISTICS OF ADAPTIVE WAVELET PACKET COEFFICIENTS | ||
| Authors: | Roberto Cossu; INRIA | ||
| Ian Jermyn; INRIA | |||
| Josiane Zerubia; INRIA | |||
| Abstract: | In recent work, it was noted that although the subband histograms for standard wavelet coefficients take on a generalized Gaussian form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, generalized Gaussian, and interestingly, in some subbands, bi- and tri-modal histograms. These subbands are closely linked to the structure of the texture. Motivated by these observations, we extend the approach to texture analysis proposed in [1] to model these subbands. We relax the Gaussian assumption to include generalized Gaussians and constrained Gaussian mixtures. We use a Bayesian methodology, finding MAP estimates for the adaptive basis, for subband model selection, and for subband model parameters. Results confirm the effectiveness of the proposed approach, and highlight the importance of multimodal subbands for texture discrimination and modelling. | ||
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