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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Cossu, R. Jermyn, I.H. Zerubia, J. |
| Copyright Year | 2004 |
| Description | Author affiliation: INRIA, France (Cossu, R.; Jermyn, I.H.; Zerubia, J.) |
| Abstract | Although subband histograms of the wavelet coefficients of natural images possess a characteristic leptokurtotic form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, leptokurtotic, and interestingly, in some subbands, multimodal histograms. These subbands are closely linked to the structure of the texture, and guarantee that the most probable image is not flat. Motivated by these observations, we propose a probabilistic model that takes them into account. Adaptive wavelet packet subbands are modelled as Gaussian, generalized Gaussian, or a constrained Gaussian mixture. 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. |
| File Size | 324365 |
| File Format | |
| ISBN | 0780384849 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2004.1326615 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-05-17 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Statistical analysis Wavelet analysis Wavelet packets Histograms Bayesian methods Statistics Frequency Wavelet coefficients Image processing Image analysis |
| Content Type | Text |
| Resource Type | Article |
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