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MAXIMUM LIKELIHOOD TEXTURE CLASSIFICATION AND BAYESIAN TEXTURE SEGMENTATION USING DISCRETE WAVELET FRAMES
| Citations: | 3 - 0 self |
BibTeX
@MISC{Liapis_maximumlikelihood,
author = {S. Liapis and N. Alvertos and G. Tziritas},
title = {MAXIMUM LIKELIHOOD TEXTURE CLASSIFICATION AND BAYESIAN TEXTURE SEGMENTATION USING DISCRETE WAVELET FRAMES},
year = {}
}
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Abstract
Abstract: In this work a new approach is presented for the classification and segmentation of texture images, where a different statistical methodology and criterion for texture characterization is proposed. The scheme, in both problems, uses the concept of Discrete Wavelet Frames for the appropriate frequency decompositions, as applied to 2-D signals, and a distance measure based on the evaluation of parametric scatter matrices of the texture images to be segmented or classified. Experiments yielding excellent results are presented for both algorithms. 1 Introduction and problem definition In computer vision tasks, including multimedia applications (e.g., [8]), often texture information must be classified and segmented for recognition purposes. Several statistical approaches have been proposed in the past for
Keyphrases
distance measure discrete wavelet frame several statistical approach 2-d signal texture information different statistical methodology recognition purpose problem definition texture image computer vision task new approach multimedia application appropriate frequency decomposition texture characterization excellent result parametric scatter matrix
