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Caractérisation de textures en imagerie sonar et invariance en rotation

Abstract : This paper addresses the automatic cartography of sea-bottom by means of high resolution sonar images. Many texture analysis methods have been developed since now, based on statistical, geometrical or spectral modelling. Nevertheless, few of them are robust toward image rotations. Indeed, the property of rotation invariance is essential in our framework, particularly for obtaining good classification rates. We present in this article five methods for the automatic classification of rotating images, corresponding to four classes of sea-floor: “sand”, “ridge”, “dune” and “wreck”. The first one is an extension of a circular AutoRegressive method, initially proposed by Kashyap et Khotanzad [19], which allows to estimate a reduced number of rotation invariant parameters. The four other methods are based on an original approach, consisting to apply a mathematical transform to a set of parameters describing texture features. Two of them consist in computing the Log- Polar transform to autoregressive (AR) or correlation (COR) parameters. The two others consist in estimating the Zernike moments of autoregressive (AR) or correlation (COR) parameters. Classification rates obtained on sonar images and on Brodatz album are presented and allow to compare the performances of each approach.
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https://hal.univ-brest.fr/hal-03222603
Contributor : Gilles Burel Connect in order to contact the contributor
Submitted on : Monday, May 10, 2021 - 1:13:48 PM
Last modification on : Thursday, May 13, 2021 - 8:43:20 PM

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  • HAL Id : hal-03222603, version 1

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Helene Thomas, Christophe Collet, Koffi Clément Yao, Gilles Burel. Caractérisation de textures en imagerie sonar et invariance en rotation. Traitement du Signal, Lavoisier, 2000, 17 (1), pp.1-19. ⟨hal-03222603⟩

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