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Self-Organizing Maps Approach to Object Localization in Sonar Imagery

Abstract : The Self-Organizing Map is well-known as the unsupervised classification method. It is employed as classifier in various applications such as image segmentation. The main purpose of this paper is to identify and detect an object of interest on side scan sonar image. This work is performed by two steps. The first one is to split an image into regions of uniform texture using the Gray Level Co-occurrence Matrix Method (GLCM) which is widely used in texture segmentation application. The last one address the unsupervised learning method based on the Artificial Neural Networks (Self-Organizing Map or SOM) used for determining the comparative model of object of interest from an image. To increase the performance of SOM, we propose a penalty function based on data histogram visualization. After a brief review of both techniques (GLCM and SOM), we present our method and some results from several experiments on the real world data set.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00191933
Contributor : Christine Carvalho de Matos <>
Submitted on : Monday, November 26, 2007 - 11:42:13 AM
Last modification on : Thursday, May 24, 2018 - 3:59:23 PM
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  • HAL Id : lirmm-00191933, version 1

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Amornrit Puttipipatkajorn, Bruno Jouvencel. Self-Organizing Maps Approach to Object Localization in Sonar Imagery. ICAR: International Conference on Advanced Robotics, 2003, Coimbra, Portugal. pp.1172-1177. ⟨lirmm-00191933⟩

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