A new method of pipeline detection in sonar imagery using self-organizing maps

The main purpose of this paper is to detect and follow the pipeline in sonar image. This work is performed by two steps. The first one is to split an transformed line image of pipeline signal 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 addresses the unsupervised learning method based on the Artificial Neural Networks (Self-Organizing Map or SOM) used for determining the comparative model of pipeline from the image. To increase the performance of SOM, we propose a penalty function based on data histogram visualization for detecting the position of pipeline. 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.