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A hierarchical classification system for object recognition in underwater environments

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2 Author(s)
Foresti, G.L. ; Dept. of Math. & Comput. Sci., Udine Univ., Italy ; Gentili, S.

In this paper, a hierarchical system, in which each level is composed by a neural-based classifier, is proposed to recognize objects in underwater images. The system has been designed to help an autonomous underwater vehicle in sea-bottom survey operations, like pipeline inspections. The input image is divided into square regions (macro-pixels) and a neural tree is used to classify each region into different object classes (pipeline, sea-bottom, or anodes). Each macro-pixel is then analyzed according to some geometric and environment constraints: macro-pixels with doubt classification are divided into four parts and re-classified. The process is iterated until the desired accuracy is reached. Experimental results, which have been performed on a large set of real underwater images acquired in different sea environments, demonstrate the robustness and the accuracy of the proposed system

Published in:

Oceanic Engineering, IEEE Journal of  (Volume:27 ,  Issue: 1 )