Reconstruction and segmentation of underwater acoustic images combining confidence information in MRF models

摘要

This paper describes a technique for the integration of confidence information using Markov random fields’ models to improve the segmentation and reconstruction of three-dimensional acoustical images. A range image in which each point is associated with a reliability measure, namely, its “confidence”, is directly provided by acoustic image formation process. In this paper, range and confidence images are modelled as Markov random fields over which several energy formulations are devised to exploit both types of data, leading to an accurate reconstruction and segmentation of such images. Results show the better performances of the proposed approach as compared with classical methods disregarding reliability information.