FAST TARGET EXTRACTION BASED ON BAYESIAN BLOB ANALYSIS AND SIMULATED ANNEALING FOR UNDERWATER IMAGES

Wei Zhang, Ximeng Wang, Tao Chen, Lifeng Gao, Xixun Sun, and Hongliang Ren

Keywords

Segmentation, annealing algorithm, Blob analysis, Bayesian designmaking, mathematical morphology

Abstract

As there are great absorption and scattering in water, it is difficult to extract the target region from an underwater image effectively. This paper investigated a Bayesian decision-making framework for segmenting underwater images, with the improved OTSU algorithm combined with the simulated annealing algorithm calculating the optimum threshold. The improved OTSU algorithm took fully into account grey values of pixels and their neighbours to have a better ability of filtering noise. The simulated annealing algorithm was contributed to reduce the amount of calculation and improved the efficiency of calculating the optimum threshold. Blob operators were used to exclude fake target regions based on Bayesian decisionmaking. The mathematical morphology operators were used to eliminate burrs and disturbances. The result of processing the images grabbed at pool experiments proved the better capability of segmentation with the proposed method.

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