Aggregated segmentation of fish from conveyor belt videos

Automation of fishery survey through the aid of visual analysis has received increasing attention. In this paper, a novel algorithm for the aggregated segmentation of fish images taken from conveyor belt videos is proposed. The watershed algorithm driven by an automatic marker generation scheme successfully separates clustered fish images without damaging their boundaries. A target selection based on appearance classification then rejects non-fish objects. By applying histogram backprojection and kernel density estimation, an innovative algorithm for combining object masks of one tracked fish from multiple frames into a refined single one is also proposed. Experimental results show that accurate fish segmentation from conveyor belt videos is achieved.