Learning and surface boundary feedbacks for colour natural scene perception

Abstract

Boundary detection and segmentation are essential stages in object recognition and scene understanding. In this paper, we present a bio-inspired neural model of the ventral pathway for colour contour and surface perception, called LPREEN (Learning and Perceptual boundaRy rEcurrent dEtection Neural architecture). LPREEN models colour opponent processes and feedback interactions between cortical areas V1, V2, V4, and IT, which produce top-down and bottom-up information fusion. We suggest three feedback interactions that enhance and complete boundaries. Our proposed neural model contains a contour learning feedback that enhances the most probable contour positions in V1 according to a previous experience, and generates a surface perception in V4 through diffusion processes. We compared the proposed model with another bio-inspired model and two well-known contour extraction methods, using the Berkeley Segmentation Benchmark. LPREEN showed better performance than two methods and slightly worse performance than another one.