Improved Color Opponent Contour Detection Model Based on Dark and Light Adaptation

Abstract

Brightness and color are two basic visual features of the human visual system. In the retina, color-sensitive cells are sensitive to brightness and color information and exhibit salient direction selectivity. However, evidence from neurobiology indicates that apart from color features, the rod and cone cells of the retina are also sensitive to high or low luminance, respectively termed the light and dark adaptation mechanisms. Classical single-opponent and color-opponent contour detection model frameworks include the computational processes of single red (R), green (G), blue (B) and yellow (Y) channels and opponent R–GB–Y channels, respectively. Thus, to combine luminance cues and traditional color cues to improve boundary detection in natural scenes, we propose the use of a dark and a light channel to simulate light and dark adaptation mechanisms. The results of the proposed model considering three datasets (BSDS300, BSDS500, NYUD) demonstrate an improvement compared with current bio-inspired contour detection models.