Contrast-dependent surround suppression models for contour detection. (December 2016) - Tang, Qiling; Sang, Nong; Liu, Haihua
Abstract: The goal of this work is to present a computational model for contour detection, based on the surround suppression mechanisms of the primary visual cortex, in which the strength of surround suppression can adaptively vary with contrast—the surround modulation tends to be clearly suppressive at high contrast and less suppressive at low contrast, which may help to achieve the tradeoff between reducing cluttered edges and retaining object structures with weak responses, thus improving performance of contour detection under different circumstances; on the other hand, for similar texels can engender stronger suppression effects, more than orientation feature, texton is introduced to homogeneity measurement, which can well character multiple properties of texture regions, thereby yielding better contour detection results than other suppression models. The study can provide useful suggestions for contour detection algorithm in computer vision, but may also contribute to understanding contrast influence and feature representation in non-classical receptive field inhibition. Highlights: The magnitude of surround suppression is contrast-dependent. Tradeoff is achieved between reducing cluttered edges and preserving structures. Combining various cues measures homogeneity, yielding better inhibitory effects. This helps to understand contrast influence and feature representation in non-CRF.