Application of the center–surround mechanism to contour detection

Physiological studies have revealed that the center–surround mechanism widely exists in the primary stages of the human visual system, such as the retina, lateral geniculate nucleus (LGN), and primary visual cortex (V1). In retina ganglion cells (RGC) and the LGN, the mechanism is well known to have two types: center “on” and center “off.” However, this mechanism in V1 is shown as classical receptive field (CRF) stimulation and surrounding non-CRF suppression. Although these two manifestations differ in function and appear in different areas of the visual pathway, from the perspective of computational simulation, they simply compute the differences between the center and its surrounding information. In the past decade, many bio-inspired computational models have demonstrated that the center–surround mechanism is good at extracting salient contours while suppressing textures. Based on this mechanism, we propose a method for extracting local center–surround contrast information from nature images by using a normalized difference of Gaussian (DoG) function and a sigmoid activated function. Compared with previous contour detection models (especially bio-motivated ones), the proposed method can efficiently suppress textures more quickly and accurately. More importantly, the proposed algorithm yields even better contour detection, yet the computational complexity is similar to the classical Canny operator.