Contour Detection Method Based On Biological Visual Perception

Contours convey the key information of the target's shape and morphological characteristics. Contour detection has long been a fundamental problem in computer vision. In some natural images, contours of the target are imbedded in cluttered texture edges, which are difficult to distinguish. How to eliminate the texture information effectively while complete extracting the target's contour, is an important problem. Human visual perceptual system can solve such situation perfectly. Exploring the human visual perception mechanism, establishing neural information processing model, is currently a hot research.Our study aimed to establish the calculation model of shape channel in human visual system, simulate the ability of accessing invariant features. Firstly, summarize the recent neurophysiology researching results about visual cortex structure and function characteristics, analyse some typical bionic contour detection algorithm. Secondly, propose an effective method based on human visual perception mechanism, which combines the properties of primary visual cortex and psychology researching results to simulate the contour perception of the V1 cortex. Include model single cell response, then, establish the mapping relationship between stimulus and neurons in primary cortex, get an effective balance between suppression and enhancement by dynamically modulating the contextual interactions. Finally, compared with traditional methods, experiments show that this algorithm implement a more reasonable simulation of the V1 function structure,availably enhance the target's contour while suppress the cluttered background, obtain a balance between over and lose detection, besides, it has better accuracy with less computational complexity and time-consuming.