Contour detection based on multilevel inhibition
多水平外区抑制的轮廓检测

Detecting object contours from natural images plays an important role in machine vision.However,because of the texture edges existing in natural images,it becomes very hard to implement.Relevant research on orientation selective neurons in the primary visual cortex shows,that a mechanism,called non-classical receptive field,can inhibit texture edges and facilitate isolated edges when the visual system processes natural images.Many biologically motivated models have been proposed for contour detection,but they share a common problem which is that some contour elements will be lost if the value of inhibition level is set to high, while some texture edges will be retained if it is set to low.In order to solve this problem,we present a new model, which combines the information from different inhibition levels.It effectively suppresses texture edges and reduces the possibility of losing contour elements.Experimental results show that in comparison with the traditional algorithms,the new algorithm increases performance about ten percent and is more robust.