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.