The invention discloses an
image edge classification detection method based on visual path orientation sensitivity. The present invention utilizes the role of
neuron synaptic connections in the centripetal distribution of the optimal orientation of the
receptive field, constructs a first-level
neuron network sensitive to multiple specific directions, uses image pixels as network input, and records neurons in a certain time window The pulse firing sequence within is calculated, and the
discharge frequency is calculated as the
network output; the
network output in multiple directions is fused and mapped to the
gray level to form an edge-sensitive image; for the edge-sensitive image, the
lateral inhibition range and inhibition amount in the
receptive field are determined , form the second-level
neuron network, and output the image after lateral suppression; finally, after threshold
processing, the
edge detection result is obtained. The invention considers important visual mechanisms such as directional receptive fields and
lateral inhibition, simulates the hierarchical
processing effect of different hierarchical structures on the visual path in contour
perception, and can effectively improve the
edge detection performance of low-contrast images.