A Classified Image Edge Detection Method Based on Visual Path Orientation Sensitivity

Abstract

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.

Description

technical field [0001] The invention belongs to the field of visual neural computing, and relates to an image edge classification detection method based on visual path orientation sensitivity. Background technique [0002] Contour feature extraction will provide important dimensionality reduction information for image understanding or moving target behavior analysis. The extraction process usually needs to meet: (1) under the premise of accurately locating edges, no missed detection occurs; (2) avoid false edges. However, in practical applications, due to the influence of unfavorable factors such as lighting and shadows, the contrast of image edges is reduced, and it is difficult for traditional detection methods to meet the above two requirements at the same time; and the current edge detection method based on the visual neural mechanism simplifies the process of real neurons. The electrophysiological characteristics in signal processing ignore the hierarchical processing e...

AI Technical Summary

Problems solved by technology

However, in practical applications, due to the influence of unfavorable factors such as lighting and shadows, the contrast of image edges is reduced, and it is difficult for traditional detection methods to meet the above two requirements at the same time; and the current edge detection method based on the visual neural mechanism simplifies the process of real neurons. The electrophysiological characteristics in signal processing ignore the hierarchical processing effect of different hierarchical structures on the visual pathway in contour perception, and essentially use a black-box mathematical model to simulate the visual mechanism

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