Image weak edge detection method based on multi-layer neuron group firing information

Abstract

The invention relates to a method for detecting weak edges of images on the basis of discharge information of multilayer neuron groups. The method includes constructing the multilayer neuron groups with interconnected inhibitory synapses, inputting digital images into input-layer neuron groups and representing image pixels by the time-space information of first discharge of various neurons; describing space details of the images by time variances by the aid of visual receptive fields and discharge time sequences of the various neurons, selecting attentive mechanisms in the consideration of lateral inhibition so as to acquire visual attention data of information of the images; implementing space variable-resolution mechanisms on the basis of a combination of selective attention procedures by the aid of Log-Gabor multi-direction filter results, acquiring reconstructed information of the edges of the images and reinforcing the information of the edges of the images by the aid of output-layer neuron groups. The method has the advantages that a synapse interconnection characteristic of the neuron groups is taken into consideration; simple visual information procedures of cortices are reflected by the aid of multi-direction filter mechanisms; the weak edges of the images can be effectively detected by the aid of the multilayer neuron groups.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image weak edge detection method based on discharge information of multi-layer neuron groups. Background technique [0002] The image edge refers to the area where the gray value or color of the image changes rapidly. This area contains a large amount of key information of the image, such as outline, texture, light and shade, etc., which is very important for feature extraction, target recognition, and even object perception. The subsequent processing of the image is very important, and irrelevant information can be eliminated through edge detection, thereby greatly reducing the amount of data that needs to be processed and improving the processing speed, so the effective detection of image edges is of great significance. Traditional edge detection methods, such as the Sobel operator based on mathematical differentiation methods, usually do not perform well for weak edges, someti...

AI Technical Summary

Problems solved by technology

Traditional edge detection methods, such as the Sobel operator based on mathematical differentiation methods, usually do not perform well enough for weak edges, sometimes produce over-segmentation, and are also sensitive to noise

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