Image edge detection method based on Spiking-convolution network model

Abstract

The invention discloses an image edge detection method based on a Spiking-convolution network model, which belongs to the technical field of image processing and solves the problem that a method in the prior art only simulates the spatial hierarchical structure of a biological nervous system, but lacks interpretation to time characteristics. The image edge detection method disclosed by the invention comprises the following steps: creating the Spiking-convolution network model of a convolution structure having an input layer, a Spiking-convolution layer and an output layer based on an information processing connection manner of a visual hierarchical structure; using a Laplace-Gauss operator and a Gauss difference operator as a filter of the Spiking-convolution layer of the created Spiking-convolution network model of the convolution structure to form a Spiking-convolution algorithm based on the operators; obtaining an image, encoding gray value pixels of the image into Spiking neurons to serve as the input layer of the Spiking-convolution network model; applying the Spiking-convolution algorithm based on the operators to the Spiking-convolution network model, carrying out pulse convolution on the input layer, and then recreating and outputting the edge of the image according to a Spiking threshold ignition model. The image edge detection method disclosed by the invention is applied to image pre-processing, characteristic extraction and edge detection and relates to neural networks, machine learning and Deep Learning.

Description

technical field [0001] An image edge detection method based on the Spiking‐convolutional network model, which is applied to image preprocessing, feature extraction, and edge detection, involves neural networks, machine learning, and Deep Learning, and belongs to the technical fields of image processing and the like. Background technique [0002] In practical image processing problems, image edge maps, as a basic feature of images, are often applied to image processing and analysis techniques such as higher-level feature description, image recognition, image segmentation, image enhancement, and image compression. , so that the image can be further analyzed and understood. It has a wide range of applications in the fields of image recognition, image segmentation, image enhancement and image compression, and is also their basis. [0003] Image edge is one of the most basic features of an image, often carrying most of the information of an image. The edge exists in the irregul...

AI Technical Summary

Problems solved by technology

However, since the convolutional neural network only simulates the spatial hierarchy of the biological nervous system, it lacks the interpretation of time characteristics, and it still uses a discretized method for processing, so the timing analysis problem has not been fundamentally solved, and there is still a huge room for improvement.

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