A grayscale image recognition method, device, equipment and readable storage medium

Abstract

The embodiment of the invention discloses a grayscale image recognition method, device, equipment and computer-readable storage medium. Among them, the method includes performing network modeling on the contour pixel points of the extracted grayscale image to be recognized based on the complex network and the watershed algorithm; applying each distance threshold value in the distance threshold value set to the node set of the initial network model, and establishing corresponding The sub-network model; calculate the topological parameters of each sub-network model to form the target recognition parameters of the grayscale image to be recognized; calculate the strength of each node in the initial network model, and select the nodes that meet the preset strength conditions from the strength of each node , forming a target network interest point set; according to the target recognition parameters and the target network interest point set, match the candidate sample images that meet the preset parameter conditions and have the same network interest points in the sample library, and the category of the candidate sample images is gray to be identified category of the degree image. The application improves the recognition efficiency of grayscale images.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and in particular, to a grayscale image recognition method, device, equipment, and computer-readable storage medium. Background technique [0002] With the rapid development of computer technology, image processing technology and artificial intelligence technology have also been developed rapidly, resulting in a large amount of text, image, audio and video data information. Among the information perceived by humans, visual information accounts for the vast majority, and images are the main means for vision to obtain information from the outside world. [0003] Image information contains very rich content, which is intuitive, specific, and efficient. It is used in remote sensing image recognition, biomedical image recognition, image transmission in the communication field, dynamic object recognition, handwriting recognition, video conference and machine vision. vari...

AI Technical Summary

Problems solved by technology

[0005] However, the existing image recognition methods based on complex networks have a large amount of modeling and many characteristic parameters, resulting in slow running speed, long time-consuming and low recognition efficiency of the entire image recognition process.

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