Target edge extraction method, image segmentation method and system

Abstract

The invention belongs to the technical field of image processing, and particularly relates to a target edge extraction method and an image segmentation method and system. According to the target edge extraction method, an unsupervised rough segmentation method based on edge search is constructed to simulate a visual working memory principle, overall cognition of a scene is carried out, and an accurate edge of a large-area target is obtained. The image segmentation method is based on a target extraction method and a segmentation neural network, visual attention is simulated to carefully observe a scene, and small-area details are obtained. And finally, generating a segmentation result by combining an accurate edge in rough segmentation according to the inference ability shown visually. According to the method, on the basis of simulating visual reasoning to respectively recognize the whole and details of the ground scene, mutual complementation and mutual correction of the whole information and the detail information are carried out, noise removal and error repair are implemented, and thus the image segmentation precision is effectively improved.

Description

Technical field [0001] The present invention is a field of image processing technology, which involves a division method based on visual reasoning combined with unsupervised clustering and deep learning. Background technique [0002] The image segmentation is currently the cornerstone of a variety of applications. It can divide the image category according to the characteristics of the spectrals, textures, and shapes of various targets to obtain the category information in the scene. However, with the continuous improvement of the current image resolution, it not only brings richer and more detailed scenario information, but also leads to high details of the image information, and the spectral spectrum of similar targets may be inconsistent. The increasing differences in the class and the decrease in inter -class variance have brought new challenges to the current image segmentation. [0003] The emergence of current deep learning neural networks provides a very good new idea for...

AI Technical Summary

Problems solved by technology

[0004] However, due to the interference of different imaging lighting conditions and inconsistent target sizes, it is difficult for the current segmentation method to reasonably balance the cognitive granularity when analyzing the scene. It may not be able to protect the accurate edges of large-area targets and easily lose small-area details

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