Bionic visual image target recognition method fusing dot-line memory information

Abstract

The invention discloses a bionic visual image target recognition method fusing dot-line memory information, and the method comprises the steps of constructing a grid cell set based on visual drive, constructing a distance cell model, and calculating a displacement vector between the positions coded by a grid cell group vector; calculating the response of all sensory neurons to each central concavepixel k through a Gaussian kernel, wherein the response is used for target recognition; calculating the fovea centralis of the current target image by using Gaussian nuclear sensory cells, taking thefeature tag unit with the strongest response as the next jump point, and accumulating the corresponding stimulated identity cells; selecting a next-hop viewpoint, and updating a foveal displacement vector through a distance cell model; and circularly repeating the calculation of the current position during the target identification process, selecting the next-hop viewpoint, and carrying out the vector calculation until the accumulation of a certain stimulated identity cell reaches a threshold value 0.9, and considering the stimulated identity as the finally identified target. The method provided by the invention has a relatively higher recognition rate for the position change, zooming and shielded images.

Description

technical field [0001] The invention relates to the fields of visual perception, recognition memory and biological information, in particular to a grid cell-based scaling and recognition method for occluded targets. Background technique [0002] Scaled and occluded target recognition is a hot issue in the current visual field. When the target image is scaled and blocked by obstacles, traditional machine learning methods are difficult to recognize the target image. For traditional object recognition models that focus on massively parallel processing of low-level features, high-level representations emerge in the post-processing stage. However, visual perception is closely related to eye movement. Human visual perception and eye movement can quickly and continuously scan the current target image. No matter how the target image is scaled or occluded, the human visual perception system can effectively convert the zoomed and occluded image to the target image. The target image i...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a bionic visual image target recognition method that integrates point and line memory information to overcome the problem of low recognition rate of occluded targets caused by traditional methods. cells, recognition memory and vector navigation to improve the recognition rate of occluded target images

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