Multichannel bionic vision method for recognizing complex scene image

Abstract

The invention discloses a multichannel bionic vision method for recognizing a complex scene image, and the steps of the method mainly employ three transformation channels. For the first channel, the method comprises the steps: carrying out the forwarding channel calculation of an original image through simulating a multichannel vision processing mechanism of a human brain, obtaining a candidate target set, and generating the memory information at two stages at the same time; for a second channel, the method comprises the steps: sequentially carrying out the reverse channel calculation of all candidate targets through the memory information, and obtaining an impact image in the original image, so as to form a target edge image; for the third channel, finally carrying out the transformation of the target edge image for two times through employing a second forwarding channel algorithm, obtaining a feature transformation image, and carrying out the verification in the candidate set, so as to complete the image recognition. The method extracts the vision memory information through simulating the multichannel vision processing mechanism of the human brain, effectively simulates the multichannel vision processing mechanism of the human brain through the reverse channel calculation, and is high in accuracy of an obtained recognition image.

Description

technical field [0001] The invention belongs to the intersecting field of biological information and machine vision technology, and in particular relates to a multi-channel bionic vision method for complex scene image recognition. Background technique [0002] Image recognition in complex scenes is a difficult and hot issue in the field of computer vision. It is well known that the human visual system can effectively block distracting information and preserve target images in complex scenes. However, using traditional computer vision algorithms to achieve target recognition in complex scene images is a very challenging task. With the continuous revelation of the response mechanism of the human visual cerebral cortex, Hubel reported in Nature that biological visual cortical cells respond very strongly to lines of certain lengths or directions. Inspired by this biological visual response mechanism, if machine vision can extract line features of different lengths and directio...

AI Technical Summary

Problems solved by technology

However, there are two deficiencies. First, the model uses a box filter, which is implemented by using the weighted average value of pixels around the image, which is not consistent with the human visual perception mechanism. Therefore, it is particularly sensitive to noise.

Secondly, this method can only be applied to binary image recognition without background, and a little background interference will greatly reduce the recognition effect

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