Brain-like imaging method, device and equipment and storage medium

Abstract

The invention discloses a brain-like imaging method, device and equipment and a storage medium, and the method comprises the steps: processing an obtained image, and extracting instance level information; judging the information category of the instance level information; under the condition that the information category comprises effective information, updating a semantic information base througha brain heuristic neural network according to instance level information corresponding to the effective information; and determining new information in the image according to the instance level information corresponding to the inferenceable information under the condition that the information category comprises the inferenceable information. Through the above processing mode, data redundancy canbe reduced, bandwidth and computing power can be distributed more efficiently, reasoning of new information can be achieved, and therefore multi-object efficient perception and understanding in a large scene are achieved.

Description

technical field [0001] The present application relates to the field of neural networks, and in particular to a method, device, device and storage medium for brain-inspired imaging. Background technique [0002] In large dynamic scenes, the expression and understanding of natural phenomena requires high perceptual resolution. Although machine imaging in the prior art has reached 100 million-level resolution, it only focuses on the perception of pixel sets. The way to improve the perception resolution, the space-time / hardware complexity is high, the amount of data is large, and it needs huge bandwidth and computing power as support. Moreover, the global understanding of the scene based on local perception is only a feed-forward one-way path, and the overall perception and understanding ability of the imaging system is severely restricted by the performance of local perception, which faces the problems of low robustness and poor adaptability. Contents of the invention [000...

AI Technical Summary

Problems solved by technology

[0002] In large dynamic scenes, the expression and understanding of natural phenomena requires high perceptual resolution. Although the machine imaging in the existing technology has reached a resolution of 100 million, it only focuses on the perception of pixel sets. The way to improve the perception resolution, the space-time / hardware complexity is high, the data volume is large, and a huge bandwidth and computing power are required as support

Moreover, the global understanding of the scene based on local perception is only a feed-forward one-way path, and the overall perception and understanding ability of the imaging system is severely restricted by the performance of local perception, facing the problems of low robustness and poor adaptability

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