Target detection method based on sparse representation and visual cortex attention mechanism

Abstract

The invention discloses a target detection method based on a sparse representation and visual cortex attention mechanism. The target detection method is realized by the following steps of: (1) utilizing a multi-scale and multi-directional Gabor filter group to carry out Gabor filtering on an input image to be detected and extracting to obtain S1 layer cells of an HMAX model; (2) utilizing independent component analysis (ICA) and utilizing C1 layer cells of the HMAX model to construct a sparse group; and (3) utilizing C2 layer cells which have invariance on scales and translation, and a Shannon entropy theory to obtain information entropy of the C2 layer cells, and furthermore, constructing a measure for detecting a visual significance region to obtain an obvious image of a previous image. According to the target detection method disclosed by the invention, the perception property of human eyes in finding the significance of a natural scene image can be better satisfied, so that the accurate rate is higher and the false detection rate is lower; and compared with the traditional image detection method, the target detection method has the advantages that the effectiveness and the accuracy in a psychological stimulation mode image are better.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a target detection method based on a sparse representation and a visual cortex attention mechanism in the technical field of natural scene image processing. The invention can be used for road sign detection, video surveillance, eye movement prediction and object recognition. Background technique [0002] Object detection is one of the key technologies in computer vision and pattern recognition systems. The effect of object detection directly affects the reliability and effectiveness of the entire system. However, simple methods based on image processing and machine learning cannot be fully applied to most images. Therefore, it is necessary to pay attention to the human visual attention mechanism and study how the human eye searches, finds and detects objects in natural scenes. The visual attention mechanism is an intrinsic property of the primate visual system. ...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that it cannot be directly used to find the salient area of ​​the image, ignoring the salient area or the position of the target, that is, it cannot simulate the "position pathway" of the human brain.

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