Object Perception Method Based on Compressed Sensing Classification

Abstract

The invention discloses a target perception method based on compressed sensing classification, which includes the following steps: 1) performing saliency detection on target images through the spectral residual method to obtain corresponding pixel saliency maps; 2) the pixel saliency The salient points in the saliency map are sorted according to the saliency; 3) select the first N salient points as fixation points, and the minimum rectangular range containing these fixation points is as the fixation area; 4) random sampling is carried out to the internal pixels of the fixation area , and randomly sample the same amount of pixels outside the gaze area; 5) use the compressed sensing classifier strategy to obtain a two-class pixel classification model, and classify all pixels of the target image through this model, and will be divided into positive samples The pixel area of ​​is used as the first fixation target area. According to the process of human visual gazing, the present invention simulates human vision through gaze point sorting and pixel classification models, quickly and effectively gazing on a target scene, and realizes a machine's visual perception of the target scene.

Description

technical field [0001] The invention relates to the technical field of human visual simulation, in particular to an object perception method based on compressed sensing classification. Background technique [0002] With the development of information technology, computer vision has been widely used in low-level feature detection and description, pattern recognition, artificial intelligence reasoning and machine learning algorithms and other fields. However, traditional computer vision methods are usually task-driven, that is, many conditions need to be limited, and corresponding algorithms should be designed according to actual tasks, which lacks versatility; it needs to solve high-dimensional nonlinear feature spaces, large data volumes, problem solving and real-time processing, etc. These problems make its research and application face great challenges. [0003] The human visual system is able to work efficiently and reliably in different environments, which has the follo...

AI Technical Summary

Problems solved by technology

However, when the mechanism of human visual perception is not yet fully understood, it is still difficult to construct machine vision with human visual characteristics. If a machine vision system that simulates human vision can be constructed to perceive the target scene, then It will inevitably have an important impact on applications such as target recognition and perception.

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