Target sensing method based on compressed sensing classification

Abstract

The invention discloses a target sensing method based on compressed sensing classification. The method comprises the following steps that 1) significance test is carried out on a target image in a spectral residual method to obtain a corresponding pixel significance figure; 2) points of significance in the pixel significance figure are ordered according to the significance; 3) the most N significant points of significance are selected as focusing points, and a minimal rectangle which includes the focusing points serves as a focusing area; 4) pixels within the focusing area are sampled randomly, and pixels, of the same amount, beyond the focusing area are sampling randomly; and 5) a dichotomy pixel classification model is obtained by utilizing the compressed sensing classifier strategy, all the pixels of the target image are classified by the model, and an pixel area of a positive class serves as a first focusing target area. According to the human visual focusing process, human visual sense is simulated by focusing points ordering and the pixel classification model, so that a target scene is rapidly focused, and a machine can visually sense 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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