Machine vision construction method based on simulating human vision

Abstract

The invention discloses a machine vision construction method based on simulating human vision, which comprises the following steps: 1) performing saliency detection on target images through a frequency domain method to obtain a corresponding pixel saliency map; 2) detecting 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 take each fixation point as the center to form a local area with the largest information entropy, and these local areas form a fixation area; 4) Carry out random sampling to the internal pixels of the fixation area, and carry out random sampling to the pixels of equal amount outside the fixation area; 5) Utilize the extreme learning machine training strategy, classify all pixels of the target image by this model, will be divided into The pixel area of ​​the positive sample is used as the first fixation target area. According to the human visual gazing process, the invention simulates the human active visual process through gaze point sorting and a neural network model, so as to construct a machine vision with fast and effective gazing on a target scene.

Description

technical field [0001] The invention relates to the technical field of human vision construction, in particular to a machine vision construction method based on simulating human vision. 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 foll...

AI Technical Summary

Problems solved by technology

However, when the mechanism of human visual perception is not yet fully understood, there are still great difficulties in how to construct machine vision with human visual characteristics. If a machine vision system that simulates human vision can be constructed, it will definitely give a boost to various practical application fields of computer vision. have an important impact

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