Machine vision construction method based on human vision simulation

Abstract

The invention discloses a machine vision construction method based on human vision simulation. The machine vision construction method comprises the following steps: 1) carrying out significance detection on a target image through a frequency domain method to obtain a corresponding pixel significance map; 2) sorting points of significance in the pixel significance map according to significance; 3) selecting first N points of significance as fixation points, taking each fixation point as a center to form a local area with a maximum information entropy, and forming a fixation area by the local areas; 4) carrying out random sampling on an internal pixel of the fixation area, and carrying out equivalent pixel random sampling on the outer part of the fixation area; and 5) utilizing an extreme learning machine training strategy to classify all pixels of the target image through a model, and taking a pixel area which is divided into a normal sample as a first fixation target area. According to a process of human vision fixation, an active vision process of human can be simulated through fixation point sorting and a neural network model so as to construct the machine vision which can carry out quick and effective fixation 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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