Imaging-based visual weight graph extraction method

Abstract

The invention discloses an imaging-based visual weight graph extraction method. A system brought forward by the invention is based on a single-optical-path double Fourier lens imaging, can directly carry out imaging analysis of a visual weight on a scene, and can effectively and rapidly obtain a high-precision visual weight graph. The method comprises the following steps: 1, constructing a single-optical-path double Fourier lens imaging system; and 2, generating a visual weight image. According to the method, the single-optical-path double Fourier lens imaging system is constructed, different filtering of two times is realized at a frequency spectrum surface, different low-pass filtering effects are realized, a filtering result is obtained by use of an industrial camera, finally, the visual weight graph is obtained through an absolute value of a gray-scale difference between images, and the processing result is close to that of human eye visual system. The corresponding visual weight graph can be rapidly obtained as long as the system aims at the scene (it should be satisfied that an object distance is greater than a lens focal length by more than ten times).

Description

technical field [0001] The invention relates to optical imaging and image processing technology, in particular to an imaging-based visual weight map extraction method. Background technique [0002] Visual information is received by human beings in the form of light and collected by image sensors. This is the most important source of information for people to understand the world. After the visual information is received by the image sensor, it forms the computer image we know. Image analysis and processing has become a very important technology, and analyzing the visual weight of images is very popular in today's image engineering. [0003] Analyze the visual weight of the image, that is, obtain images reflecting different weight distributions in different regions based on the original image. The value of the image is between 0 and 1. The larger the value, the more the human eye pays attention to the region, and the smaller it is, the opposite is true. This is to use infor...

AI Technical Summary

Problems solved by technology

Currently, visual weight analysis methods such as saliency detection techniques are not satisfactory in all aspects

On the one hand, the resolution of the visual weight map itself is not high enough; on the other hand, the acquisition of the visual weight map is generally through the relatively complex calculation of the computer, and the efficiency depends on the computing power of the hardware

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