Dynamic vision caution region extracting method based on characteristic

Abstract

The invention relates to a dynamic visual attention area extraction method based on features in the technical field of machine vision. The method comprises the following steps: first, an independent component analysis method is adopted for carrying out sparse decomposition to mass natural images so as to obtain a group of filtering base functions and a group of corresponding reconfigurable base functions and the input images are divided into small RGB blocks of m multiplied by m and projected on a group of the base to obtain the features of the images; second, effective encoding principle is utilized to measure an incremental encoding length index for each feature; third, according to the incremental encoding length index, the remarkable degree of each small block is processed through the energy reallocation of each feature and finally a remarkable map is obtained. The method can reduce a 'time slice', realize continuous sampling, therefore, data of different frames can direct the processing of remarkable degrees together, and the problem that the remarkable degrees of different frames require independent process is solved so as to realize the dynamic performance.

Description

technical field [0001] The present invention relates to a method in the technical field of image processing, in particular to a feature-based dynamic visual attention region extraction method. Background technique [0002] With the continuous development of artificial intelligence technology, machine vision is used more and more in real life. It mainly uses computers to simulate human visual functions, but it is not just a simple extension of the human eye, but more importantly, it has the human brain. A part of the function - to extract information from the image of objective things, process and understand it, and finally use it for actual detection, measurement and control. Because machine vision has the characteristics of fast speed, large amount of information, and multiple functions, it is widely used in quality inspection, identity authentication, object detection and recognition, robots, and automatic cars. [0003] At present, engineering has been able to produce se...

AI Technical Summary

Problems solved by technology

Although this method and the subsequent space-based analysis techniques have good performance in many scenes, they almost inevitably face the following problems: 1) only pay attention to a certain part of visual cues, 2) the distribution of attention is not continuous in time

For example, when observing continuous images, the system cannot consider the situation of multiple frames, which leads to the need to re-analyze the salient map separately at each moment, which greatly reduces the continuity and reliability of the system

Moreover, when the viewing angle and the position of the object change, since there is no tracking mechanism for the feature, the prediction of the new salient map is likely to be offset from the previous frame.

Furthermore, a range of visual attentional behaviors, such as inhibition of return, and viewpoint shift, are not well represented by spatially based analysis techniques

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