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Method for predicting visual attention area transfer in gray images

A technology of visual attention and area transfer, applied in instruments, character and pattern recognition, computer parts, etc., can solve the problem of not being able to reflect the characteristics of human vision, and achieve the effect of easy implementation and low computational complexity.

Active Publication Date: 2011-03-09
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

The existing pixel-based viewpoint transfer methods cannot reflect this human visual characteristic

Method used

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  • Method for predicting visual attention area transfer in gray images
  • Method for predicting visual attention area transfer in gray images
  • Method for predicting visual attention area transfer in gray images

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Embodiment Construction

[0027] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings. The present invention is limited to processing grayscale images, and the processed images are obtained various natural scenes or artificial images.

[0028] see figure 1 , figure 2 , image 3 As shown, the present invention is divided into 4 steps, 1) calculate the side potential of all pixels in a grayscale image, obtain the leader according to the side potential and the threshold that have been drawn; 2) each leader finds its follower , and form different regions. An area may include more than one leader, but in the actual computer implementation process, if a leader follows other leaders, it is judged as a follower; 3) Calculate the significant value for each area; 4) According to the difference of the significant value Display the first three significant regions, which are believed to be reliable at this stage. The overall flowchart of the metho...

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Abstract

The invention discloses a method for predicting visual attention area transfer in gray images, which comprises four steps of: determining leaders, searching followers, calculating significant values and ordering the significant values. The step of determining the leaders is to calculate side potentials of all pixels and determine the leaders in the pixels according to the acquired side potentials and a threshold value. The step of searching the followers is to determine the followers of each leader in all the pixels according to the connectivity and the similarity to form different areas, wherein each area may comprise more than one leader, but in the actual computer implementation process, the leader following other leaders is regarded as a follower. The step of calculating the significant values is to calculate the obtained areas respectively, and one area corresponds to one significant value. The step of ordering the significant values is to order all the areas according to the magnitude of the significant values and take the front three areas. The method successfully introduces the selective attention function of a human vision system into a computer vision system, and can simulate and predict attention transfer of a human eye view point among different areas.

Description

technical field [0001] The invention relates to region analysis in image processing, in particular to the detection of salient regions in the image and provides a prediction method for the transfer of viewpoints between extremely salient regions in the grayscale image. Background technique [0002] Among all human senses, at least 70% of external information is obtained through the visual system. Biological vision systems, including human vision systems, can automatically select salient regions of a scene for understanding and switch between different regions. Extracting salient features from images or receptive fields and dividing them into different regions, selecting salient regions is the fundamental task of perceptual understanding. This ability is the visual attentional selection in visual comprehension. When the salient area is selected, due to the adaptability of the visual system, the attention will shift from the current salient area to the next salient area, whi...

Claims

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Application Information

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IPC IPC(8): G06K9/00
Inventor 段立娟房法明乔元华王海丽吴春鹏苗军杨震
Owner BEIJING UNIV OF TECH
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