Significance human body regional detecting method based on regional integration

Abstract

The invention provides a significance human body regional detecting method based on regional integration. The method includes steps of taking an ultra-pixel regional unit as a node, orderly executing the adjoining integration and the overall integration according to an integration rule psi; orderly structuring an adjoining integration picture FORMULA and an overall integration picture FORMULA; combining the color contrast, space distance weight and background probability weight among the ultra-pixel regional units, and respectively calculating the significance value of the ultra-pixel zone unit at an initial integration picture FORMULA, the adjoining integration picture FORMULA, and the overall integration picture FORMULA; through a confidence genetic mechanism of a cellular automaton machine, updating three initial significance pictures, accumulating and summing the pictures, and acquiring an integrated significance pictures S. In the invention, the method is good for presenting the gradation information in the input image on the basis of the initial integration picture, the adjoining integration picture and the overall integration picture; meanwhile, the method sets the confidence matrix according to the background probability value, and is good for the optimized performance of the cellular automaton; finally, the integrated significance picture S including the significant human body zone is acquired.

Description

technical field [0001] The invention belongs to the field of computer image processing, in particular to a method for detecting salient human body regions based on region fusion. Background technique [0002] Visual salience can be intuitively understood as the ability of semantic elements in visual scenes to attract visual attention, which depends on the salient attributes of target elements, such as particularity and rarity. When processing image scene information, the priority processing object can be obtained through salient area detection, so as to allocate computing resources reasonably, reduce the amount of computing and save costs. Therefore, the detection of image salient regions has a high application value. [0003] Since the pioneers such as Itti proposed using the center-periphery difference framework for saliency detection, more and more researchers have begun to pay attention to the research neighborhood of saliency detection, and a large number of bottom-up ...

AI Technical Summary

Problems solved by technology

Therefore, the traditional human body area detection method is still not ideal; at the same time, the traditional detection method cannot obtain the effect map of the salient human body area

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