Extraction method of visual saliency area based on monocular depth map

Abstract

The invention discloses an extraction method of a visual saliency area based on a monocular depth map. The extraction method of the visual saliency area comprises the following sequential steps: dividing an original image to obtain super pixels; building a characteristic vector of each super pixel to estimate an absolute depth characteristic of absolute depth in a scene in the image; building a probability model by using a Gaussian-Markov random field model, calculating a distance relation between the super pixel characteristic vector and an adjacent super pixel characteristic vector by virtue of the probability model to obtain a relative depth characteristic on the basis of the absolute depth characteristic and obtain depth values and depth maps of the super pixels simultaneously; calculating saliency values of the super pixels; calculating a gain coefficient according to the depth values and correcting the saliency values by using the gain coefficient. The extraction method can be used for quickly and automatically identifying the saliency objects in the image without any prior knowledge, is high in universality and can also be used for accurately detecting the saliency area.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method for extracting a visually salient region based on a monocular depth map. Background technique [0002] With the development of science and technology and the improvement of computer computing performance, human beings increasingly expect computers to be able to complete computing tasks more intelligently and autonomously. Meeting this expectation will require computers to acquire the ability to understand their surroundings. Vision is the most important way for human beings to perceive external information. Salient region detection plays a key role in computer vision, image processing and other fields, and has always been a hot topic of research. [0003] Human visual perception has evolved to a higher level through long-term natural selection. The human visual system can quickly and effectively extract objects of interest from the complex external environment and resp...

AI Technical Summary

Problems solved by technology

However, conventional saliency algorithms can only identify the salient regions of simple images, and these conventional algorithms are difficult to achieve satisfactory quality for complex images, because the lack of consideration of object distance information will cause a large amount of calculation and blurred edges And other issues

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