Semantic object dividing method suitable for low depth image

Abstract

The present invention relates to a semantic object segmentation method suitable for low field depth image, which includes: firstly introducing gradient histogram to figure out the distribution of image in the energy space, obtaining an energy focusing significance map in combination with the character of the low field depth map; using the two-sided filter and morphology instrument to rehandle the energy focusing significance map; and then setting self-adapting threshold value and processing to obtain an initial object mask map, combining with the edge information obtained by canny operator to obtain the corrected object mask, in order to raise the segmentation accuracy of the interesting object; finally using the Bayesian eclosion algorithm to obtain the ideal semantic object segmentation result, in order to delicately process the complicated image boundary, such as hairs. Accurate segmentation to the interesting object in the field depth scope in the image video sequence.

Description

technical field [0001] The invention relates to a semantic object segmentation method suitable for images with low depth of field, which is completely different from the current method in that, in terms of the accuracy of solving the distribution of the focus area, the energy focus saliency map is obtained through the energy space of the image; at the same time, the method The edge information of the image and the matting algorithm are used to refine the boundary of the object, and the segmentation accuracy of the object is improved. Background technique [0002] Image segmentation is an important issue in the fields of image analysis, pattern recognition and computer vision, and it is also a classic difficult problem. The ultimate goal of image segmentation is to segment objects with specific practical significance, that is, semantic objects. Many scholars have proposed many classic segmentation algorithms in the field of image segmentation, but relying on traditional image...

AI Technical Summary

Problems solved by technology

Many scholars have proposed many classic segmentation algorithms in the field of image segmentation, but relying on traditional image segmentation methods to extract semantic objects from arbitrary images, the practicability and adaptability of the algorithm are not ideal, so the proposed algorithm for a class of images A reliable semantic object segmentation method has become a very necessary research direction

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