Image foreground segmentation method combining subject determination and edge precision

Abstract

The invention discloses an image foreground segmentation method combining main body determination and edge precision, which combines a main body determination process for positioning a salient region and an edge precision process for precisely segmenting a target. Firstly, a main body part is determined, and a context sensing pyramid feature extraction module is designed to obtain rich context features; a channel attention mechanism CA module after feature mapping of a context sensing pyramid feature extraction module and a space attention mechanism SA module after low-level feature mapping are combined, and cross entropy loss is used for supervising generation of significant boundary positioning information so as to obtain positioning information; secondly, an edge process is precise, non-local color features in the image are obtained on the basis of a spectrum extinction technology, advanced semantic features are obtained through a ResNet-101 deep residual network, pixel points in the image are classified by combining the non-local color features with the advanced semantic features through a Laplacian matrix, and the effect of precise target segmentation is achieved; and finally, the results of the two processes are fused.

Description

Technical field [0001] The present invention relates to the field of information technology, and more particularly to a method of determining an image foreground segmentation method combining the body and precision edge. Background technique [0002] When people get an image, the more introduced partial part is often a significant main area, which is called the foreground. Some spatial information (color, contour, texture, grayscale, etc.) in the foreground part is different from the surrounding environment. The foreground segmentation is based on these differences, and the image will split and extract the image with unique properties. Such as FASR R-CNN, Mask R-CNN, Masklab has a wide range of applications in people's actual life: the measurement image in the medical field, in the field of cloud map in the field of remote sensing, in the transportation field Extraction of vehicle contour characteristics and pedestrian testing, etc. While foreground segmentation technology has fa...

AI Technical Summary

Problems solved by technology

However, the segmentation method based on low-level feature information has great limitations: the image needs to have similarity and continuity in the same area, the quality of the segmentation effect is strongly dependent on the similarity condition, and the segmentation result is prone to over-segmentation

However, the development of segmentation methods based on high-level feature information is still immature, and its universality and speed still need to be strengthened

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