Edge Prior Multilayer Segmentation Network Based on Bayesian Framework

Abstract

In recent years, methods based on neural network have achieved excellent performance for image segmentation. However, segmentation around the edge area is still unsatisfactory when dealing with complex boundaries. This paper proposes an edge prior semantic segmentation architecture based on Bayesian framework. The entire framework is composed of three network structures, a likelihood network and an edge prior network at the front, followed by a constraint network. The likelihood network produces a rough segmentation result, which is later optimized by edge prior information, including the edge map and the edge distance. For the constraint network, the modified domain transform method is proposed, in which the diffusion direction is revised through the newly defined distance map and some added constraint conditions. Experiments about the proposed approach and several contrastive methods show that our proposed method had good performance and outperformed FCN in terms of average accuracy for 0.0209 on ESAR data set.

Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance

“…It draws on the Domain Transform method of one-dimensional signal, and uses the edge intensity as the weight of the filter to correct the original segmentation result. The diffusion method is improved in [32], and the edge distance map is proposed to guide the direction of diffusion. Both methods in [31,32] belong to the method of adding edge information to correct the segmentation after the segmentation is completed.…”

“…The diffusion method is improved in [32], and the edge distance map is proposed to guide the direction of diffusion. Both methods in [31,32] belong to the method of adding edge information to correct the segmentation after the segmentation is completed. What we have done in this paper is to use an end-to-end network to combine semantic segmentation with edge detection so that the associated parameters can be updated by training.…”