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Entropy-Based Global and Local Weight Adaptive Image Segmentation Models

Tsinghua Science and technology 2020年第1期25卷 149-160页

作者： Gang Li Yi Zhao Ling Zhang Xingwei Wang Yueqin Zhang fayun guo Taiyuan University of technology Taiyuan 030024China college of computer Science and Engineering Northeastern UniversityShengyang 110819China shanxi Tizones technology co.ltd. Taiyuan 030082China fayun guo is with shanxi taisen technology co.ltd. Taiyuan 030082China

This paper proposes a parameter adaptive hybrid model for image segmentation. The hybrid model combines the global and local information in an image, and provides an automated solution for adjusting the selection of the two weight parameters. Firstly, it combines an improved local model with the global Chan-Vese(CV) model, while the image’s local entropy is used to establish the index for measuring the image’s gray-level information. Parameter adjustment is then performed by the real-time acquisition of the ratio of the different functional energy in a self-adapting model responsive to gray-scale distribution in the image segmentation *** with the traditional linear adjustment model, which is based on trial-and-error, this paper presents a more quantitative and intelligent method for achieving the dynamic nonlinear adjustment of global and local *** show that the proposed model achieves fast and accurate segmentation for different types of noisy and non-uniform grayscale images and noise images. Moreover, the method demonstrates high stability and is insensitive to the position of the initial contour.

关键词： image local entropy parameter adaption image segmentation active contour

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A DAC-CLGD-Danet network based method for defaced image segmentation

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Intelligent and converged Networks 2022年第3期3卷 294-308页

作者： Pengbo Li Gang Li Yibin He Ling Zhang Yuanjin Sun fayun guo college of Software Taiyuan University of TechnologyTaiyuan 030024China Information technology Department Shanxi Taisen Technology Co.Ltd.Taiyuan 030082China

Based on the problems of high noise,lower contrast,and complex features in defaced images and the low accuracy of existing defaced image segmentation techniques,this paper proposes a defaced image segmentation algorithm based on ***,a CBDNet asymmetric blind denoising network is used for noise-containing defaced images,and natural and synthetic images are trained together to model the image noise and enhance the denoising ability of natural ***,Danet is used as the base network.A Dense Atrous convolution module(DAC)is added to the dual attention mechanism module to extend the perceptual domain of deep convolution,reduce image feature loss,and enhance the representation of global information and edge features of defaced images;Cross-Level Gating Decoder module(CLGD)is introduced to lighten the segmentation network,enhance image context aggregation,and produce accurate semantic *** experimental results demonstrated that the method in this paper has a significant effect on the HRF dataset and Cityscapes dataset,with a significant improvement compared with FCN,UNet,and SETR models,with Intersection over Union(IoU)improved by 9.81%and Mean Intersection over Union(mIoU)improved by 3.01%compared with UNet.

关键词： image segmentation Danet deep learning neural networks defaced images

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