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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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Robust Segmentation Method for Noisy Images Based on an Unsupervised Denosing Filter

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Tsinghua Science and technology 2021年第5期26卷 736-748页

作者： Ling Zhang Jianchao Liu Fangxing Shang Gang Li Juming Zhao Yueqin Zhang college of Software Taiyuan University of TechnologyTaiyuan 030024China college of Information and computer Taiyuan University of TechnologyTaiyuan 030024China Information technology Department Shanxi Tizones Technology Co.LtdTaiyuan 030024China

Level-set-based image segmentation has been widely used in unsupervised segmentation *** have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing *** existing methods are based on the assumption that the distribution of image noise is known or ***,real-time images do not meet this *** bridge this gap,we propose a novel level-set-based segmentation method with an unsupervised denoising ***,a denoising filter is acquired under the unsupervised learning ***,the denoising filter is integrated into the level-set framework to separate noise from the noisy image ***,the level-set energy function is minimized to acquire segmentation *** experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.

关键词： image segmentation noisy image level set autoencoder

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