An active contour model for image segmentation using morphology and nonlinear Poisson's equation

被引：4
|
作者：
Chen Y. [1 ]
Wu L. [1 ]
Wang G. [1 ]
He H. [1 ]
Weng G. [1 ]
Chen H. [2 ]
机构：
[1] School of Mechanical and Electric Engineering, Soochow University, No. 8, Jixue Road, Jiangsu, Suzhou
[2] Department of Chemical and Materials Engineering, University of Alberta, Edmonton, T6G 1H9, AB
来源：
Optik | 2023年 / 287卷
基金：
中国国家自然科学基金;
关键词：
Active contour model; Morphology; Poisson's equation; Region-scalable fitting model;
D O I：
10.1016/j.ijleo.2023.170997
中图分类号：
学科分类号：
摘要：
Active contour model (ACM) is considered as a feasible tool to handle image segmentation problems via an unsupervised learning approach. This paper compensates the shortcomings of well-known ACMs to introduce erosion and dilation operations in mathematical morphology to fit the intensity differential functions of contours on both sides of the image. A nonlinear Poisson's equation is developed to deal with the interference of nonuniform noise within the image, and finally build a new model. This paper develops a new image processing approach using morphology and nonlinear Poisson's equation, which yields a fast and efficient algorithm among known similar image segmentation methods, and has a high level of accuracy in processing image segmentation. © 2023 Elsevier GmbH
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