Variation Level Set Method for Multiphase Image Classification
Автор: Zhong-Wei Li, Ming-Jiu Ni, Zhen-Kuan Pan
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 5 vol.3, 2011 года.
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In this paper a multiphase image classification model based on variation level set method is presented. In recent years many classification algorithms based on level set method have been proposed for image classification. However, all of them have defects to some degree, such as parameters estimation and re-initialization of level set functions. To solve this problem, a new model including parameters estimation capability is proposed. Even for noise images the parameters needn’t to be predefined. This model also includes a new term that forces the level set function to be close to a signed distance function. In addition, a boundary alignment term is also included in this model that is used for segmentation of thin structures. Finally the proposed model has been applied to both synthetic and real images with promising results.
Partial differential equations, level set, image classification, boundary alignment, re-initialization
Короткий адрес: https://sciup.org/15012183
IDR: 15012183
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