کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1784018 | 1524112 | 2016 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An improved Chan-Vese model by regional fitting for infrared image segmentation
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
فیزیک اتمی و مولکولی و اپتیک
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: An improved Chan-Vese model by regional fitting for infrared image segmentation An improved Chan-Vese model by regional fitting for infrared image segmentation](/preview/png/1784018.png)
چکیده انگلیسی
In this paper, a regional fitting method is proposed for infrared image segmentation. In our model, the intensity of each pixel in a region is described by using the sum of the class center and the weighted variance of the region, in order to build energy function for encouraging the similarity pixels to be clustered together. The adoption of such way can thereby eliminate the issue associated with the drift of the class center that is existed in Chan-Vese model. Particularly, followed by incorporating energy function into the level set evolution without re-initialization framework, the variational formulation can force the level set function to be closed to object boundaries. Experiments on some representative and real infrared images have demonstrated that our model has higher performance of segmentation in comparison with Chan-Vese model without re-initialization, and some existing methods, including LBF and LCV model.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Infrared Physics & Technology - Volume 74, January 2016, Pages 81-88
Journal: Infrared Physics & Technology - Volume 74, January 2016, Pages 81-88
نویسندگان
Dongguo Zhou, Hong Zhou, Yanhua Shao,