| 基于C-V模型的图像分割研究 |
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| 引用本文： | 高勇,郭建,李存华.基于C-V模型的图像分割研究[J].淮海工学院学报,2009,18(4):18-21. |
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| 作者姓名： | 高勇 郭建 李存华 |
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| 作者单位： | 高勇,李存华(淮海工学院,计算机工程学院,江苏,连云港,222005);郭建(华东师范大学,软件学院,上海,200062) |
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| 基金项目： | 江苏省高校自然科学基金资助项目，江苏省"六大人才高峰"项目 |
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| 摘 要： | 活动轮廓模型已经成功应用于图像分割,它可以是基于边界的,也可以是基于区域的。在演化过程中,关键问题是如何使水平集函数逼近符号距离函数。CHAN-VESE（C-V）模型是基于Mumford-Shah分割模型和水平集的,它不依赖图像梯度而检测目标,但其距离保持能力较差。在对C-V模型研究的基础上,提出了一个解决其距离保持问题的办法。
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| 关 键 词： | 活动轮廓 区域 图像分割 水平集 模型 |
Study on Image Segmentation Based on C-V Model |
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| Affiliation: | GAO Yong, GUO Jian, LI Cun-hua (1. School of Computer Engineering, Huaihai Institute of Technology, Lianyungang 222005, China; 2. Software Engineering Institute, East China Normal University, Shanghai 200062, China) |
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| Abstract: | Active contour model has been successfully applied in image segmentation. It can be edge-based or region-based. During the evolution, the key issue is to force the level set function to be close to a signed distance function. CHAN-VESE （C-V） model can detect objects whose boundaries are not necessarily defined by gradient. Based on Mumford-Shah functional for segmentation and level sets, it is weak in preserving distance. In this paper, we proposed a method to address the issue of the distance preserving based on a study of the C-V model. |
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| Keywords: | active contour region image segmentation level set model |
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