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基于粗糙集和新能量公式的水平集图像分割

张迎春 郭禾

张迎春, 郭禾. 基于粗糙集和新能量公式的水平集图像分割. 自动化学报, 2015, 41(11): 1913-1925. doi: 10.16383/j.aas.2015.c140823
引用本文: 张迎春, 郭禾. 基于粗糙集和新能量公式的水平集图像分割. 自动化学报, 2015, 41(11): 1913-1925. doi: 10.16383/j.aas.2015.c140823
ZHANG Ying-Chun, GUO He. Level Set Image Segmentation Based on Rough Set and New Energy Formula. ACTA AUTOMATICA SINICA, 2015, 41(11): 1913-1925. doi: 10.16383/j.aas.2015.c140823
Citation: ZHANG Ying-Chun, GUO He. Level Set Image Segmentation Based on Rough Set and New Energy Formula. ACTA AUTOMATICA SINICA, 2015, 41(11): 1913-1925. doi: 10.16383/j.aas.2015.c140823

基于粗糙集和新能量公式的水平集图像分割

doi: 10.16383/j.aas.2015.c140823
基金项目: 

国家自然科学基金(61033012)资助

详细信息
    作者简介:

    郭禾 大连理工大学软件学院教授.1982年在吉林大学计算机系获学士学位,1989年在大连理工大学计算机系获硕士学位.主要研究方向为图像处理,系统结构等.E-mail:guohe@dlut.edu.cn

    通讯作者:

    张迎春 大连理工大学软件学院博士研究生.2009年获得辽宁师范大学计算机学院硕士学位.主要研究方向为图像处理和粗糙集理论.本文通信作者.E-mail:zhangyingchun1871@163.com

Level Set Image Segmentation Based on Rough Set and New Energy Formula

Funds: 

Supported by National Natural Science Foundation of China (61033012)

  • 摘要: 为了提高水平集图像分割的质量和减少水平集迭代次数,本文提出了新的能量公式和水平集函数.在粗糙集数据离散化基础上引入了针对图像数据的离散化方法,根据图像离散区域的信息对新能量函数进行直接加权并且对核函数进行间接加权,使用加权的核映射函数将原始离散图像数据映射到高维空间,从而使得该模型可以处理多种类型的图像甚至是一定信噪比的噪声图像.新的能量公式联合由它导出的区域参数能够更好地表达同质区域的灰度信息,从而能够更精确地分割图像.与传统水平集图像分割不同,在迭代过程中新的水平集函数中的水平集元素可以拥有不同的步长,步长越大水平集元素的更新速度越快并且水平集函数能够快速达到收敛状态,实现快速图像分割.人工合成图像和真实图像的分割实验表明本文方法可以获得更好的分割效果.
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出版历程
  • 收稿日期:  2014-11-28
  • 修回日期:  2015-08-31
  • 刊出日期:  2015-11-20

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