在线咨询
中国工业与应用数学学会会刊
主管：中华人民共和国教育部
主办：西安交通大学
ISSN 1005-3085  CN 61-1269/O1
工程数学学报

工程数学学报 ›› 2015, Vol. 32 ›› Issue (5): 633-642.doi: 10.3969/j.issn.1005-3085.2015.05.001

• •    下一篇

一种改进的C-V图像分割模型

李五强,   杨  巧,   韩国栋   

  1. 陕西师范大学数学与信息科学学院，西安 710062
  • 收稿日期:2014-05-28 接受日期:2014-11-20 出版日期:2015-10-15 发布日期:2015-12-15
  • 通讯作者: 韩国栋 E-mail: gdhan@snnu.edu.cn
  • 基金资助:
    国家自然科学基金 (11101253)；中央高校基本科研业务费 (GK201301007; GK201401004; GK201503016)；陕西省教育厅科学研究计划 (14JK1461).

An Improved C-V Image Segmentation Model

LI Wu-qiang,   YANG Qiao,   HAN Guo-dong   

  1. School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062
  • Received:2014-05-28 Accepted:2014-11-20 Online:2015-10-15 Published:2015-12-15
  • Contact: G. Han. E-mail address: gdhan@snnu.edu.cn
  • Supported by:
    The National Natural Science Foundation of China (11101253); the Fundamental Research Funds for the Central Universities (GK201301007; GK201401004; GK201503016); the Science Program of Education Department of Shaanxi Province (14JK1461).

PDF (PC)

4

摘要： 针对传统C-V图像分割模型在分割效率和准确性两方面的不足，本文提出了一种改进的C-V图像分割模型：一，在模型中加入内部能量项，使水平集函数被限定为符号距离函数，从而避免了水平集函数的重新初始化，提高了图像分割的效率．二，选取Heaviside函数的新正则化函数，使其逼近效果更佳，提高了图像分割的准确性．三，用正实数函数去替换传统C-V模型中Dirac函数的正则化函数，一方面，消除了后者对非初始活动轮廓线附近同质区域边界检测的抑制作用，进而使模型具有更好的全局优化特性，提高了图像分割的准确性；另一方面，使得模型的计算更为简单，提高了图像分割的效率．数值实验表明本文提出的改进C-V图像分割模型提高了图像分割的效率与准确性．

关键词: 图像分割, C-V模型, 水平集函数, Heaviside函数, Dirac函数

Abstract:

Aiming at the deficiency of the traditional C-V model for image segmentation in terms of efficiency and accuracy of segmentation, this paper presents an improved C-V image segmentation model. Firstly, the level set function is restricted as a signed distance function by adding the internal energy term in the model, which could avoid the re-initialization and improve the efficiency of image segmentation. Secondly, the new regularization function of Heaviside function is chosen to improve the approximation effect and the accuracy of image segmentation. Finally, the regularization function is applied to replace the traditional Dirac function in C-V model with positive real functions. On the one hand, it's able to eliminate the latter inhibition of homogeneous areas near the border to detect non-initial active contour lines, and then makes the better global optimization features to improve the accuracy of image segmentation; on the other hand, it gives more simple model and improves the efficiency of image segmentation. Compared with the original C-V model, the numerical experiments show that the improved model has better efficiency and higher accuracy.

Key words: image segmentation, C-V model, level set function, Heaviside function, Dirac function

中图分类号: