计算机应用 ›› 2012, Vol. 32 ›› Issue (03): 749-751.DOI: 10.3724/SP.J.1087.2012.00749

• 图形图像技术 • 上一篇    下一篇

基于水平集的新型彩色图像分割算法

陈沅涛1,2,徐蔚鸿1,2,吴佳英1,2   

  1. 1.南京理工大学 计算机科学与技术学院,南京 210094;
    2.长沙理工大学 计算机与通信工程学院,长沙 410004
  • 收稿日期:2011-08-18 修回日期:2011-12-08 发布日期:2012-03-01 出版日期:2012-03-01
  • 通讯作者: 陈沅涛
  • 作者简介:陈沅涛(1980-),男,湖南怀化人,讲师,博士研究生,主要研究方向:图像分割、图像识别;徐蔚鸿(1963-),男,湖南湘潭人,教授,博士生导师,博士,主要研究方向:模糊理论、模糊逻辑;吴佳英(1977-),女,湖南桃江人,副教授,博士研究生,主要研究方向:无线传感器网络、模式识别。
  • 基金资助:

    湖南省教育厅科研基金资助项目(11C0043,11C0035);湖南省科技计划基金资助项目(2011GK3086,2011SK3079);长沙市科技局基金资助重点项目(K1104022-11)。

New colorful images segmentation algorithm based on level set

CHEN Yuan-tao1,2, XU Wei-hong1,2, WU Jia-ying1,2   

  1. 1.School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China;
    2.School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha Hunan 410004, China
  • Received:2011-08-18 Revised:2011-12-08 Online:2012-03-01 Published:2012-03-01

摘要: 由于考虑的泛函变分形式是非凸性质,向量值图像分割模型的计算结果经常会陷入局部最小值。基于活动轮廓的向量值图像的全局图像分割方法,以新型变分形式将向量值图像分割和图像去噪融入具有全局极小能力泛函框架中。新模型具有容易构造和较少计算量的特点,对比经典的水平集方法,可以避免繁琐的距离重复化水平集过程。通过对人工图像和真实图像进行分析,验证新方法具有更好的图像分割效果。

关键词: 活动轮廓, 局部极小值, 全局极小值, 向量值图像, 图像分割

Abstract: Since the functional form in consideration is of non-convex variational nature, the calculation results of the image segmentation model often fall into local minimum. Based on the global vector-valued image segmentation of active contour, the global vector-valued image segmentation and image denoising were integrated in a new variational form within the framework of global minimum. The new model was easy to construct and of less computation. Compared to the classical level set method, tedious repetition of the level set could be avoided. With the analyses on artificial images and real images, the new method is verified to have better segmentation results.

Key words: active contour, local minimum, global minimum, vector-valued image, image segmentation

中图分类号: