J4 ›› 2010, Vol. 27 ›› Issue (6): 677-682.

• 图像与信息处理 • 上一篇    下一篇

一种基于图像灰度信息和方差信息的图像分割方法

杨旭朗 侯榆青 陈燊 高士瑞   

  1. 西北大学信息科学与技术学院,陕西 西安 710127
  • 收稿日期:2009-12-30 修回日期:2010-04-14 出版日期:2010-11-28 发布日期:2010-11-19
  • 通讯作者: 侯榆青(1963-),女,教授,主要研究方向:模式识别与图像处理、DSP应用技术. E-mail:loyoung@sohu.com
  • 作者简介:杨旭朗(1983-),男,硕士,主要研究领域:模式识别与图像处理;陈燊(1985-),男,硕士,主要研究领域:模式识别与图像处理.
  • 基金资助:

    国家自然科学基金(10671156)、陕西省自然科学基础研究计划数字图像合成及其匹配技术研究基金(2009JM8004-3) 、西北大学研究生交叉学科基金(08YJC12)

An image segmenting method based on image gray and variance information

YANG Xu-lang, HOU Yu-qing, CHEN Shen, GAO Shi-rui   

  1. School of Information Science and Technology, Northwest University, Xi’an 710127, China
  • Received:2009-12-30 Revised:2010-04-14 Published:2010-11-28 Online:2010-11-19

摘要:

针对C-V方法对非二值图像分割不理想,运行效率不高的问题,提出一种改进的C-V方法。在C-V方法只运用图像灰度信息的基础上,加入基于图像局部方差的信息,并且设置加权参数k,通过k来控制基于图像的灰度信息和方差信息的驱动力在整个图像分割驱动力中的比重,使得改进C-V方法能利用图像区域灰度信息和区域方差信息对非二值图像进行分割,同时应用隐式方案的数值实现方式对改进方法进行数值实现。图像分割实验结果表明,该方法能够更为准确地提取非二值图像边界,减少迭代次数。

关键词: image processing, improved C-V method, partial differential equations, non-binary image, image variance

Abstract:

An improved C-V method was proposed aiming at unfavorable result and low efficiency when the original C-V method was used to segment non-binary image. Considering that the original C-V method only used the gray information, the improved C-V method added local variance information of the image. In addition to that, the improved C-V method set a weight parameter k, and by adjusting k it can control the proportions of the driving force based on the gray and variance information in the process of segmenting the entire image. Thus, this method can segment non-binary image by the regional gray and variance information. Simultaneously, the implicit scheme was used in the process of the numerical realization of the improved method. The experimental results show that the improved C-V method can extract edge of non-binary image more accurately and reduce the times of iteration.

Key words: image processing, improved C-V model, partial differential equations, non-binary image, image variance

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