Fast Image Segmentation Based on Two-dimensional Minimum Tsallis-Cross Entropy

摘要：

Image segmentation based on 2-D (two dimensional)histogram is an effective method because the structure information is taken into account in image. However, it always is on the assumption that partial region of 2-D histogram equals to zero, while utilizing Shannon entropy as optimization function. As a result, the efficiency of image segmentation is degraded seriously. In this paper, we proposed a fast thresholding segmentation based on two-dimensional minimum Tsallis-cross entropy and PSO, which utilizes minimum Tsallis-cross entropy as optimization function which is non-extensive and calculates optimal threshold in improved gray level-gradient histogram which cancels previous hypothesis that partial region value equals to zero in histogram. At the same time, the improved 2-D histogram is clustered before searching optimal threshold value to shorten the time. Experiment results show that the proposed algorithm achieves a better segmentation quality and computation efficiency.

关键词： image segmentation Tsallis-cross entropy particle swarm optimization

作者: Weiyi Wei Xianghong Lin Guicang Zhang

作者单位: College of Mathematics and Information Science, Northwest Normal University Lanzhou, China

会议类型: 国际会议

会议名称: 2010 International Conference on Image Analysis and Signal Processing(2010 图像分析与信号处理国际会议 IASP 10)

会议地点: 厦门

会议语种:英文

页码: 332-335

在线出版日期: 2010-04-12（万方平台首次上网日期，不代表论文的发表时间）