基于粗糙性度量的彩色图像分割方法

Roughness Measure Approach to Color Image Segmentation

彩色图像分割技术是现代图像处理和图像分析领域的重要研究议题.结合粗糙集理论,利用像素邻域的空间信息,可以构造图像色彩分布的上下近似以及量化粗糙性表示,并据此设计基于量化粗糙信息的分割方法:QR measure,该方法依据数据分布获取自适应阈值进行峰值选定和区域合并.实验采用UCBerkeley开放的图像分割测试集,通过比较基于多种统计信息的分割方法,验证提出的优化算法可以取得更好的分割效果.

Color image segmentation is very essential and critical to modern image processing and analysis.According to the rough set theory,the lower,upper approximations and quantitative roughness representation of the color distributions are constructed based on the homogeneity of the pixels neighborhood.Furthermore,a new segmentation approach—QR measure is designed,in which an adaptive thresholding strategy is proposed to select peaks of indexes and merge regions.The experimental results demonstrate that the proposed approach outperforms several methods based on various kinds of statistics.