基于局部灰度聚类的高斯分布拟合模型  

Gaussian Distribution Fitting Model Based on Local Intensity Clustering

作　　者：于晓升[1] 胡楠[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110819

出　　处：《东北大学学报（自然科学版）》2017年第2期185-189,共5页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(61273078);中央高校基本科研业务费专项资金资助项目(N140403005)

摘　　要：针对高斯分布拟合模型对初始轮廓敏感的问题,提出一个基于局部灰度聚类的高斯分布拟合模型.新模型根据图像局部像素灰度聚类特点,采用灰度偏移场和一个分片常量函数共同拟合图像的局部灰度均值,实现了图像全局信息和局部信息的有机结合,使轮廓可以从任意初始位置向目标边缘演化,最后收敛在边缘上.新模型采用一种快速有效的数值方法实现,水平集函数在整个演化过程中不必重新初始化,活动轮廓演化速度得到显著提高.实验结果表明,本文算法能够在不同的轮廓初始化情况下获得准确的分割结果.A novel active contour model was developed for the problem that the Gaussian distribution fitting model is sensitive to a starting contour. According to the characteristics of the local intensity clustering, the bias field and a piecewise constant function were integrated to approximate the local image intensities, which made the objects be segmented with the starting contour being anywhere in the image. An efficient numerical schema was used for the implementation of the proposed model in order to converge rapidly and avoid re- initialization. Experimental results on a series of real and synthetic images demonstrate that the proposed model is robust to the starting contour and the images with intensity inhomogeneities can be effectively segmented.

关 键 词：图像分割 灰度偏移场 分片常量 水平集 活动轮廓 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]