摘要
针对基于局部熵的过渡区阈值算法中没有同时考虑局部图像灰度变化的频率和幅度,提出一种融合局部描述子的过渡区阈值算法.提出算法首先采用图像的局部熵和局部方差等局部描述子提取图像的局部特征;其次融合局部图像特征构造特征矩阵,并选取合适的特征阈值提取图像的过渡区;最后根据图像过渡区的灰度均值分割图像.实验结果表明,根据一些图像分割的定量评价标准,提出算法提取过渡区的质量高,分割图像效果好.
Because the transition region thresholding algorithm based on local entropy did not simultaneously consider the amplitude and frequency of the local gray varying of the image,a new transition region thresholding algorithm based on a fusion of local descriptors was proposed.Firstly,in the proposed algorithm,local image feature was extracted by using local image descriptors about local entropy and local variance.Secondly,the feature matrix was constructed by the fusion image feature.Thirdly,the transition region was extracted through opposite feature threshold.Finally,the image was segmented by the grayscale mean of transition pixels.The experimental results show the algorithm performs well in transition region extraction and image segmentation.
出处
《湖南科技大学学报（自然科学版）》
CAS
北大核心
2014年第2期76-80,共5页
Journal of Hunan University of Science And Technology：Natural Science Edition
基金
国家自然科学基金资助项目(60975083)
肇庆学院青年项目(201321)
关键词
图像分割
局部描述子
特征融合
过渡区
局部熵
局部方差
image segmentation
local descriptor
feature fusion
transition region
local entropy
local variance
