适用于小样本问题的有监督边界检测方法  

Supervised boundary detection for small sample problem

作　　者：高梁[1] 廖志武[2] 刘晓云[1] 陈武凡[1,3] 

机构地区：[1]电子科技大学自动化工程学院,成都611731 [2]四川师范大学计算机科学学院,成都610101 [3]南方医科大学生物医学工程学院,广州510515

出　　处：《计算机应用》2011年第10期2697-2701,共5页journal of Computer Applications

基　　金：国家973计划项目(2010CB732501)

摘　　要：针对自然图像纹理复杂的特点,提出了一种多种信息融合的有监督边界检测方法。首先,该方法在小样本的情况下,通过快速生成纹理基元特征来引入纹理信息;然后,根据图像中每个像素邻域内的灰度分布和纹理基元分布的差异来计算灰度梯度和纹理梯度,并在此基础上构造出二维的梯度特征向量;接着,用有监督的分类器进行分类,自适应地检测出初始的边缘点;最后,设计一个边界定位函数确定最终的边缘点,实现边界检测。实验结果表明,该算法运算速度较快,所检测的边界效果好。For natural images of complex texture,a supervised boundary detection method using the multi-information fusion was proposed.The texture information was introduced by quickly generating texton feature in the case of small sample.Intensity and texture gradients were further computed according to the differences of intensity and texton distributions within a pixel＇s neighborhood.In this way,a two-dimensional gradient feature vector was constructed,and a supervised classifier was used to adaptively detect original edge pixels.Finally,a boundary localization function was designed to determine the final edge pixels.The experimental results have demonstrated that the proposed method is faster and more effective.

关 键 词：小样本问题 边界检测 纹理基元 监督学习 分类器 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]