改进非参数变换测度下的立体匹配  

Stereo Matching with Modified Non-parametric Transform Measure

作　　者：胡春海[1] 平兆娜[1] 郭士亮[1] 苏翔宇[1] 

机构地区：[1]燕山大学测试计量技术及仪器重点实验室,河北秦皇岛066004

出　　处：《光电工程》2014年第4期47-53,共7页Opto-Electronic Engineering

基　　金：河北省自然科学基金资助项目(F2011203117);河北省教育厅自然科学青年基金(2011137)

摘　　要：针对传统非参数变换测度的局限性,提出了一种基于局部纹理加权项的非参数Census变换测度,并使用半全局匹配法聚合代价的立体匹配算法。根据图像纹理度量的方向性,通过增加局部纹理反差值计算匹配窗口内所有像素的灰度均值,将其与反差值的加权和作为现匹配基元。使用半全局匹配法计算8邻域方向的匹配代价,以最小代价为匹配条件选取初始视差值。最后,利用图像分割法统计各分割区域的视差直方图,以直方图主峰所对应的视差值作为最终视差值。实验结果表明,该算法获得的视差精度优于当前多数局部算法,处理立体匹配中幅度失真的问题效果明显,能够很好地适应于真实场景测量。Due to the limitations of the traditional non-parametric transform measures, a stereo matching algorithm based on non-parametric transform measure with local texture weighted item and semi-global matching method to aggregate cost is proposed. According to the directivity of the image texture metric, a contrast value of local texture is added to calculate the grayscale mean of all of the pixels in the window. The mean and the local texture contrast value are weighted sum as new matching primitive. The matching cost is determined by using semi-global matching from 8 directions. It is subsequently optimized by minimum cost to gain initial disparity. Finally, the parallax histogram of each divided region is obtained through image segmentation based on mean-shift. Peak is selected as the final disparity of each region to obtain the dense disparity map. Experimental results show that the algorithm gets more accurate results than lots of the local algorithms. It is a good solution to the distortion problem and be well adapted to the measurement of the real scene

关 键 词：机器视觉 立体匹配 相似性测度 纹理度量 非参数变换 

分 类 号：TP394.41[自动化与计算机技术—计算机应用技术]