自适应区域划分立体匹配算法

Adaptive Region Division Stereo Matching Algorithm

针对现有立体匹配算法在弱纹理区域及深度不连续区域匹配精度低的问题,提出一种基于自适应区域划分的立体匹配算法。首先,利用十字交叉域算法获取像素点臂长,计算像素变化率完成区域划分。然后,通过绝对差算法,改进Census变换和自适应加权梯度算子计算初始代价卷,利用十字交叉域进行代价聚合,对聚合后图像通过改进引导图滤波优化,使用赢者通吃策略筛选最优视差。最后,利用左右一致性检测、迭代区域投票、视差填充优化和中值滤波得到最终视差图。在Middlebury测试平台上测试结果表明,所提算法平均误差率为4.21%,能够有效提升在弱纹理区域及深度不连续区域的匹配精度。

Addressing the low matching accuracy of existing stereo matching algorithms in weak texture and depth discontinuous regions,a stereo matching algorithm based on adaptive region division is proposed.First,the cross domain algorithm is used to obtain the arm length of a pixel and calculate the pixel change rate to complete the region division.Then,the absolute difference algorithm,the improved Census transform,and the adaptive weighted gradient operator are used to calculate the initial cost volume which is aggregated by cross domain.The aggregated images are optimized by the improved guidance map filtering and the winner take all strategy is used to filter the optimal disparity.Finally,the final disparity map is obtained by using left and right consistency detection,iterative region voting,disparity filling optimization,and median filtering.The test results based on the Middlebury test platform show that the average error rate of the proposed algorithm is 4.21%,which is an effective matching accuracy improvement in terms of the weak texture and depthdiscontinuous regions.