激光与光电子学进展, 2019, 56 (10): 101502, 网络出版: 2019-07-04
基于高斯拉普拉斯算子的加权引导图滤波立体匹配算法 下载: 900次
Stereo-Matching Algorithm Using Weighted Guided Image Filtering Based on Laplacian of Gaussian Operator
机器视觉 立体匹配 加权绝对差 高斯拉普拉斯算子 引导图滤波 machine vision stereo matching weighted absolute difference Laplacian of Gaussian operator guided image filtering
摘要
提出了一种基于高斯拉普拉斯(LoG)算子的加权引导图滤波立体匹配算法。采用加权绝对差与梯度相融合的方式计算匹配代价。利用基于LoG算子的引导图滤波进行代价聚合,使惩戒参数能够随图像中的不同纹理区域进行自适应调整。使用赢者通吃(WTA)策略计算视差,并采用二次插值法得到最终的视差图。对Middlebury平台中的图像进行测试,结果表明,所提算法的平均误匹配率为4.32%。该算法可以有效地兼顾图像的高纹理区域和低纹理区域,并降低了视差图的误匹配率。
Abstract
A stereo-matching algorithm using weighted guided image filtering based on the Laplacian of Gaussian (LoG) operator is proposed. The algorithm calculates matching cost by fusing weighted absolute difference and gradient. Then, cost aggregation is implemented using an improved guided image filtering based on the LoG operator to ensure that the penalty parameter is self-adaptive. The disparity computation is implemented using the winner-take-all (WTA) strategy, and the final disparity map is obtained using two different interpolation methods. The experimental results show that the average mismatch rate of the proposed algorithm on the Middlebury benchmark standard dataset is 4.32%. The proposed algorithm can process both high and low texture regions effectively; thus, the mismatch rate of the disparity map is reduced.
周博, 秦岭, 龚伟. 基于高斯拉普拉斯算子的加权引导图滤波立体匹配算法[J]. 激光与光电子学进展, 2019, 56(10): 101502. Bo Zhou, Ling Qin, Wei Gong. Stereo-Matching Algorithm Using Weighted Guided Image Filtering Based on Laplacian of Gaussian Operator[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101502.