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一种改进的多尺度多特征立体匹配算法
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Abstract:
在双目立体视觉领域中，立体匹配是其重要研究方向。为了针对部分立体匹配算法在弱纹理区域有较高的误匹配率的问题，本文提出一种基于多尺度多特征的立体匹配算法。将STAD、梯度与改进后的Census代价融合作为代价计算方法，代价聚合阶段，以引导图滤波算法为核心，利用跨尺度的思想将不同尺度的代价立方体进行融合，其中对于不同尺度的代价立方体设置了不同的代价聚合参数。对于视差图结果的一些错误，采用了多种视差后处理的方法。实验结果表明了该算法在弱纹理区域的准确性，对Middlebury3.0测试平台上标准图像对的实验结果表明，该算法在多组弱纹理图像上的平均误匹配率为8.16%，较传统的SGM等算法有更高的匹配精度。
In the field of binocular stereo vision, stereo matching is an important research direction. In order to solve the problem that some stereo matching algorithms have high error matching rate in the weak texture region, this paper proposes a stereo matching algorithm based on multi-scale and multi feature. The STAD, gradient and improved Census cost fusion are used as the cost computing method. In the cost aggregation stage, take the guided filtering algorithm as the core. The cost cubes of different scales are fused using the idea of cross scales, and different cost aggregation parameters are set for the cost cubes of different scales. For some errors in disparity map results, a variety of methods of disparity post-processing are used. The experimental results show the accuracy of the algorithm in the weak texture area. The experimental results of standard image pairs on the Mid-dlebury 3.0 test platform show that the average mismatch rate of the algorithm in multiple groups of weak texture images is 8.16%, which has higher matching accuracy than the traditional SGM and other algorithms.
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