We propose a novel feature extraction method and a cost function for stereo matching, that is robust and stable in matching images from different photographing conditions. Based on the Spearman rank correlation, for the pixel in the window centered around a certain pixel, the code is proposed as a rank sequence obtained from an ordered set of the pixel values. We also apply the same approach to X-gradient image and Y-gradient image. Finally, three rank sequences are obtained from the matching windows of original image, X-gradient image and Y-gradient image. They are used as the matching features of the center pixel. Then, the matching cost of two pixels will be defined as the weighted combination of the difference between their features. Our algorithm has a better matching effect than existing algorithms, especially in low-texture areas and object boundaries. We conducted experiments on Middlebury dataset that has different illumination and exposure images and KITTI dataset whose images were taken outdoor under radiometric distortions. The experimental results indicate that our algorithm is superior to the recently developed algorithms under radiometric variations, such as fuzzy encoding pattern1 and robust soft rank transform,3 whereas the speed is still fast.

中文翻译：

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立体声匹配的基于等级的编码功能

我们提出了一种新颖的特征提取方法和一种用于立体匹配的代价函数，该方法在匹配来自不同拍摄条件的图像时既鲁棒又稳定。基于Spearman等级相关性，对于以某个像素为中心的窗口中的像素，该代码被建议为从像素值的有序集合中获得的等级序列。我们还将相同的方法应用于X梯度图像和Y梯度图像。最后，从原始图像，X梯度图像和Y梯度图像的匹配窗口中获得了三个等级序列。它们用作中心像素的匹配特征。然后，将两个像素的匹配成本定义为其特征之间差异的加权组合。与现有算法相比，我们的算法具有更好的匹配效果，特别是在低纹理区域和物体边界处。我们对具有不同照明和曝光图像的Middlebury数据集以及在辐射失真下在室外拍摄的KITTI数据集进行了实验。实验结果表明，在诸如模糊编码模式1和鲁棒软秩变换3等辐射变化下，我们的算法优于最近开发的算法，但速度仍然很快。