Local stereo matching using binary weighted normalized cross-correlation

Significant achievements have been attained in the field of dense stereo correspondence by local algorithms since the emergence of adaptive support weight by Yoon [1]. However, most algorithms suffer from photometric distortions and low-texture areas. In this paper, we present a novel stereo matching algorithm that can be sensitive to low-texture changes within support windows while keep insensitive to radiometric variations between left and right images. The algorithm performs Normalized Cross-Correlation with Binary Weighted support window (BWNCC) using k-nearest neighbors algorithm to resolve boundary problems. And, the proposed algorithm can be accelerated with transform domain convolution. We also propose to accelerate the BWNCC with transform domain computation. Experiment results confirm that the proposed method is robust, and has the comparable accuracy as the state-of-the-art.