In this paper, a multi-scale slanted O(1) stereo (MSOS) matching algorithm is proposed. In the proposed MSOS, the concept of multi-scale propagation is introduced to handle the problem of different object size in practical applications. Moreover, the census feature, which is more robust than the original sum of absolute difference (SAD), is employed in the proposed MSOS to compensate the effect of practical situations such as radiometric change. Experimental results show that the matching performance of the proposed MSOS is greatly improved over the original SOS on the outdoor KITTI2015 dataset.

In this paper, the impact of demosaicing on gradient extraction is studied and a gradient-based feature extraction pipeline based on raw Bayer pattern images is proposed. It is shown both theoretically and experimentally that the Bayer pattern images are applicable to the central difference gradient-based feature extraction algorithms with negligible performance degradation, as long as the arrangement of color filter array (CFA) patterns matches the gradient operators. The color difference constancy assumption, which is widely used in various demosaicing algorithms, is applied in the proposed Bayer pattern image-based gradient extraction pipeline. Experimental results show that the gradients extracted from Bayer pattern images are robust enough to be used in histogram of oriented gradients (HOG)-based pedestrian detection algorithms and shift-invariant feature transform (SIFT)-based matching algorithms. By skipping most of the steps in the image signal processing (ISP) pipeline, the computational complexity and power consumption of a computer vision system can be reduced significantly.