  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 without performance degradation, or even with
superior performance in some cases. 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.
