Abstract:
Robust stereo and optical flow disparity matching is essential for
computer vision applications with varying illumination conditions. Most robust
disparity matching algorithms rely on computationally expensive normalized
variants of the brightness constancy assumption to compute the matching
criterion. In this paper, we reinvestigate the removal of global and large area
illumination artifacts, such as vignetting, camera gain, and shading reflections,
by directly modifying the input images. We show that this significantly
reduces violations of the brightness constancy assumption, while maintaining the
information content in the images. In particular, we define metrics and perform a
methodical evaluation to firstly identify the loss of information in the images, and
secondly determine the reduction of brightness constancy violations. Thirdly, we
experimentally validate that modifying the input images yields robustness against
illumination artifacts for optical flow disparity matching.
Description:
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