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Green Image
Issue No. 09 - September (2010 vol. 32)
ISSN: 0162-8828
pp: 1646-1658
Til Aach , RWTH Aachen University, Aachen
Tobias Schuchert , Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe
Hanno Scharr , Forschungszentrum Jülich, Jülich
ABSTRACT
We extend estimation of range flow to handle brightness changes in image data caused by inhomogeneous illumination. Standard range flow computes 3D velocity fields using both range and intensity image sequences. Toward this end, range flow estimation combines a depth change model with a brightness constancy model. However, local brightness is generally not preserved when object surfaces rotate relative to the camera or the light sources, or when surfaces move in inhomogeneous illumination. We describe and investigate different approaches to handle such brightness changes. A straightforward approach is to prefilter the intensity data such that brightness changes are suppressed, for instance, by a highpass or a homomorphic filter. Such prefiltering may, though, reduce the signal-to-noise ratio. An alternative novel approach is to replace the brightness constancy model by 1) a gradient constancy model, or 2) by a combination of gradient and brightness constancy constraints used earlier successfully for optical flow, or 3) by a physics-based brightness change model. In performance tests, the standard version and the novel versions of range flow estimation are investigated using prefiltered or nonprefiltered synthetic data with available ground truth. Furthermore, the influences of additive Gaussian noise and simulated shot noise are investigated. Finally, we compare all range flow estimators on real data.
INDEX TERMS
Range flow, illumination changes, brightness constancy constraint, prefiltering, homomorphic filter, gradient constancy, structure tensor, 3D motion estimation.
CITATION
Til Aach, Tobias Schuchert, Hanno Scharr, "Range Flow in Varying Illumination: Algorithms and Comparisons", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 1646-1658, September 2010, doi:10.1109/TPAMI.2009.162
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