Dynamic histogram warping of image pairs for constant image brightness

The constant image brightness (CIB) assumption assumes that the intensities of corresponding points in two images are equal. This assumption is central to much of computer vision. However, surprisingly little work has been performed to support this assumption, despite the fact the many of algorithms are very sensitive to deviations from CIB. Deviations from the CIB model have usually been modelled by a simple global spatially-invariant additive and/or multiplicative model of the form I L = I R +. An examination of the images contained in the SRI JISCT stereo database revealed that the constant image brightness assumption is indeed often false. Moreover , the simple additive/multiplicative models do not adequately represent the observed deviations. A comprehensive physical model of the observed deviations is diicult to develop. However, many potential sources of deviations might be represented by a non-linear monotonically increasing function of intensities. Under Universit e de Montr eal, D epartement d'informatique et de recherche op erationnelle C. 1 these conditions, we believe that an expansion/contraction matching of the intensity histograms represents the best method to both measure the degree of validity of the CIB assumption and correct for it. The dynamic histogram warping (DHW) is closely related to histogram speciication. However, it is shown that histogram speciication introduces artifacts that do not occur with dynamic histogram warping. Experimental results show that image histograms are closely matched after DHW, especially when both histograms are modiied simulatenously. DHW is also capable of removing simple constant additive and multiplicative biases without derivative operations, thereby avoiding ampliication of high frequency noise. It is demonstrated that DHW can improve the estimates from stereo and optical ow estimators.