ALIGNMENT OF UNCALIBRATED IMAGES FOR MULTI-VIEW CLASSIFICATION

Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pair-wise similarity measure between images, where a common choice is the Euclidean distance. However, the accuracy of the Euclidean distance as a similarity measure is restricted to cases where images are captured from nearby viewpoints. In settings with large transfor-mations and viewpoint changes, alignment of images is necessary prior to distance computation. We propose a method for the registra-tion of uncalibrated images that capture the same 3D scene or object. We model the depth map of the scene as an algebraic surface, which yields a warp model in the form of a rational function between image pairs. The warp model is computed by minimizing the registration error, where the registered image is a weighted combination of two images generated with two different warp functions estimated from feature matches and image intensity functions in order to provide ro-bust registration. We demonstrate the flexibility of our alignment method by experimentation on several wide-baseline image pairs with arbitrary scene geometries and texture levels. Moreover, the results on multi-view image classification suggest that the proposed alignment method can be effectively used in graph-based classifica-tion algorithms for the computation of pairwise distances where it achieves significant improvements over distance computation with-out prior alignment. Index Terms — Image alignment, image registration, image warping, multi-view image classification, graph-based classification 1