Department of Pattern Recognition

(eng): This paper presents a novel image-based registration method for high-resolution multi-view images of a planar material surface. Contrary to standard registration approaches, this method aligns images based on a true plane of the material's surface and not on a plane defined by registration marks. It combines the camera calibration and the iterative fitting of desired position and slant of the surface plane, image re-registration, and evaluation of the surface alignment. To optimize image compression performance, we use an error of a compression method as a function evaluating the registration quality. The proposed method shows encouraging results on example visualizations of view- and illumination-dependent textures. In addition to a standard multi-view data registration approach, it provides a better alignment of multi-view images and thus allows more detailed visualization using the same compressed parameterization size.