UC San Diego

Image analysis and image synthesis are the goals of computer vision and computer graphics, respectively. These research areas represent the domains into which the work presented in this dissertation fall. Specifically, we present work on three problems: segmentation and estimation of large disparity motion, simulating the reflectance for rough surfaces using microfacet models, and the perception of material reflectance. We present a novel framework for motion segmentation that combines the concepts of layer-based methods and feature-based motion estimation. We demonstrate our approach on image pairs containing large inter-frame motion and partial occlusion. The approach is efficient, and it successfully segments scenes with inter-frame disparities beyond the scope of previous methods. We also present an extension that accounts for the case of non-planar motion, and applications of our method to automatic object removal and to structure from motion. The Bidirectional Reflectance Distribution Function (BRDF) describes the way a surface reflects light. Microfacet reflectance models have been shown to work well for simulating the interaction of light with a rough surface. We give an overview of the existing techniques for reflection modeling and show how these techniques can be extended to handle refraction in a unified framework. To this end, two new derivations are presented for computing quantities required for refraction as well as a result that is (to our knowledge) previously unpublished. While BRDFs allows for a complete radiometric description of light reflecting from a surface, they are complex mathematical objects that can be difficult to use in practice. Our aim is to construct a low-dimensional, perceptual space for BRDFs that can be easily navigated. To this end, we design and carry out a comprehensive psychophysical study of the perception of measured reflectance. This is the largest study of its kind to date, and the first to use real material measurements. In addition, we introduce a new multidimensional scaling (MDS) algorithm for analyzing ordinal data that unlike existing methods is both efficient and optimal. We use the results of our study to construct a perceptual space of these BRDFs and introduce a new method for perceptual construction of novel BRDFs