This research investigates how changes in illumination, pose, and shape of an object produce changes in the observed images. The central idea is that the variability in the images of an object can be decomposed into component parts - illumination, pose, and shape - each of which, when analyzed separately, is well behaved. This paradigm, termed ``Image Variability Decomposition,'' differs substantially from the appearance-based paradigm in that by decomposing the variability, one is able to uncover generative structures to the set of images over each source of the variability. Thus, unlike appearance-based methods, it is not necessary to have seen the object under all of the seemingly infinite possible permutations of lighting conditions, pose, and shape. Instead, each component of the image variability is explicitly modeled. This research is applied to problems in which this variability plays an important role: face recognition, change detection, structure from motion, and visual tracking. The education component of this award focuses on laboratory and project intensive teaching. Students, both graduate and undergraduate, are not only taught about known unsolved problems, but are encouraged to consider posing new problems in computer vision, robotics, and pattern recognition. Students are also rewarded for finding new applications in new domains. The education activities will center around the construction of a computer vision laboratory within Yale University's newly formed Center for Computational Vision and Control, the design of laboratory intensive courses on both computer vision and pattern and object recognition, and the completion of a textbook on computer vision.
