Speaker: Ross Whitaker, Scientific Computing and Imaging Institute, University of Utah Title: Adaptive Markov Models with Information-Theoretic Methods for Image Analysis Low-level problems in image processing, such as denoising, reconstruction, and segmentation typically require some kind of model of image structure. Thus, the modeling images in a general, yet tractable manner remains an important area of research. Most image processing algorithms make strong geometric or statistical assumptions about the properties of the signal and/or noise. Therefore, they lack the generality to be easily applied to new applications or diverse image collections. This talk presents an adaptive Markov model of images that allows algorithms to automatically learn the local statistical dependencies of image neighborhoods. Probability densities for neighborhoods are estimated nonparametrically, through a kernel-based strategy, and thus, image statistics are captured through large sets of examples of image neighborhoods. We use this strategy to create adaptive algorithms for low-level image processing. We incorporate prior information, when available, using optimal Bayesian formulations. We enforce optimality criteria based on fundamental information-theoretic concepts that capture the functional dependence, information content, and uncertainty in the data. This talk presents examples of the application of this strategy to denoising, reconstruction, and segmentation.