Cutset Sampling and Processing

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PROJECT ABSTRACTCutset Sampling and ProcessingD.L. Neuhoff (P.I.)This research project is focused on the development of efficient new methods for sampling andprocessing multidimensional data such as images, video, or spatially distributed sensor data,especially the first and last. Traditional methods of digital image processing rely on the takingand processing of regularly spaced samples (pixels), typically at the sites of a lattice, for examplea square grid of points. In contrast, this project is investigating digital image processingbased on samples taken in a cutset, the simplest example of which is a square grid of lines thatspans the image. More generally a cutset is defined in terms of a set of neighborhood relationsamong a discrete set of potential sample sites. Cutset sampling is expected to be especiallyeffective when the capturing of image edge information is important.The principle motivations for cutset sampling derive from the facts that two-dimensional datacan often be well modeled by Markov random fields and that unsampled pixels of Markovrandom fields can be efficiently estimated with belief propagation algorithms. This researchproject is investigating different forms of cutset sampling, and it is developing reconstructionalgorithms tailored to cutset sampling, image compression methods based on cutsets, distributedsensor network algorithms for scenarios in which sensors are deployed on cutsets, and analysistechniques to determine the strengths and weakness of cutset sampling, and to enable comparisonswith conventional lattice-based sampling.