{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/76087"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/76087","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Advanced spatial information processes: modeling and application","abstract":"Making full use of spatial information is an important problem in information-processing and decision making. In this dissertation, two Bayesian decision theoretic frameworks for context classification are developed which make full use of spatial information. The first framework is a new multispectral image context classification technique which is based on a recursive algorithm for optimal estimation of the state of a two-dimensional discrete Markov Random Field (MRF). The implementation of the recursive algorithm is a form of dynamic programming. The second framework is based on a stochastic relaxation algorithm and Markov-Gibbs Random Fields. The relaxation algorithm constitutes an optimization using annealing. We also discuss how to estimate the Markov Random Field Model parameters, which is a key problem in using MRF in image processing and pattern recognition. The estimation of transition probabilities in a 2-D MRF is converted into two 1-D estimation problems. Then a Space-varying estimation method for transition probabilities is discussed.","abstract_html":"Making full use of spatial information is an important problem in information-processing and decision making. In this dissertation, two Bayesian decision theoretic frameworks for context classification are developed which make full use of spatial information. The first framework is a new multispectral image context classification technique which is based on a recursive algorithm for optimal estimation of the state of a two-dimensional discrete Markov Random Field (MRF). The implementation of the recursive algorithm is a form of dynamic programming. The second framework is based on a stochastic relaxation algorithm and Markov-Gibbs Random Fields. The relaxation algorithm constitutes an optimization using annealing. We also discuss how to estimate the Markov Random Field Model parameters, which is a key problem in using MRF in image processing and pattern recognition. The estimation of transition probabilities in a 2-D MRF is converted into two 1-D estimation problems. Then a Space-varying estimation method for transition probabilities is discussed.","abstract_has_math":false,"creators":["Zhang, Mingchuan"],"institution":"Virginia Polytechnic Institute and State University","degree_name":"Ph. 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