Topic
Markov random field
About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.
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TL;DR: In this paper, the authors proposed a general system that can extract and represent knowledge contained in EMRs to support three clinical decision support tasks (test recommendation, initial diagnosis, and treatment plan recommendation) given the condition of a patient.
33 citations
Book•
31 Jul 2001
TL;DR: This paper presents a meta-model for Bayesian Networks and Probabilistic Inference for Image Interpretation using Markov Random Field Models and its applications to Segmentation and Imageinterpretation.
Abstract: List of Figures. List of Tables. Preface. Acknowledgments. 1: Overview. 1. Introduction. 2. Image Interpretation. 3. Literature Review. 4. Approaches. 5. Layout of the Monograph. 2: Background. 1. Introduction. 2. Markov Random Field Models. 3. Multiresolution. 3: MRF Framework For Image Interpretation. 1. MRF on a Graph. 4. Bayesian Net Approach To Interpretation. 1. Introduction. 2. MRF model leading to Bayesian Network Formulation. 3. Bayesian Networks and Probabilistic Inference. 4. Probability Updating in Bayesian Networks. 5. Bayesian Networks for Gibbsian Image Interpretation. 6. Experimental Results. 7. Conclusions. 5: Joint Segmentation And Image Interpretation. 1. Introduction. 2. Image Interpretation using Integration. 3. The Joint Segmentation and Image Interpretation Scheme. 4. Experimental Results. 5. Conclusions. 6: Conclusions. Appendices: Appendix A. Bayesian Reconstruction. Appendix B. Proof of Hammersley-Clifford Theorem. Appendix C. Simulated Annealing Algorithm - Selecting Toin practise. Appendix D. Custom Made Pyramids. Appendix E. Proof of Theorem 4.6. Appendix F. k-means clustering. Appendix G. Features used in Image Interpretation. Appendix H. Knowledge Acquisition. Appendix I. HMM for Clique Functions. References. Index.
33 citations
TL;DR: In this paper, the authors define the notion of Markov strategy for the general optimal control problem where the index set is partially ordered and prove that the supremum over all strategies is always attained by a Markov Strategy if and only if the structure of the probability space is that of a markov random field.
Abstract: We define the notion of a Markov strategy for the general optimal control problem where the index set is partially ordered. We prove that the supremum over all strategies is always attained by a Markov strategy if and only if the structure of the probability space is that of a Markov random field.
33 citations
01 Sep 1986
TL;DR: This chapter shows how the MRF’s are used as texture image models, region geometry models, as well as edge models, and how they have been successfully used for image classification, surface inspection, image restoration, and image segmentation.
Abstract: This chapter deals with the problem of image modelling through the use of 2D Markov Random Field (MRF). The MRF’s are parametric models with a noncausal structure where the various dependencies over the plane is described in all directions. We first show how the MRF’s are used as texture image models, region geometry models, as well as edge models.Then we show how they have been successfully used for image classification, surface inspection, image restoration, and image segmentation.
33 citations
01 Jul 2000
TL;DR: The proposed method adopts a pattern recognition approach based on the extraction and the analysis of the cast shadows exhibited by each seabottom type, and is refined thanks to a Markov random field model which allows to incorporate spatial homogeneity properties one would expect for the final classification map.
Abstract: This paper proposes an original method for the classification of seafloors from high resolution sidescan sonar images. We aim at classifying the sonar images into five kinds of regions: sand, pebbles, rocks, ripples, and dunes. The proposed method adopts a pattern recognition approach based on the extraction and the analysis of the cast shadows exhibited by each seabottom type. This method consists of three stages of processing. First, the original image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each “object” lying on the seabed) and seabottom reverberation. Second, based on the extracted shadows, shape parameter vectors are computed on subimages and classified with a fuzzy classifier. This preliminary classification is finally refined thanks to a Markov random field model which allows to incorporate spatial homogeneity properties one would expect for the final classification map. Experiments on a variety of real high-resolution sonar images are reported.
33 citations