Effective shape-based retrieval and classification of mammograms
Proceedings of the 2006 ACM symposium on Applied computing
Topological triangle characterization with application to object detection from images
Image and Vision Computing
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Model-based adaptive spatial sampling for occurrence map construction
Statistics and Computing
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In this paper we present new results relative to the “expectation-maximization/maximization of the posterior marginals” (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured images. The EM/MPM algorithm uses a Markov random field model for the pixel class labels and alternately approximates the MPM estimate of the pixel class labels and estimates parameters of the observed image model. The goal of the EM/MPM algorithm is to minimize the expected value of the number of misclassified pixels. We present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal, to the extent that the EM estimates of the model parameters are close to the true values of the model parameters. We also present new experimental results demonstrating the performance of the EM/MPM algorithm