EM-Markov Random Field-ICM Image Segmentation
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Resource Overview
Image segmentation program based on EM-Markov Random Field-ICM framework, providing excellent learning code with comprehensive algorithm implementations
Detailed Documentation
This image segmentation program utilizes the EM algorithm combined with Markov Random Field modeling and ICM optimization techniques. The Expectation-Maximization (EM) algorithm handles parameter estimation for statistical modeling, while Markov Random Fields capture spatial relationships between image pixels. The Iterated Conditional Modes (ICM) algorithm performs iterative optimization to refine segmentation results. The implementation demonstrates practical integration of these algorithms, featuring key functions for probability distribution estimation, neighborhood system modeling, and energy minimization. This serves as an excellent educational resource for understanding both the theoretical principles and practical implementation of advanced image segmentation algorithms, including parameter initialization methods, convergence criteria, and post-processing techniques for segmentation refinement.
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