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About Markov Random Field Models in Computer Vision: A Posteriori
This document discusses Markov Random Field (MRF) models in computer vision. MRF models provide a unified approach for modeling both low-level and high-level computer vision problems. MRF theory allows contextual constraints to be encoded into the prior probability in a Bayesian framework. The optimal solution to vision problems posed as Bayesian labeling problems is defined as the maximum a posteriori probability estimate. Recent advances have enabled the formulation of high-level problems like object matching and recognition within a single Bayesian framework based on MRF models, providing a systematic approach for vision modeling based on mathematical principles.
Detailed Information
| Author: | ['desyagustinn'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 2 |
| Language: | English |
| Format: | |
| Price: | FREE |
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