Random Field Models in Pattern Recognition and Image Processing

这项研究将进一步发展马尔可夫随机场(MRF)模型，用于图像处理、模式分析和用于决策的工程或科学数据分析。数学模型是领域相关的：离散的MRF用于图像网格，空间点过程用于特征空间中的模式，马尔可夫图用于邻近数据。研究人员将研究模型定义、采样、参数估计和拟合优度等基本问题，并具体应用于图像分割和恢复、纹理分析和合成、集群验证和多传感器融合。

This research will further develop Markov random field (MRF) models for image processing, pattern analysis, and engineering or scientific data analysis for decision making. The mathematical models are domain-dependent: discrete MRFs for image lattices, spatial point processes for patterns in a feature space, and Markov graphs for proximity data. The researchers will study fundamental problems of model definition, sampling, parameter estimation, and goodness of fit, with specific applications in image segmentation and restoration, texture analysis and synthesis, cluster validation, and multisensor fusion.