Readership: Graduate students in biomedical engineering, mathematics, computer science and electrical engineering with a good background in mathematics and probability.
Ulf Grenander, Brown University, and Michael I. Miller, Johns Hopkins
1: Introduction 2: The Bayes paradigm, estimation and information measures 3: Probabilistic directed acyclic graphs and their entropies 4: Markov random fields on undirected graphs 5: Gaussian random fields on undirected graphs 6: The canonical representations of general pattern theory 7: Matrix group actions transforming patterns 8: Manifolds, active modes, and deformable templates 9: Second order and Gaussian fields 10: Metrics spaces for the matrix groups 11: Metrics spaces for the infinite dimensional diffeomorphisms 12: Metrics on photometric and geometric deformable templates 13: Estimation bounds for automated object recognition 14: Estimation on metric spaces with photometric variation 15: Information bounds for automated object recognition 16: Computational anatomy: shape, growth and atrophy comparison via diffeomorphisms 17: Computational anatomy: hypothesis testing on disease 18: Markov processes and random sampling 19: Jump diffusion inference in complex scenes
