Slide 1580.691 Learning Theory Reza Shadmehr Bayesian learning 1: Bayes rule, priors and maximum a posteriori Slide 2 Frequentist vs. Bayesian Statistics Frequentist Thinking…

Slide 1Basic Steps 1.Compute the x and y image derivatives 2.Classify each derivative as being caused by either shading or a reflectance change 3.Set derivatives with the…

1.12 Recent Advances in Synthetic Aperture Radar Enhancement and Information Extraction Dušan Gleich and Žarko Čučej University in Maribor, Faculty of Electrical Engineering…

1. Machine Learning for Data Mining Maximum A Posteriori (MAP) Andres Mendez-Vazquez June 23, 2015 1 / 66 2. Outline 1 Introduction A first solution Example Properties of…

Slide 1 1 On the Statistical Analysis of Dirty Pictures Julian Besag Slide 2 2 Image Processing Required in a very wide range of practical problems  Computer vision …

Slide 1 GIS Tutorial 1 Lecture 6 Digitizing Slide 2 Outline  Digitizing overview  GIS features  Digitizing features  Advanced digitizing tools  Spatial adjustments…

Overview Problem: entity extraction from (remote sensing) images. Need for prior ‘shape’ knowledge. Modelling prior shape knowledge: higher-order active contours (HOACs).…

Ibrahim Hoteit Examples of Four-Dimensional Data Assimilation in Oceanography University of Maryland October 3, 2007 Outline 4D Data Assimilation 4D-VAR and Kalman Filtering…

Markov Random Fields Allows rich probabilistic models for images. But built in a local, modular way. Learn local relationships, get global effects out. MRF nodes as pixels…

Ibrahim Hoteit Examples of Four-Dimensional Data Assimilation in Oceanography University of Maryland October 3, 2007 Outline 4D Data Assimilation 4D-VAR and Kalman Filtering…