Introduction to MAP Inference in Discrete Models Andrew Blake, Microsoft Research Cambridge Modern probabilistic modelling has revolutionized the design and implementation of machine vision systems. There are now numerous instances of systems that can see stereoscopically in depth, or separate foreground from background, or accurately pinpoint objects of a particular class, all in real time. The underlying advances owe a lot to probabilistic frameworks for inference in images. In particular, the Markov Random Field (MRF), borrowed originally from statistical physics, first appeared in image processing in the 70s. It has staged a resounding comeback in the last decade, for very interesting reasons. Lecturers: Pushmeet Kohli, Microsoft Research Cambridge M. Pawan Kumar, Stanford University Carsten Rother, Microsoft Research Cambridge
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ICCV2009: MAP Inference in Discrete Models: Part 1: Introduction
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Introduction to MAP Inference in Discrete Models
Andrew Blake, Microsoft Research Cambridge
Modern probabilistic modelling has revolutionized the design and
implementation of machine vision systems. There are now numerous instances
of systems that can see stereoscopically in depth, or separate foreground from
background, or accurately pinpoint objects of a particular class, all in real time.
The underlying advances owe a lot to probabilistic frameworks for inference in
images. In particular, the Markov Random Field (MRF), borrowed originally
from statistical physics, first appeared in image processing in the 70s. It has
staged a resounding comeback in the last decade, for very interesting reasons.
Lecturers:
Pushmeet Kohli, Microsoft Research Cambridge
M. Pawan Kumar, Stanford University
Carsten Rother, Microsoft Research Cambridge
Course programme
9.30-10.00 Introduction (Andrew Blake)
10.00-11.00 Discrete Models in Computer Vision (Carsten Rother)