Ninth International Conference in Computer Vision (ICCV), Nice 2003.
Short Course: Learning
and Inference in Vision: Generative Methods (3˝ hours).
Presenters: Bill Freeman (MIT
AI Laboratory) and Andrew Blake (Microsoft Research
(0) Intro – roadmap for learning and
inference in vision
(1) Bayesian inference introduction;
integration of sensory data
applications:
color constancy, Bayes Matte
(2) Learning and inference in temporal and
spatial Markov processes
Techniques:
2.1
PCA, FA, TCA:
inference – linear
(Wiener) filter
learning: by Expectation Maximization (EM);
(tutorial: EM for 2-line fitting)
applications: face simulation, denoising, Weiss’s intrinsic
images
and furthermore: Active Appearance Models,
Simoncelli,
2.2
Markov chain & HMM:
inference: - MAP by Dynamic Programming, Forward
and Forward-Backward (FB) algorithms;
learning: by EM – Baum-Welch;
representations: pixels, patches
applications: stereo vision
and furthermore: gesture models
(Bobick-Wilson)
2.3
AR models:
Inference: Kalman-Filter, Kalman
Smoother, Particle Filter;
learning: by EM-FB;
representations: patches, curves, chamfer maps, filter banks
applications: tracking (Isard-Blake,
Black-Sidenbladh, El Maraghi-Jepson-Fleet); Fitzgibbon-Soatto textures
and furthermore: EP
2.4
MRFs:
Inference: ICM, Loopy Belief Propagation (BP), Generalised BP, Graph Cuts;
Parameter learning: Pseudolikelihood
maximisation;
representations: color pixels, patches
applications: Texture segmentation, super resolution
(Freeman-Pasztor), distinguishing shading from paint
and furthermore: Gibbs sampling,
Discriminative Random Field (DRF)
2.5
Bayes network:
Inference:
Belief Propagation (BP)
Parameter learning: Pseudolikelihood
maximisation;
applications: scene context analysis: combine top
down with bottom up (Murphy et al)
2.6 Markov network:
Inference:
MCMC
applications: low level segmentation (Zhu et al.)
(3) Summary and finish
Biographies of Presenters.
Bill Freeman is an Associate Professor of Electrical
Engineering and Computer Science at the Computer Science and Artificial
Intelligence Laboratory (CSAIL) at MIT.
From 1992 - 2001 he worked at Mitsubishi Electric Research Labs (MERL),
and from 1981 - 1987, heworked at the Polaroid Corporation, both in
His current research interests include
machine learning applied to computer vision, Bayesian models of visual
perception, and interactive applications of computer vision. In 1997, he
received the Outstanding Paper prize at the Conference on Computer Vision and
Pattern Recognition for work on applying bilinear models to "separating
style and content". Previous research topics include steerable filters and
pyramids, the generic viewpoint assumption, color constancy, and computer
vision for computer games. He holds 22
patents.
Andrew Blake has served on
the on the faculty of Computer Science at the University of Edinburgh and as a
Royal Society Research Fellow from 1983-7 and then on the faculty of the
Department of Engineering Science in the University of Oxford, where he ran the
Visual Dynamics Research Group, became a Professor in 1996, and and was a Royal
Society Senior Research Fellow for 1998-9. In 1999 he moved to Microsoft
Research Cambridge as Senior Researcher working in Machine Learning and
Perception, while continuing with the
He has published several books including "Visual
Reconstruction" with A.Zisserman (MIT press), "Active Vision"
with Alan Yuille (MIT Press) and "Active Contours"
with Michael Isard (Springer-Verlag). He has twice won the prize of the
European Conference on Computer Vision, with R. Cipolla in 1992 and with M.
Isard in 1996, and was awarded the IEEE David Marr Prize (jointly with K.
Toyama) in 2001. He has served as programme chairman for the International
Conference on Computer Vision in 1995 and 1999, and is on the editorial boards
of the journals "Image and Vision Computing", the "International
Journal of Computer Vision" and "Computer Vision and Image
Understanding". He was elected a Fellow of the Royal Academy of Engineering
in 1998.