Supervised Learning for Computer Vision Applications

Paul Viola

Microsoft Research

Focusing on practical applications, I will describe several algorithms for 
solving computer vision problems using machine learning.  I will include a 
discussion of perceptrons, kernel machines, support vector machines, adaboost, 
cascaded classifiers, and memory based classifiers. Applications include face 
and pedestrian detection, as well as face and character recognition.

Bio: Before moving to Microsoft Paul Viola was a researcher at MERL and an 
Associate Professor of Computer Science and Engineering at the Massachusetts 
Institute of Technology.  He also spent two years as a visiting scientist in the 
Computational Neurobiology of the Salk Institute in San Diego.  Paul has a broad 
background in advanced computational techniques, publishing in the fields of 
computer vision, neurobiological vision, medical imaging, mobile robotics, 
machine learning, and automated drug design. Paul was a recipient of a National 
Science Foundation Career award in 1998. He has worked on research and 
development with a number of companies including:  Compaq, IBM Research, Arris 
Pharmaceuticals and Intarka.