Abstract:
In classification problems, when the number of examples per class is less
than or comparable to the dimension of the feature space, the performance of
statistical pattern recognition techniques tends to deteriorate. This
problem, called the ‘limited sample size problem’, is indeed quite common
nowadays, especially in image recognition applications. In this talk, I will
present a couple of ideas of using multivariate statistical classifiers to
identify and analyse the most discriminating hyper-planes separating two
populations. The goal is to analyse all the image features simultaneously
rather than segmented versions of the data separately, feature-by-feature,
or distinct models for texture and shape information. To demonstrate the
performance of these statistical pattern recognition approaches I show some
experimental results on medical data composed of 3D magnetic resonance
images and on frontal 2D face images.

Dr Carlos E. Thomaz
Head of the Image Processing Lab (IPL)
Dept of Electrical Engineering, Centro Universitario da FEI
Av. Humberto de Alencar Castelo Branco, 3972 - Sao Bernardo do Campo
Sao Paulo - Brazil
Office: K5-04
Mob: +55 (0)11 9138-0580 
Tel: +55 (0)11 4353-2910 (ext. 2209)
Fax: +55 (0)11 4109-5994
Email: cet@fei.edu.br
URL: http://www.fei.edu.br/~cet