Title :
Learning and vision machines
Author :
Heisele, Bernd ; Verri, Alessandro ; Poggio, Tomaso
Author_Institution :
Center for Biol. & Computational Learning, MIT, Cambridge, MA, USA
fDate :
7/1/2002 12:00:00 AM
Abstract :
The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this paper we review our approach to the problem of visual perception based on supervised learning. After a brief presentation of the theoretical background, we focus on some of the engineering applications of statistical learning to computer vision and discuss the main open problems and directions of our future research.
Keywords :
computer vision; learning (artificial intelligence); visual perception; computer vision; intelligence; learning; morphable models; object categorization; object detection; object recognition; pattern classification; statistical learning; supervised learning; support vector machines; visual learning; visual perception; Application software; Biological system modeling; Biology computing; Brain modeling; Computer vision; Machine learning; Neuroscience; Object recognition; Supervised learning; Visual perception;
Journal_Title :
Proceedings of the IEEE
DOI :
10.1109/JPROC.2002.801450
