title: Image representations for visual learning.
abstract: Computer vision researchers are developing new approaches to object recognition and detection that are based almost directly on images and avoid the use of intermediate three-dimensional models. Many of these techniques depend on a representation of images that induce a linear vector space structure and in principle requires dense feature correspondence. This image representation allows the use of learning techniques for the analysis of images (for computer vision) as well as for the synthesis of images (for computer graphics).
authors.affiliation: Department of Brain and Cognitive Science, Center for Biological and Computational Learning (CBCL) and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge 02142, USA.
authors.forename: D
authors.initials: D
authors.lastname: Beymer
authors.forename: T
authors.initials: T
authors.lastname: Poggio
mesh_terms: D001185:Artificial Intelligence; D003196:Computer Graphics; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted; D010363:Pattern Recognition, Automated