A Neural Architecture for Learning, Detecting and Recognizing Objects

Tuesday, November 14, 2000 - 2:00pm - 3:00pm
Keller 3-180
Yali Amit (University of Chicago)
I will present a neural architecture based on simple binary neurons which uses field dependent Hebbian learning to train object models and classifiers. The models are used to drive detection, and the classifiers for recognition; all are integrated into one architecture.

The object models as well as the classifiers are based on a family of binary local features with hard wired invariance to contrast changes and local geometric deformations. Recognition among several object classes is obtained through a vote among a large number of randomized perceptrons based on these binary features. When a particular object model is evoked in a central module, detection in the entire visual scene, and at a range of poses, is obtained through top-down priming of particular retinotopic detectors of the features. Some analogies to well known experiments on object detection and recognition in primates will be discussed.

This is joint work with Massimo Mascaro.