Hierarchical Architecture for View-Based Object Recognition in Cluttered Scenes

University Of California-San Diego, La Jolla CA

Investigators

Abstract

For computers and robots to live up to their full potential as human assistants, they must be able to reliably perceive humans and objects in their environment. This project addresses the recognition of objects in complex everyday scenes (e.g.\ office, household, or traffic scenes). Biological vision systems have successfully solved the vision problem and the philosophy of this project is to try to extract principles from the information processing in the primate visual system and to apply them in the design of object recognition algorithms. The goal is to understand how object recognition in complex environments can be achieved in a hierarchical architecture that mimics the layout of the object recognition pathway in the primate brain, and to build a demonstration system capable of recognizing a large number of objects in complex everyday scenes. In particular, this project will focus on three processing principles: hierarchical representations, massive feedback, and active scene analysis. If successful, the project will further our understanding of how objects can be recognized using hierarchical view based object representations, how these representations can be learned from unsegmented training images, and how feedback can aid recognition in this kind of hierarchical recognition architecture. This will potentially open a range of new application areas for computer vision systems and may also lead to a better understanding of object recognition in the brain.