Layered object recognition system using a hierarchical hybrid neural network architecture

Srinivasan Raghavan; Naresh Gupta; Barbara A. Lambird; David Lavine; Laveen N. Kanall

doi:10.1117/12.57112

1 February 1992 Layered object recognition system using a hierarchical hybrid neural network architecture

Srinivasan Raghavan, Naresh Gupta, Barbara A. Lambird, David Lavine, Laveen N. Kanall

Proceedings Volume 1609, Model-Based Vision Development and Tools; (1992) https://doi.org/10.1117/12.57112
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

A layered object recognition paradigm is described in this paper. The lower layers of the proposed system extracts rich feature information in the sense of a primal sketch including oriented edges, blobs, corners, and texture primitives from a raw image. The middle layers of the system extracts object parts such as faces, sides and adjacency relationships between them. The highest layers of the system use the information obtained beneath them to recognize the objects. The system consists of a combination of different types of neural networks making appropriate use of their different capabilities. That is, a collection of unsupervised neural networks are employed for generic feature extraction, while a similar collection of supervised networks are employed for learning object-specific shape information. We present some results of a partial implementation of this system.

Citation Download Citation

Srinivasan Raghavan, Naresh Gupta, Barbara A. Lambird, David Lavine, and Laveen N. Kanall "Layered object recognition system using a hierarchical hybrid neural network architecture", Proc. SPIE 1609, Model-Based Vision Development and Tools, (1 February 1992); https://doi.org/10.1117/12.57112

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available