Share Email Print

Proceedings Paper

Interpreting Segmented Laser Radar Images Using a Knowledge-Based System
Author(s): Chen-Chau Chu; N. Nandhakumar; J. K. Aggarwal
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

This paper presents a knowledge-based system (KBS) for man-made object recognition and image interpretation using laser radar (ladar) images. The objective is to recognize military vehicles in rural scenes. The knowledge-based system is constructed using KEE rules and Lisp functions, and uses results from pre-processing modules for image segmentation and integration of segmentation maps. Low-level attributes of segments are computed and converted to KEE format as part of the data bases. The interpretation modules detect man-made objects from the background using low-level attributes. Segments are grouped into objects and then man-made objects and background segments are classified into pre-defined categories (tanks, ground, etc.) A concurrent server program is used to enhance the performance of the KBS by serving numerical and graphics-oriented tasks for the interpretation modules. Experimental results using real ladar data are presented.

Paper Details

Date Published: 1 March 1990
PDF: 10 pages
Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); doi: 10.1117/12.969985
Show Author Affiliations
Chen-Chau Chu, University of Texas at Austin (United States)
N. Nandhakumar, University of Texas at Austin (United States)
J. K. Aggarwal, University of Texas at Austin (United States)

Published in SPIE Proceedings Vol. 1198:
Sensor Fusion II: Human and Machine Strategies
Paul S. Schenker, Editor(s)

© SPIE. Terms of Use
Back to Top