Interpreting Segmented Laser Radar Images Using a Knowledge-Based System

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.