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The interpretation of laser radar images by a knowledge-based system

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Abstract

This paper presents a knowledge-based system to interpret laser radar (ladar) images. The objective of this research is to detect and recognize man-made objects in outdoor scenes. Our system applies themultisensor fusion approach to multiple ladar modalities to improve both segmentation and interpretation. The segmentation modules are written in C. The knowledge-based interpretation system is constructed usingKEE and Lisp. Low-level attributes of image segments (regions) are computed by the segmentation modules and then converted to theKEE format. The interpretation system applies forward chaining in a bottom-up fashion to derive object-level interpretation from input generated by low-level processing and segmentation modules. The interpretation modules detect man-made objects from the background using low-level attributes. Segments are grouped into objects, which are then classified into predefined categories (vehicles, ground, etc.). The efficiency of the interpretation system is enhanced by transferring nonsymbolic processing tasks to a concurrent service manager (program). Experimental results using ladar data are presented.

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Authors and Affiliations

  1. Computer and Vision Research Center, University of Texas at Austin, Engineering Science Building, ENS-520, 78712, Austin, TX, USA

    Chen-Chau Chu &  J. K. Aggarwal

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  1. Chen-Chau Chu
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  2. J. K. Aggarwal
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Chu, CC., Aggarwal, J.K. The interpretation of laser radar images by a knowledge-based system. Machine Vis. Apps. 4, 145–163 (1991). https://doi.org/10.1007/BF01230198

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