Image interpretation using multiple sensing modalities
Chu, C.-C.
Aggarwal, J.K.
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Aug 1992
Volume: 14,
Issue: 8
On page(s): 840-847
ISSN: 0162-8828
References Cited: 24
CODEN: ITPIDJ
INSPEC Accession Number: 4246276
Digital Object Identifier: 10.1109/34.149595
Current Version Published: 2002-08-06
Abstract
The AIMS (automatic interpretation using multiple sensors) system,
which uses registered laser radar and thermal imagers, is discussed. Its
objective is to detect and recognize man-made objects at kilometer range
in outdoor scenes. The multisensor fusion approach is applied to four
sensing modalities (range, intensity, velocity, and thermal) to improve
both image segmentation and interpretation. Low-level attributes of
image segments (regions) are computed by the segmentation modules and
then converted to the KEE format. The knowledge-based interpretation
modules are constructed using KEE and Lisp. AIMS applies forward
chaining in a bottom-up fashion to derive object-level interpretations
from databases generated by the low-level processing modules. The
efficiency of the interpretaton process is enhanced by transferring
nonsymbolic processing tasks to a concurrent service manager (program).
A parallel implementation of the interpretation module is reported.
Experimental results using real data are presented
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