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Proceedings Paper

A Multi-Sensor Robotics System For Object Recognition
Author(s): Khosrow M. Hassibi; Kenneth A. Loparo; Francis L. Merat
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Paper Abstract

An algorithm for object recognition based on the data from various contact and/or non-contact sensors is described. The sensors are primarily used for resolving the ambiguities which may be encountered in recognizing the object identity and its pose. The feature vector representing an object-state is partitioned into a finite number of feature subvectors each containing the features extracted from a different sensory source. To perform the recognition task through integration of data from different sensors, an a priori cost value is assigned to each feature. These feature costs and the object models are used for deriving a decision tree based on a sequential pattern, recognition approach. The decision tree guides the system during the recognition phase. General strategies for feature extraction from various sources are implemented as a finite state machine. A coordinator module supervises the coordination of manipulation and recognition processes and the execution of system state changes which are required to successfully implement the recognition algorithm.

Paper Details

Date Published: 27 March 1989
PDF: 8 pages
Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); doi: 10.1117/12.960283
Show Author Affiliations
Khosrow M. Hassibi, Case Western Reserve University (United States)
Kenneth A. Loparo, Case Western Reserve University (United States)
Francis L. Merat, Case Western Reserve University (United States)

Published in SPIE Proceedings Vol. 1002:
Intelligent Robots and Computer Vision VII
David P. Casasent, Editor(s)

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