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Proceedings Paper

Hybrid machine vision method for autonomous guided vehicles
Author(s): Jian Lu; Kyoko Hamajima; Koji Ishihara
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Paper Abstract

As a prospective intelligent sensing method for Autonomous Guided Vehicle (AGV), machine vision is expected to have balanced ability of covering a large space and also recognizing details of important objects. For this purpose, the proposed hybrid machine method here combines the stereo vision method and the traditional 2D method. The former implements coarse recognition to extract object over a large space, and the later implement fine recognition about some sub-areas corresponding to important and/or special objects. This paper is mainly about the coarse recognition. In order to extract objects in the coarse recognition stage, the disparity image calculated according to stereo vision principle is segmented by two consequent steps of region expansion and convex split. Then the 3D measurement about the rough positions and sizes of extracted objects is performed according to the disparity information of the corresponding segmentation, and is used for recognizing the objects' attributes by means of pattern learning/recognition. The attribute information resulted is further used to assist fine recognition in the way of performing gaze control to input suitable image of the interested objects, or to directly control AGV's travel. In our example AGV application, some navigation-signs are introduced to indicate the travel route. When the attribute shows that the object is a navigation-sign, the 3D measurement is used to gaze the navigation-sign, in order for the fine recognition to analyze the specific meaning by means of traditional 2D method.

Paper Details

Date Published: 22 May 2003
PDF: 10 pages
Proc. SPIE 5011, Machine Vision Applications in Industrial Inspection XI, (22 May 2003); doi: 10.1117/12.474027
Show Author Affiliations
Jian Lu, National Institute of Industrial Safety (Japan)
Kyoko Hamajima, National Institute of Industrial Safety (Japan)
Koji Ishihara, Japan Science and Technology Corp. (Japan)


Published in SPIE Proceedings Vol. 5011:
Machine Vision Applications in Industrial Inspection XI
Martin A. Hunt; Jeffery R. Price, Editor(s)

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