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JRM Vol.22 No.4 pp. 506-513
doi: 10.20965/jrm.2010.p0506
(2010)

Paper:

Automatic Counting Robot Development Supporting Qualitative Asbestos Analysis -Asbestos, Air Bubbles, and Particles Classification Using Machine Learning-

Kenichi Ishizu*1, Hiroshi Takemura*1, *2, Kuniaki Kawabata*2,
Hajime Asama*2, *3, Taketoshi Mishima*2, *3, *4,
and Hiroshi Mizoguchi*1, *2

*1Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba 278-8510, Japan

*2Kawabata Intelligent System Research Unit, RIKEN

*3RACE, The University of Tokyo

*4Department of Information and Computer Science, Saitama University

Received:
December 20, 2009
Accepted:
April 22, 2010
Published:
August 20, 2010
Keywords:
asbestos, microscopic observation, qualitative analysis, machine learning
Abstract

Asbestos, particle, and air bubble counting generally supports qualitative asbestos analysis, using such procedures as dispersion staining. Operators conventionally check and count asbestos fibers visually using a microscope – a difficult, time-consuming process. The microscopic observation robot we are automating to support qualitative asbestos analysis images fibers and saves them automatically to a database. In this paper, we introduce image processing method using machine learning to count asbestos, particles, and air bubbles automatically.

Cite this article as:
Kenichi Ishizu, Hiroshi Takemura, Kuniaki Kawabata,
Hajime Asama, Taketoshi Mishima, and
and Hiroshi Mizoguchi, “Automatic Counting Robot Development Supporting Qualitative Asbestos Analysis -Asbestos, Air Bubbles, and Particles Classification Using Machine Learning-,” J. Robot. Mechatron., Vol.22, No.4, pp. 506-513, 2010.
Data files:
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