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JRM Vol.22 No.4 pp. 496-505
doi: 10.20965/jrm.2010.p0496
(2010)

Paper:

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Asbestos Detection in Building Materials Through Consolidation of Similarities in Color and Shape Features

Atsuo Nomoto*, Kazuhiro Hotta**, and Haruhisa Takahashi*

*The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

**Meijo University, 1-501 Shiogamaguchi, Tenpaku, Nagoya 468-850, Japan

Received:
December 16, 2009
Accepted:
March 29, 2010
Published:
August 20, 2010
Creative Commons License
Keywords:
asbestos, automatic counting, consolidation of kernel functions, microscope images, object detection
Abstract
As the general public has become increasingly aware of problems caused by asbestos, it is a pressing issue to check for the presence of asbestos fibers in buildings and elsewhere. In this paper, we propose a method using computers to detect and count asbestos particles in microscope images. Noting that asbestos particles have distinctive colors and shapes, we have extracted color and shape features to detect them. As detectors, we have used Support Vector Machine (SVM), which has better performance in utility than other classifiers. We do not consider it possible to properly utilize similarities in color and shape features if we input consolidated features of colors and shapes into a kernel function. We have therefore defined weighted summation of kernels applied to colors and shapes respectively. As a result, we have confirmed that the use of weighted summation of kernels can improve the precision of detection.
Cite this article as:
A. Nomoto, K. Hotta, and H. Takahashi, “Asbestos Detection in Building Materials Through Consolidation of Similarities in Color and Shape Features,” J. Robot. Mechatron., Vol.22 No.4, pp. 496-505, 2010.
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