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

Kenichi Ishizu; Hiroshi Takemura; Kuniaki Kawabata; Hajime Asama; Taketoshi Mishima; Hiroshi Mizoguchi

doi:10.20965/jrm.2010.p0506

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JRM Vol.22 No.4 pp. 506-513

doi: 10.20965/jrm.2010.p0506

(2010)

Paper:

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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:

K. Ishizu, H. Takemura, K. Kawabata, <. Asama, T. Mishima, and <. 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.

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References

[1] T. Murakami, “A study of quantitative prediction of asbestos pollution risks for the future,” Research on Environmental Disruption, Vol.32, pp. 31-38, 2002. (in Japanese)
[2] JIS (Japanese Industrial Standard) A1481,2006(J), “Determination of Asbestos in Building Material Products,” 2006.
[3] L. C. Kenny, “Asbestos fibre counting by image analysis – the performance of Manchester asbestos program on Magiscan,” Anm Occup Hyg, Vol.28, No.4, pp. 401-415, 1984.
[4] P. A. Baron and S. A. Shulman, “Evaluation of the Magiscan image analyzer for asbestos fiber counting,” Am Ind Hyg Assoc J., Vol.48, No.1, pp. 39-46, 1987.
[5] Y. Inoue, A. Kaga, K. Yamaguchi, “Development of an automatic system for counting asbestos fibers using image processing,” Paticul Sci Technol, Vol.16, No.4, pp. 263-279, 1998.
[6] K. Kawabata, S. Morishita, H. Takemura, K. Hotta, T. Mishima, H. Asama, H. Mizoguchi, and H. Takahashi, “Development of an Automated Microscope for Supporting Qualitative Asbestos Analysis by Dispersion Staining,” J. of Robotics and Mechatronics, Vol.21, No.2, pp. 186-192, 2009.
[7] H. Kumagai, S. Morishita, K. Kawabata, H. Asama, and T. Mishima, “Accuracy Improvement of Counting Asbestos in Particles using a Noise Redacted Background Subtraction,” Proc. of IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems, pp. 74-79, 2008.
[8] T. Watanabe, S. Morishita, K. Kawabata, H. Asama, and T. Mishima, “Resolution Dependency of a Particle Detection Method in Microscopy Images for Asbestos Qualitative Analysis,” SSI2008, pp. 319-322, 2008. (in Japanese)
[9] H. Kuba, K. Hotta, and H. Takahashi, “Automatic Particle Detection and Counting By One-Class SVM From Microscope Image,” Proc. of Int. Conf. on Neural Information Processing, pp. 361-368, 2008.
[10] A. Nomoto, K. Hotta, and H. Takahashi, “An Asbestos Counting Method from Microscope Image of Building Materials Using Summation Kernel of Color and Shape,” Proc. of Int. Conf. on Neural Information Processing, pp. 671-678, 2008.
[11] Y. Moriguchi, K. Hotta, and H. Takahashi, “An Asbestos Detection Method From Microscope Image Using Support Vector Random Field of Local Color Features,” IEEJ Trans. EIS, Vol.129, No.5, pp. 818-823, 2009.
[12] J. A. Mclaughlin and J. Raviv, “N-th-order autocorrelations in pattern recognition,” Information and Control, Vol.12, pp. 121-142, 1968.
[13] N. Otsu and T. Kurita, “A new scheme for practical flexible and intelligent vision systems,” Proc. IAPR Workshop on Computer Vision, pp.431-435, 1988.
[14] V. N. Vapnik, “An overview of statistical learning theory,” Neural Networks, IEEE Trans. on, Vol.10, No.5, pp. 988-999, 1999.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] T. Murakami, “A study of quantitative prediction of asbestos pollution risks for the future,” Research on Environmental Disruption, Vol.32, pp. 31-38, 2002. (in Japanese)

[2] [2] JIS (Japanese Industrial Standard) A1481,2006(J), “Determination of Asbestos in Building Material Products,” 2006.

[3] [3] L. C. Kenny, “Asbestos fibre counting by image analysis – the performance of Manchester asbestos program on Magiscan,” Anm Occup Hyg, Vol.28, No.4, pp. 401-415, 1984.

[4] [4] P. A. Baron and S. A. Shulman, “Evaluation of the Magiscan image analyzer for asbestos fiber counting,” Am Ind Hyg Assoc J., Vol.48, No.1, pp. 39-46, 1987.

[5] [5] Y. Inoue, A. Kaga, K. Yamaguchi, “Development of an automatic system for counting asbestos fibers using image processing,” Paticul Sci Technol, Vol.16, No.4, pp. 263-279, 1998.

[6] [6] K. Kawabata, S. Morishita, H. Takemura, K. Hotta, T. Mishima, H. Asama, H. Mizoguchi, and H. Takahashi, “Development of an Automated Microscope for Supporting Qualitative Asbestos Analysis by Dispersion Staining,” J. of Robotics and Mechatronics, Vol.21, No.2, pp. 186-192, 2009.

[7] [7] H. Kumagai, S. Morishita, K. Kawabata, H. Asama, and T. Mishima, “Accuracy Improvement of Counting Asbestos in Particles using a Noise Redacted Background Subtraction,” Proc. of IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems, pp. 74-79, 2008.

[8] [8] T. Watanabe, S. Morishita, K. Kawabata, H. Asama, and T. Mishima, “Resolution Dependency of a Particle Detection Method in Microscopy Images for Asbestos Qualitative Analysis,” SSI2008, pp. 319-322, 2008. (in Japanese)

[9] [9] H. Kuba, K. Hotta, and H. Takahashi, “Automatic Particle Detection and Counting By One-Class SVM From Microscope Image,” Proc. of Int. Conf. on Neural Information Processing, pp. 361-368, 2008.

[10] [10] A. Nomoto, K. Hotta, and H. Takahashi, “An Asbestos Counting Method from Microscope Image of Building Materials Using Summation Kernel of Color and Shape,” Proc. of Int. Conf. on Neural Information Processing, pp. 671-678, 2008.

[11] [11] Y. Moriguchi, K. Hotta, and H. Takahashi, “An Asbestos Detection Method From Microscope Image Using Support Vector Random Field of Local Color Features,” IEEJ Trans. EIS, Vol.129, No.5, pp. 818-823, 2009.

[12] [12] J. A. Mclaughlin and J. Raviv, “N-th-order autocorrelations in pattern recognition,” Information and Control, Vol.12, pp. 121-142, 1968.

[13] [13] N. Otsu and T. Kurita, “A new scheme for practical flexible and intelligent vision systems,” Proc. IAPR Workshop on Computer Vision, pp.431-435, 1988.

[14] [14] V. N. Vapnik, “An overview of statistical learning theory,” Neural Networks, IEEE Trans. on, Vol.10, No.5, pp. 988-999, 1999.

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

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