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
-  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)
-  JIS (Japanese Industrial Standard) A1481,2006(J), “Determination of Asbestos in Building Material Products,” 2006.
-  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.
-  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.
-  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.
-  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.
-  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.
-  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)
-  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.
-  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.
-  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.
-  J. A. Mclaughlin and J. Raviv, “N-th-order autocorrelations in pattern recognition,” Information and Control, Vol.12, pp. 121-142, 1968.
-  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.
-  V. N. Vapnik, “An overview of statistical learning theory,” Neural Networks, IEEE Trans. on, Vol.10, No.5, pp. 988-999, 1999.
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