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


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

December 16, 2009
March 29, 2010
August 20, 2010
asbestos, automatic counting, consolidation of kernel functions, microscope images, object detection
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.
Data files:
  1. [1] P. A. Baron and S. A. Shulman, “Evaluation of the Magiscan Image Analyzer for Asbestos Fiber Counting,” American Industrial Hygiene Association J., Vol.48, pp. 39-46, 1987.
  2. [2] L. C. Kenny, “Asbestos Fiber Counting by Image Analysis – The Performance of The Manchester Asbestos Program on Magiscan,” Annals of Occupational Hygiene, Vol.28, No.4, pp. 401-415, 1984.
  3. [3] Y. Inoue, A. Kaga, and K. Yamaguchi, “Development of An Automatic System for Counting Asbestos Fibers Using Image Processing,” Particle Science and Technology, Vol.16, pp. 263-279, 1989.
  4. [4] A. Nomoto, K. Hotta, and H. Takahashi, “An Asbestos Counting Method From Microscope Images of Building Materials Using Summation Kernel of Color and Shape,” Proc. Int. Conf. on Neural Information Processing, Lecture Notes in Computer Science Vol.5507, pp. 671-678, 2009.
  5. [5] Y. Moriguchi, K. Hotta, and H. Takahashi, “An Asbestos Detection Method from Microscope Images Using Support Vector Random Field of Local Color Features,” IEEJ Trans. EIS, Vol.129, No.5, pp. 818-823, 2009.
  6. [6] K. Kawabata, Y. Komori, H. Asama, and T. Mishima, “An Asbestos Fibers Detection Technique Utilizing Image Processing Based on Dispersion Colour,” Particle Science and Technology: An International Journal, Vol.27, No.2, pp. 177-192, 2009.
  7. [7] E. Hjelmas and B. K. Low, “Face detection: A survey,” Computer Vision and Image Understanding, Vol.83, No.2, pp. 236-274, 2001.
  8. [8] M.-H. Yang, D. Kriegman, and N. Ahuja, “Detecting faces in images: A survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.24, No.1, pp. 34-58, 2002.
  9. [9] C. Papageorgiou and T. Poggio, “A Trainable System for Object Detection,” Int. J. of Computer Vision, Vol.38, No.1, pp. 15-33, 2000.
  10. [10] N. Dalal and B. Trigs, “Histogram of oriented gradients for human detection,” Proc. IEEE CS Conf. on Computer Vision and Pattern Recognition, pp. 886-893, 2005.
  11. [11] V. Vapnik, “Statistical Learning Theory,” John Wiley and Sons, 1998.
  12. [12] K. Hotta, “Robust Face Recognition under Partial Occlusion Based on Support Vector Machine with Local Gaussian Summation Kernel,” Image and Vision Computing, Vol.26, No.11, pp. 1490-1498, 2008.
  13. [13] D. Haussler, “Convolution kernels on discrete structures,” Technical report, UCSC-CRL-99-10, 1999.
  14. [14] R. Debnath and H. Takahashi, “Kernel Selection for the Support Vector Machine,” IEICE Trans. Information and System, Vol.E-87-D, No.12, pp. 2903-2904, 2004.
  15. [15] J. Shawe-Taylor and N. Cristianini, “Kernel methods for Pattern Analysis,” Cambridge University Press, 2004.
  16. [16] “Determination of Asbestos in BuildingMaterial Products,” JIS A 1481, 2008.
  17. [17] K. Sung and T. Poggio, “Example-Based Learning for View-Based Human Face Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.20, No.1, pp. 39-51, 1998.
  18. [18] H. A. Rowley, S. Baluja, and T. Kanade, “Neural Network-Based Face Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.20, No.1, pp. 23-38, 1998.
  19. [19] K. Hotta, M. Tanaka, T. Kurita, and T. Mishima, “An Efficient Search Method Based on Dynamic Attention Map by Ising Model,” IEICE Trans. Information and Systems, Vol.E88-D, No.10, pp. 2286-2296, 2005.
  20. [20] H. Kuba, K. Hotta, and H. Takahashi, “Automatic Particle Detection and Counting By One-Class SVM From Microscope Image,” Proc. Int. Conf. on Neural Information Processing, Lecture Notes in Computer Science, Vol.5507, pp. 361-368, 2009.
  21. [21] K. Kawabata, S. Morishita, H. Takemura, K. Hotta, T. Mishima, H. Asama, H. Mizoguchi, and H. Takahashi, “Development of an Automatic Microscopic System for Asbestos Qualitative Analysis by Dispersion Staining Method,” J. of Robotics and Mechatronics, Vol.21, No.2, pp. 186-192, 2009.

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