Classification of Dust Elements by Spatial Geometric Features

A. Proietti, M. Panella, E. D. Di Claudio, G. Jacovitti, G. Orlandi

Abstract

Management of air quality is an important task in many human activities. It is carried out mainly by installing ventilation and filtering facilities. In order to ensure efficiency, these systems must be designed after the knowledge of key environmental parameters, such as size and type of particles and fibres present in the air. In this paper, we propose a new method for the classification of dust particles and fibres based on a minimal set of geometric features extracted from binary images of dust elements, captured by a very cheap imaging system. The proposed technique is discussed and tested. Experimental results obtained by real- measured data are presented, showing satisfactory performance by using several well-known classifiers.

References

  1. Baron, P. (2001). Measurement of airborne fibers: A review. Industrial Health, 39(2):39-50.
  2. Bogomolny, A. (1987). On the perimeter and area of fuzzy sets. Fuzzy Sets and Systems, 23(2):257 - 269.
  3. Camuffo, D. (2013). Microclimate for Cultural Heritage: Conservation, Restoration, and Maintenance of Indoor and Outdoor Monuments. Elsevier Science, Boston.
  4. Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13(1):21-27.
  5. Di Claudio, E., Jacovitti, G., Orlandi, G., and Proietti, A. (2015). Fast classification of dust particles from shadows. In ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings, volume 2, pages 241-247.
  6. Drolon, H., Druaux, F., and Faure, A. (2000). Particles shape analysis and classification using the wavelet transform. Pattern Recognition Letters, 21(67):473 - 482.
  7. Fisher, R. A. (1938). The statistical utilization of multiple measurements. Annals of Eugenics, 8(4):376-386.
  8. Ghedini, N., Ozga, I., Bonazza, A., Dilillo, M., Cachier, H., and Sabbioni, C. (2011). Atmospheric aerosol monitoring as a strategy for the preventive conservation of urban monumental heritage: The florence baptistery. Atmospheric Environment, 45(33):5979 - 5987.
  9. Golub, G. and Loan, C. V. (1989). Matrix Computations. John Hopkins University Press, Baltimore, USA, 2nd edition.
  10. Gonzalez, R. C., Woods, R. E., and Eddins, S. L. (2004). Digital image processing using matlab. Upper Saddle River, N. J: Pearson Prentice Hall.
  11. ISO (2010). Cleanrooms and associated controlled environments - part 1: Classification of air cleanliness by particle concentration. ISO/DIS 14644-1.
  12. Krzanowski, W. J., editor (1988). Principles of Multivariate Analysis: A User's Perspective. Oxford University Press, Inc., New York, NY, USA.
  13. Langley, P., Iba, and, W., and Thompson, K. (1992). An analysis of bayesian classifiers. In Proceedings of the Tenth National Conference on Artificial Intelligence, AAAI'92, pages 223-228. AAAI Press.
  14. Lawrence, R. L. and Wright, A. (2001). Rule-based classification systems using classification and regression tree (cart) analysis. Photogrammetric engineering and remote sensing, 67(10):1137-1142.
  15. Liparulo, L., Proietti, A., and Panella, M. (2013). Fuzzy membership functions based on point-to-polygon distance evaluation. In Fuzzy Systems (FUZZ), 2013 IEEE International Conference on, pages 1-8. IEEE.
  16. Maisto, M., Panella, M., Liparulo, L., and Proietti, A. (2013). An Accurate Algorithm for the Identification of Fingertips Using an RGB-D Camera. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 3(2):272-283.
  17. Mamdani, E. and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1):1 - 13.
  18. Panella, M. and Martinelli, G. (2011). Neural networks with quantum architecture and quantum learning. International Journal of Circuit Theory and Applications, 39(1):61-77.
  19. Panella, M., Rizzi, A., and Martinelli, G. (2003). Refining accuracy of environmental data prediction by MoG neural networks. Neurocomputing, 55(3-4):521-549.
  20. Panella, M., Rizzi, A., Mascioli, F. F., and Martinelli, G. (2001). ANFIS synthesis by hyperplane clustering. In Proceedings of Joint IFSA World Congress and NAFIPS International Conference (IFSA/NAFIPS 2001), volume 1, pages 340-345. IEEE.
  21. Parisi, R., Cirillo, A., Panella, M., and Uncini, A. (2007). Source localization in reverberant environments by consistent peak selection. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2007), volume I, pages I37-I-40. IEEE.
  22. Proietti, A., Leccese, F., Caciotta, M., Morresi, F., Santamaria, U., and Malomo, C. (2014). A new dusts sensor for cultural heritage applications based on image processing. Sensors (Switzerland), 14(6):9813-9832.
  23. Proietti, A., Liparulo, L., Leccese, F., and Panella, M. (2016). Shapes classification of dust deposition using fuzzy kernel-based approaches. Measurement, 77:344 - 350.
  24. Proietti, A., Panella, M., Leccese, F., and Svezia, E. (2015). Dust detection and analysis in museum environment based on pattern recognition. Measurement, 66(0):62 - 72.
  25. Purushotham, S. and Tripathy, B. (2012). Evaluation of classifier models using stratified tenfold cross validation techniques. Communications in Computer and Information Science, 270 CCIS(PART II):680-690.
  26. Ranzato, M., Taylor, P., House, J., Flagan, R., LeCun, Y., and Perona, P. (2007). Automatic recognition of biological particles in microscopic images. Pattern Recognition Letters, 28(1):31 - 39.
  27. Reddy, P. R., Amarnadh, V., and Bhaskar, M. (2012). Evaluation of stopping criterion in contour tracing algorithms. International Journal of Computer Science and Information Technologies (IJCSIT), 3:3888- 3894.
  28. Rizzi, A., Buccino, M., Panella, M., and Uncini, A. (2008). Genre classification of compressed audio data. In Proceedings of IEEE MMSP 2008, pages 654-659. IEEE.
  29. Scardapane, S., Wang, D., Panella, M., and Uncini, A. (2015). Distributed learning for random vector functional-link networks. Information Sciences, 301:271-284.
  30. Specht, D. F. (1990). Probabilistic neural networks. Neural Networks, 3(1):109 - 118.
  31. Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics, 15(1):116-132.
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Paper Citation


in Harvard Style

Proietti A., Panella M., Di Claudio E., Jacovitti G. and Orlandi G. (2016). Classification of Dust Elements by Spatial Geometric Features . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 247-254. DOI: 10.5220/0005697502470254


in Bibtex Style

@conference{icpram16,
author={A. Proietti and M. Panella and E. D. Di Claudio and G. Jacovitti and G. Orlandi},
title={Classification of Dust Elements by Spatial Geometric Features},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={247-254},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005697502470254},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Classification of Dust Elements by Spatial Geometric Features
SN - 978-989-758-173-1
AU - Proietti A.
AU - Panella M.
AU - Di Claudio E.
AU - Jacovitti G.
AU - Orlandi G.
PY - 2016
SP - 247
EP - 254
DO - 10.5220/0005697502470254