Fast Classification of Dust Particles from Shadows

Elio D. Di Claudio, Giovanni Jacovitti, Gianni Orlandi, Andrea Proietti

2015

Abstract

A fast and versatile method for classifying dust particles dispersed in the air is presented. The method uses images captured by a simple imaging system composed of a photographic sensor array and of an illuminating source. Such a device is exposed to free particulate deposition from the environment, and its accumulation is measured by observing the shadows of the particles the air casts onto the photographic sensor. Particles are detected and classified in order to measure their density and to analyse their composition. To this purpose, the contour paths of particle shadows are traced. Then, distinctive features of single particles, such as dimension and morphology, are extracted by looking at corresponding features of the sequence of local orientation changes of contours. Discrimination between dust and fibre particles is efficiently done using the varimax norm of these orientation changes. It is shown through field examples that such a technique is very well suited for quantitative and qualitative dust analysis in real environments.

References

  1. Anderson, J., Thundiyil, J., and Stolbach, A. (2012). Clearing the air: A review of the effects of particulate matter air pollution on human health. Journal of Medical Toxicology, 8(2):166-175.
  2. Arkin, E., Chew, L., Huttenlocher, D., Kedem, K., and Mitchell, J. (1991). An efficiently computable metric for comparing polygonal shapes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 13(3):209-216.
  3. Bennett, J. R. and MacDonald, J. S. (1975). On the measurement of curvature in a quantized environment. IEEE Transactions on Computers, C-24(8):803-820.
  4. Chichinadze, M. and Kvavadze, E. (2013). Pollen and non-pollen palynomorphs in organic residue from the hoard of ancient vani (western georgia). Journal of Archaeological Science, 40(5):2237 - 2253.
  5. Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation. Journal of intelligent and Fuzzy systems, 2(3):267-278.
  6. Coronas, M., Bavaresco, J., Rocha, J., Geller, A., Caramo, E., Rodrigues, M., and Vargas, V. (2013). Attic dust assessment near a wood treatment plant: Past air pollution and potential exposure. Ecotoxicology and Environmental Safety, 95:153-160.
  7. Delgado-Saborit, J., Stark, C., and Harrison, R. (2011). Carcinogenic potential, levels and sources of polycyclic aromatic hydrocarbon mixtures in indoor and outdoor environments and their implications for air quality standards. Environment International, 37(2):383-392.
  8. Foi, A., Katkovnik, V., and Egiazarian, K. (2007). Pointwise shape-adaptive dct for high-quality denoising and deblocking of grayscale and color images. IEEE Transactions on Image Processing, 16(5):1395-1411.
  9. Frontczak, M. and Wargocki, P. (2011). Literature survey on how different factors influence human comfort in indoor environments. Building and Environment, 46(4):922-937.
  10. Jones, A. (1999). Indoor air quality and health. Atmospheric Environment, 33(28):4535-4564.
  11. Kyropoulou, D. (2013). Scanning electron microscopy with energy dispersive x-ray spectroscopy: An analytical technique to examine the distribution of dust in books. Journal of the Institute of Conservation, 36(2):173- 185.
  12. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, Inc., New York, NY, USA, 1 edition.
  13. Ozga, I., Bonazza, A., Ait Lyazidi, S., Haddad, M., BenNcer, A., Ghedini, N., and Sabbioni, C. (2013). Pollution impact on the ancient ramparts of the moroccan city sal. Journal of Cultural Heritage, 14(3 SUPPL):S25-S33.
  14. Persoon, E. and Fu, K. (1977). Shape discrimination using fourier descriptors. IEEE Transactions on Systems, Man and Cybernetics, 7(3):170-179.
  15. 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.
  16. Torgashov, P. (2014). Contour analysis for image recognition in c#.
Download


Paper Citation


in Harvard Style

Di Claudio E., Jacovitti G., Orlandi G. and Proietti A. (2015). Fast Classification of Dust Particles from Shadows . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 241-247. DOI: 10.5220/0005218802410247


in Bibtex Style

@conference{icpram15,
author={Elio D. Di Claudio and Giovanni Jacovitti and Gianni Orlandi and Andrea Proietti},
title={Fast Classification of Dust Particles from Shadows},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={241-247},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005218802410247},
isbn={978-989-758-077-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - Fast Classification of Dust Particles from Shadows
SN - 978-989-758-077-2
AU - Di Claudio E.
AU - Jacovitti G.
AU - Orlandi G.
AU - Proietti A.
PY - 2015
SP - 241
EP - 247
DO - 10.5220/0005218802410247