NOISE REMOVAL IN CRACK DETECTION ALGORITHM ON ASPHALT SURFACE IMAGES

Siwaporn Sorncharean, Suebskul Phiphobmongkol

2009

Abstract

This paper presents an image processing technique for noise removal in the intermediate stage of crack detection algorithm. Unlike noise in other domains, noise in this kind of image is unique in terms of size and dispersal. This technique is based on Newton’s theory of universal gravitation. The technique highlights noise within an image by giving low values to noise objects while giving high values to cracks, thus, making it simple to indicate an object as a noise or a crack. This method gave good results in removing noise from crack segmentation algorithm.

References

  1. Pynn, J., Wright, A., Lodge, R., 1999. Automatic identification of cracks in road surfaces. In 7th International Conference on Image Processing and Its Applications.
  2. Yu, S.-N., Janga, J.-H., Han, C.-S., 2007. Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel. In Automation in Construction.
  3. Subirats, P., Dumoulin, J., Legeay, V., Barba, D., 2006. Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform. In IEEE International Conference on Image Processing 2006.
  4. Xu, B., Huang, Y., 2006. Automatic Inspection of Pavement Cracking Distress. In Journal of Electronic Imaging, vol. 15.
  5. Sorncharean, S., Phiphobmongkol, S., 2008. Crack Detection on Asphalt Surface Image Using Enhanced Grid Cell Analysis. In DELTA2008, IEEE International Symposium on Electronic Design, Test and Applications.
  6. Zhang, H. G., Wang, Q., 2004. Use of Artificial Living System for Pavement Distress Survey. In 30th Annual Conference on Industrial Electronics Society 2004. IEEE Computer Society.
  7. Tomikawa, T., 1999. A study of road crack detection by the meta-genetic algorithm. In AFRICON. IEEE.
  8. Meignen, D., Bernadet, M., Briand, H., 1997. One Application of Neural Networks for Detection of Defects Using Video Data Bases: Identification of Road Distresses. In Proceedings of the 8th International Workshop on Database and Expert Systems Applications. IEEE Computer Society.
  9. Transportation Information Center, University of Wisconsin - Madison, 2002. Pavement Surface Evaluation and Rating Asphalt Roads, PASER Manual.
  10. Gonzalez, R. C., Woods, R. C., 1992. Digital Image Processing.
  11. Drakos, N., 1999. Newton's Law of Gravity. Online Document URL: http://theory.uwinnipeg.ca/ mod_tech/node54.html.
Download


Paper Citation


in Harvard Style

Sorncharean S. and Phiphobmongkol S. (2009). NOISE REMOVAL IN CRACK DETECTION ALGORITHM ON ASPHALT SURFACE IMAGES . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 269-272. DOI: 10.5220/0001797902690272


in Bibtex Style

@conference{visapp09,
author={Siwaporn Sorncharean and Suebskul Phiphobmongkol},
title={NOISE REMOVAL IN CRACK DETECTION ALGORITHM ON ASPHALT SURFACE IMAGES },
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={269-272},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001797902690272},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - NOISE REMOVAL IN CRACK DETECTION ALGORITHM ON ASPHALT SURFACE IMAGES
SN - 978-989-8111-69-2
AU - Sorncharean S.
AU - Phiphobmongkol S.
PY - 2009
SP - 269
EP - 272
DO - 10.5220/0001797902690272