Call for Paper

CAE solicits original research papers for the April 2023 Edition. Last date of manuscript submission is March 31, 2023.

Read More

Determination of Additional Aperture in Non-Metal Sewer Pipes by Image Processing

SocButtons v1.5

Mohamed Qays Jameel Alsalihi, Murat Selek. Published in Image Processing.

Determination of Additional Aperture in Non-Metal Sewer Pipes by Image Processing
Communications on Applied Electronics
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Series: Volume 7 Number 6
Authors: Mohamed Qays Jameel Alsalihi, Murat Selek
10.5120/cae2017652682
Download full text

Mohamed Qays Jameel Alsalihi and Murat Selek. Determination of Additional Aperture in Non-Metal Sewer Pipes by Image Processing. Communications on Applied Electronics 7(6):8-11, September 2017. BibTeX

@article{10.5120/cae2017652682,
	author = {Mohamed Qays Jameel Alsalihi and Murat Selek},
	title = {Determination of Additional Aperture in Non-Metal Sewer Pipes by Image Processing},
	journal = {Communications on Applied Electronics},
	issue_date = {September 2017},
	volume = {7},
	number = {6},
	month = {Sep},
	year = {2017},
	issn = {2394-4714},
	pages = {8-11},
	numpages = {4},
	url = {http://www.caeaccess.org/archives/volume7/number6/760-2017652682},
	doi = {10.5120/cae2017652682},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In this investigation, image processing was applied to find out the disorder may accrue in the non-metal sewer underground pipelines (i.e. additional aperture). This disorder may be discovered via extracting the characteristics from interior image taken and tackled through suitable filters to improve its appearance, and ultimately subjected to feature extraction process to extract and determine their characters, and at last step, finding out and diagnosing the faults and disadvantages that may exist in the tested pipelines, automatically, without the need to the human eye. For detecting the additional apertures, edge detection technique was used. By this technique a reliable result was achieved. An image with additional apertures was produced.

References

  1. Gonzalez, R. C. and Woods, R. E., 2008, Digital Image Processing -3rd ed., Upper Saddle River, New Jersey 07458, usa.
  2. Shefali, G. and kaur Y., 2014. Review of Different Contrast Enhancement Techniques for a Digital Image, I.J.A.R.C.SC.S. Eng. Vol.4 (7), pp 1213-1218.
  3. Wu, X. F. and Bai H., " Automated assessment of buried pipeline defects by image processing",  Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference.
  4. Stephen, M., Ulrich, N., and Jonathan, S., 2005. " On-line novelty detection for autonomous mobile robots", 2005 Elsevier Robotics and Autonomous Systems 51 (2005), pp 191–206.
  5. Mayuri, D. S. and Prof. Kishor W., 2016. "An Application of Image Processing to Detect the Defects of Industrial Pipes", International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 3, March 2016, pp 979-981.
  6. Safizadeh, M. S. and Azizzadeh, T., 2012. "Automated Detection of Inner Surface Defects in Pipes Using Image Processing Algorithms", Advanced Design and Manufacturing Technology, Vol. 5/ No. 5/ December – 2012, pp53-59.
  7. Amin, S. S. and Ivanov, N., 2014. "Statistical Image Classification for Image Steganographic Techniques", International Journal of Image, Graphics and Signal Processing (IJIGSP), vol. 6, no. 8, pp. 19-24, 2014.DOI: 10.5815/ijigsp.2014.08.03.
  8. Morales, A. G., Acosta, N. M., Gago, A. A., García, E. B. R., and Medina, J. E. P., 2014. “A new proposal for graph-based image classification using frequent approximate subgraphs”, Pattern Recognition, vol. 47, no. 1, pp. 169-177, 2014.
  9. Qian, J., Yang, J. and Gao, G., 2013. “Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction”, Pattern Recognition, vol. 46, no. 10, pp. 2724-2739, 2013.
  10. Xiao, K., Liang, A. L., Guan, H. B. and Hassanien, A. E., 2013. “Extraction and application of deformation-based feature in medical images”, Neurocomputing, vol. 120, pp. 177- 184, 2013.
  11. Moreno, R., Puig, D., Julia, C., and GARCIA, M., 2009. A new methodology for evaluation of edge detectors. In Proceedings of the 16th IEEE International Conference on Image Processing (2009), pp. 2157–2160.
  12. Wenlong, F., 2014. 'Feature Extraction in Edge Detection using Genetic Programming', PHD thesis, submitted to the Victoria University of Wellington. PP 17-19.

Keywords

Edge detection technique, Sobel, and Prewitt operators