DOI QR코드

DOI QR Code

Comparison of Edge Localization Performance of Moment-Based Operators Using Target Image Data

  • Seo, Suyoung (Department of Civil Engineering, Kyungpook National University)
  • Received : 2016.01.08
  • Accepted : 2016.02.18
  • Published : 2016.02.28

Abstract

This paper presents a method to evaluate the performance of subpixel localization operators using target image data. Subpixel localization of edges is important to extract the precise shape of objects from images. In this study, each target image was designed to provide reference lines and edges to which the localization operators can be applied. We selected two types of moment-based operators: Gray-level Moment (GM) operator and Spatial Moment (SM) operator for comparison. The original edge localization operators with kernel size 5 are tested and their extended versions with kernel size 7 are also tested. Target images were collected with varying Camera-to-Object Distance (COD). From the target images, reference lines are estimated and edge profiles along the estimated reference lines are accumulated. Then, evaluation of the performance of edge localization operators was performed by comparing the locations calculated by each operator and by superimposing them on edge profiles. Also, enhancement of edge localization by increasing the kernel size was also quantified. The experimental result shows that the SM operator whose kernel size is 7 provides higher accuracy than other operators implemented in this study.

Keywords

References

  1. Basu, M., 2002. Gaussian-based edge-detection methods - a survey, IEEE Transaction on Systems, Man and Cybernetics, 32(3): 252-260. https://doi.org/10.1109/TSMCC.2002.804448
  2. Canny, J., 1986. A computational approach to edge detection, IEEE Transaction o Pattern Analysis Machine Intelligence, PAMI-8(6): 679-698. https://doi.org/10.1109/TPAMI.1986.4767851
  3. Chen, P., F. Chen, Y. Han, and Z. Zhang, 2014. Subpixel dimensional measurement with Logistic edge model, Optik, 125: 2076-2080. https://doi.org/10.1016/j.ijleo.2013.10.020
  4. Cheng, S.-C. and T.-L. Wu, 2005. Subpixel edge detection of color images by principal axis analysis and moment-preserving principle, Pattern Recognition, 38: 527-537. https://doi.org/10.1016/j.patcog.2004.08.016
  5. Hagara, M. and P. Kulla, 2011. Edge detection with sub-pixel accuracy based on approximation of edge with Erf function, Radioengineering, 20(2): 516-524.
  6. Lindeberg, T., 1998. Edge detection and ridge detection with automatic scale selection, International Journal of Computer Vision, 30(2): 117-154. https://doi.org/10.1023/A:1008097225773
  7. Lyvers, E.P., O.R. Mitchell, M.L. Akey, and A.P. Reeves, 1989. Subpixel measurements using a moment-based edge operator, IEEE Transaction on Pattern Analysis Machine Intelligence, 11(12): 1293-1309. https://doi.org/10.1109/34.41367
  8. Papari, G. and N. Petkov, 2011. Edge and line oriented contour detection: state of the art, Image and Vision Computing, 29: 79-103. https://doi.org/10.1016/j.imavis.2010.08.009
  9. Seo, S., 2016. Estimation of edge displacement against brightness and camera-to-object distance, IET Image Processing (in review).
  10. Tabatabai, A. and R. Mitchell, 1984. Edge location to subpixel values in digital imagery, IEEE Transaction on Pattern Analysis and Machine Intelligence, 1984, PAMI-6(2): 188-201. https://doi.org/10.1109/TPAMI.1984.4767502
  11. Trujillo-Pino, A., K. Krissian, M. Aleman-Flores, and D. Santana-Cedres, 2013. Accurate subpixel edge location based on partial area effect, Image and Vision Computing, 31: 72-90. https://doi.org/10.1016/j.imavis.2012.10.005
  12. Ye, J., G. Fu, and U.P. Poudel, 2005. High-accuracy edge detection with blurred edge model, Image and Vision Computing, 23: 453-467. https://doi.org/10.1016/j.imavis.2004.07.007
  13. Ziou, D. and S. Tabbone, 1998. Edge detection techniques - an overview, International Journal of Pattern Recognition and Image Analysis, 8: 537-559.

Cited by

  1. Estimation of edge displacement against brightness and camera-to-object distance vol.11, pp.8, 2017, https://doi.org/10.1049/iet-ipr.2016.0796