DOI QR코드

DOI QR Code

The Detection of Rectangular Shape Objects Using Matching Schema

  • Ye, Soo-Young (Department of Radiological Science, Catholic University of Pusan) ;
  • Choi, Joon-Young (Department of Electronics Engineering, Pusan National University) ;
  • Nam, Ki-Gon (Department of Electronics Engineering, Pusan National University)
  • Received : 2016.08.19
  • Accepted : 2016.09.12
  • Published : 2016.12.25

Abstract

Rectangular shape detection plays an important role in many image recognition systems. However, it requires continued research for its improved performance. In this study, we propose a strong rectangular shape detection algorithm, which combines the canny edge and line detection algorithms based on the perpendicularity and parallelism of a rectangle. First, we use the canny edge detection algorithm in order to obtain an image edge map. We then find the edge of the contour by using the connected component and find each edge contour from the edge map by using a DP (douglas-peucker) algorithm, and convert the contour into a polyline segment by using a DP algorithm. Each of the segments is compared with each other to calculate parallelism, whether or not the segment intersects the perpendicularity intersecting corner necessary to detect the rectangular shape. Using the perpendicularity and the parallelism, the four best line segments are selected and whether a determined the rectangular shape about the combination. According to the result of the experiment, the proposed rectangular shape detection algorithm strongly showed the size, location, direction, and color of the various objects. In addition, the proposed algorithm is applied to the license plate detecting and it wants to show the strength of the results.

Keywords

References

  1. T. Hermosilla , L. A. Ruiz, J. A. Recio, and J. Estornell, Remote Sens., 3, 1188 (2011). [DOI: http://dx.doi.org/10.3390/rs3061188]
  2. C. R. Jung and R. Schramm, Proc. the 17th Brazilian Symposium on Computer Graphics and Image Processing (Curitiba, Brazil, 2004) p. 113. [DOI: http://dx.doi.org/10.1109/SIBGRA.2004.1352951]
  3. D. Shaw and N. Barnes, Proc. the Workshop of the Application of Computer Vision in conjunction with ECCV, (Graz, Austria, 2006) p.119. [DOI: http://dx.doi.org/10.1.1.591.1009]
  4. S. F. Moosavizade, S. R. Mohammadi, M. Arefkhani, and A. Poyan, J. of Basic and Applied Scientific Research, 3, 120 (2013). [DOI: http://dx.doi.org/10.1.1.59.4239]
  5. J. lllingworth and J. Kittler, Comput. Vision Graph. Image Process., 44, 87 (1988). [DOI: http://dx.doi.org/10.1016/S0734-189X(88)80033-1]doi:10.1016/S0734-189X(88)80033-1. [DOI:http://dx.doi.org/10.1016/S0734-189X(88)80033-1
  6. H. Kalviainen and P. Hirvonen, Pattern Recognition Lett., 77, (1997). [DOI: http://dx.doi.org/10.1016/S0167-8655(96)00132-8]
  7. K. Vaheesan, C. Chandrakumar, S. Mathavan, K. Kamal, M. Rahman, and A. Al-Habaibeh, Twelfth International Conference on Quality Control by Artificial Vision 2015, 953411. [DOI: http://dx.doi.org/10.1117/12.2182913]
  8. Y. Zhu, B. Carragher, F. Mouche, C. Potter, IEEE Trans. Med. Imaging, 22, 1053 (2003). [DOI: http://dx.doi.org/10.1109/TMI.2003.816947]
  9. G. Roth and M. D. Levine, IEEE Trans. Pattern Anal. Machine Intell., 16, 901 (1994). [DOI: http://dx.doi.org/10.1109/34.310686]
  10. Y. Liu, T. Ikenaga, S. Goto, Signal Processing, 87, 2649 (2007). [DOI: http://dx.doi.org/10.1016/j.sigpro.2007.04.018]
  11. D. H. Douglas and T. K. Peucker, Cartographica, 10, 112 (1973). [DOI: http://dx.doi.org/10.3138/FM57-6770-U75U-7727]
  12. J. B. Burns, A. R. Hanson, and E. M. Riseman, IEEE Trans. on Pattern Analysisand Machine Intelligence, 8, 425 (1986). [DOI: http://dx.doi.org/10.1109/TPAMI.1986.4767808]
  13. R. G. von Gioi, J. Jakubowicz, J. M. Morel, and G. Randall, IEEE Trans. On Pattern Analysis and Machine Intelligence, 32, 722 (2010). [DOI: http://dx.doi.org/10.1109/TPAMI.2008.300]
  14. C. Akinlar and C. Topal, Pattern Recognition Letters, 32, 1633 (2011). [DOI: http://dx.doi.org/10.1016/j.patrec.2011.06.001]
  15. C. Topal and C. Akinlar, Journal of Visual Communication and Image Representation, 23, 862 (2012). [DOI: http://dx.doi.org/10.1016 /j.jvcir. 2012.05.004] https://doi.org/10.1016/j.jvcir.2012.05.004
  16. J. Canny, IEEE Trans. On Pattern Anal. Machine Intell. 8, 679 (1986). [DOI: http://dx.doi.org/10.1109/TPAMI.1986.4767851]