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Step-by-step pipeline processing approach for line segment detection

Step-by-step pipeline processing approach for line segment detection

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This study proposes a line segment detection that can efficiently and effectively handle non-linear uniform intensity changes. The presented sketching algorithm applies the resistant to affine transformation and monotonic intensity change (RATMIC) descriptor to conduct binary translation in the image pre-processing step, which can remove the unwanted smoothing of the Canny detector in most line detections. The Harris corner detector is applied to catch regions of line segments for the purpose of simulating the composition of sketching and achieving a sense of unity within the picture. Furthermore, the RATMIC descriptor is employed to obtain binary images of the regions of interest (ROIs). Finally, small eigenvalue analysis is implemented to detect straight lines in the ROIs. The experiments conducted on various images with image rotation, scaling, and translation validate the effectiveness of the proposed method. The experimental results also demonstrate that about 30% in the overall coverage of major lines and 20% in the coverage per major line are increased compared with the state-of-the-art line detectors. Moreover, the performance of the proposed method produces a combined advantage of ∼17% in the coverage of line segments over the line segment detector with noisy images.

References

    1. 1)
      • 1. Guru, D.S., Shekar, B.H., Nagabhushan, P.: ‘A simple and robust line detection algorithm based on small eigen value analysis’, Pattern Recognit. Lett., 2004, 25, (1), pp. 113.
    2. 2)
      • 2. Guerreiro, R.F.C., Aguiar, P.M.Q.: ‘Extraction of line segments in cluttered images via multiscale edges’. Proc. IEEE. Int. Conf. Image Processing, Melbourne, VIC, September 2013, pp. 30453048.
    3. 3)
      • 3. Gioi, R.G., Jakubowicz, J., Morel, J.M., et al: ‘LSD: A fast line segment detector with a false detection control’, IEEE TPAMI, 2010, 32, (4), pp. 722732.
    4. 4)
      • 4. Nelson, R.C.: ‘Finding line segments by stick growing’, IEEE TPAMI, 1994, 16, (5), pp. 519523.
    5. 5)
      • 5. Niroshika, A.U.A., Meegama, R.G.N., Lokupitiya, R.S., et al: ‘Active contours with prior corner detection to extract discontinuous boundaries of anatomical structures in X-ray images’, IET Image Process., 2015, 9, (3), pp. 202210.
    6. 6)
      • 6. Swaminathan, A., Ramapackiyam, S.S.K.: ‘Edge detection for illumination varying images using wavelet similarity’, IET Image Process., 2014, 8, (5), pp. 261268.
    7. 7)
      • 7. Du, S., Wyk, B.J.V., Tu, C., et al: ‘An improved Hough transform neighbourhood map for straight line segments’, IEEE TIP, 2010, 19, (3), pp. 573585.
    8. 8)
      • 8. Guerreiro, R.F.C., Aguiar, P.M.Q.: ‘Connectivity-enforcing Hough transform for the robust extraction of line segments’, IEEE TIP, 2012, 21, (12), pp. 48194829.
    9. 9)
      • 9. Hough, P.V.C.: ‘Method and means for recognizing complex patterns’. U. S. Patents 3069654 A, December 1962.
    10. 10)
      • 10. Duda, R.O., Hart, P.E.: ‘Use of the Hough transformation to detect lines and curves in pictures’, Commun. ACM., 1972, 15, pp. 1115.
    11. 11)
      • 11. Matas, J., Galambos, C., Kittler, J.: ‘Robust detection of lines using the progressive probabilistic Hough transform’, Comput. Vis. Image Underst., 2000, 78, pp. 119137.
    12. 12)
      • 12. Palmer, P.L., Kittler, J., Petrou, M.: ‘An optimizing line finder using a Hough transform algorithm’, Comput. Vis. Image Underst., 1997, 67, (1), pp. 123.
    13. 13)
      • 13. Yang, K., Sam Ge, S., He, H.: ‘Robust line detection using two-orthogonal direction image scanning’, Comput. Vis. Image Underst., 2011, 115, pp. 12071222.
    14. 14)
      • 14. Raghavan, V., Masumoto, S., Koike, K., et al: ‘Automatic lineament extraction from digital images using a segment tracing and rotation transformation approach’, Comput. Geosci., 1995, 21, (4), pp. 555591.
    15. 15)
      • 15. Rahnama, M., Gloaguen, R.: ‘TecLines: A MATLAB-based toolbox for tectonic lineament analysis from satellite images and DEMs, Part 1: line segment detection and extraction’, Remote Sens., 2014, 6, pp. 59385958.
    16. 16)
      • 16. Rahnama, M., Gloaguen, R.: ‘TecLines: A MATLAB-based toolbox for tectonic lineament analysis from satellite images and DEMs, Part 2: line segments linking and merging’, Remote Sens., 2014, 6, pp. 1146811493.
    17. 17)
      • 17. Kim, J., Lee, S.: ‘Extracting major lines by recruiting zero-threshold canny edge links along sobel highlights’, IEEE SPL, 2015, 22, (10), pp. 16891691.
    18. 18)
      • 18. Mikolajczyk, K., Schmid, C.: ‘An affine invariant interest point detector’. Proc. Eur. Conf. Computer Vision, Copenhagen, Denmark, May 2002, pp. 128142.
    19. 19)
      • 19. Harris, C., Stephens, M.: ‘A combined corner and edge detector’. Proc. Int. Conf. Alvey Vision, Manchester, UK, August 1988, pp. 147151.
    20. 20)
      • 20. Huang, Z.Y., Kang, W.X., Wu, Q.X., et al: ‘A new descriptor resistant to affine transformation and monotonic intensity change’, Comput. Vis. Image Underst., 2014, 120, pp. 117125.
    21. 21)
      • 21. Shen, F., Wang, H.: ‘Corner detection based on modified Hough transform’, Pattern Recognit. Lett., 2002, 23, (8), pp. 10391049.
    22. 22)
      • 22. Montero, A.S., Stojmenovic, M., Nayak, A.: ‘Robust detection of corners and corner-line links in images’. Proc. IEEE. Int. Conf. Computer and Information Technology, Bradford, UK, June 2010, pp. 495502.
    23. 23)
      • 23. Davies, E.: ‘Application of the generalized Hough transform to corner detection’, IET Comput. Digit. Techniques, 1988, 135, (1), pp. 4954.
    24. 24)
      • 24. Shen, F., Wang, H.: ‘A local edge detector used for finding corners’. Proc. Int. Conf. Information and Communications Security, Xian, China, November 2001, pp. 15.
    25. 25)
      • 25. Tuytelaars, T., Mikolajczyk, K.: ‘Local invariant feature detectors-a survey’, Comput. Graph. Vis., 2007, 3, (3), pp. 177280.
    26. 26)
      • 26. Zhong, B.J., Xu, D.S., Yang, J.W.: ‘Vertical corner line detection on buildings in quasi-Manhattan world’. Proc. IEEE. Int. Conf. Image Processing, Melbourne, VIC, September 2013, pp. 30643068.
    27. 27)
      • 27. Tsai, D.M., Hou, H.T., Su, H.J.: ‘Boundary-based corner detection using eigenvalues of covariance matrices’, Pattern Recognit. Lett., 1999, 20, (1), pp. 3140.
    28. 28)
      • 28. ‘Berkeley segmentation dataset’. Available at http://www.wisdom.weizmann.ac.il/∼vision/Seg_Evaluation_DB/index.html, accessed 27 October2015.
    29. 29)
      • 29. ‘Source code of LSD: A Line segment detector, version 1.6’. Available at http://www.ipol.im/pub/art/2012/gjmr-lsd/, accessed 24 September2015.
    30. 30)
      • 30. ‘Experimental results of proposed method’. Available at http://isri.skku.ac.kr/majorline, accessed 24 September2015.
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