Accuracy of the straight line Hough Transform: The non-voting approach

Computer Vision and Image Understanding

Volumn 103, Issue 1, 2006, Pages 1-21

  Shapiro, Vladimir ^a  

^a Orbograph Ltd   (Israel)

Author keywords

Accuracy; Angular projection; Hough Transform; Hough Green Transform; Voting paradigm

Indexed keywords

IMAGE ANALYSIS; IMAGE RECONSTRUCTION; PROJECTION SYSTEMS;

ACCURACY; ANGULAR PROJECTION; HOUGH TRANSFORM; HOUGH-GREEN TRANSFORM; VOTING PARADIGM;

HOUGH TRANSFORMS;

EID: 33646813264     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2006.02.001     Document Type: Article

References (36)

1. Duda R., and Hart P. Use of the Hough Transform to detect lines and curves in pictures. Communications on ACM 15 (1972) 11-15
2. Breuel T.M. Finding lines under bounded error. Pattern Recognition 29 (1996) 167-178
3. Kiryati N., and Bruckstein A.M. Antialiasing the Hough Transform. CVGIP: Graphical Models and Image Processing 53 (1991) 213-222
4. Van Veen T.M., and Groen F.C.A. Discretisation errors in the Hough Transform. Pattern Recognition 14 (1981) 137-145
5. Atiquzzaman M., and Akhtar M.W. Complete line segment description using the Hough transform. Image and Vision Computing 12 (1994) 267-273
6. Leavers V. Which Hough transform?. CVGIP: Image Understanding 58 (1993) 250-264
7. Deans S.R. The Radon transform and Some of Its Applications (1983), Wiley, New York
8. Steier W.H., and Shori R.K. Optical Hough transform. Applied Optics 25 (1986) 2734-2738
9. Zhang M. On the discretization of parameter domain in Hough Transformation. Proc. Int'l Conf. Pattern Recognition (1996) 527-537
10. Niblack W., and Petkovic D. On improving the accuracy of the Hough Transform. Machine Vision and Applications 3 (1990) 87-106
11. Lam C., Lam T., Yuen S., and Leung N. An analysis on quantizing the Hough Space. Pattern Recognition Letters 15 (1994) 1127-1135
12. Guo L., and Chutatape O. Influence of discretization in image space on Hough Transform. Pattern Recognition 32 (1999) 635-644
13. Yuen S., and Ma C. An investigation of the nature of parameterization for the Hough Transform. Pattern Recognition 30 (1997) 1009-1040
14. Kiryati N., Kalviainen H., and Alaoutinen S. Randomized or probabilistic Hough Transform: unified performance evaluation. Pattern Recognition Letters 21 (2000) 1157-1164
15. Atiquzzaman M. Multiresolution Hough Transform-an efficient method of detecting patterns in images. IEEE Trans. Pattern Analysis Machine Intelligence 14 (1992) 1090-1095
16. Palmer P., Petrou M., and Kittler J. A Hough transform with a 2D hypothesis testing kernel. CVGIP: Image Understanding 58 (1993) 221-234
17. Palmer P., Kittler J., and Petrou M. Using focus of attention with the Hough transform for accurate line parameter estimation. Patter Recognition 27 (1994) 1127-1134
18. P. Palmer, M. Petrou, J. Kittler, Accurate line parameters from optimising Hough Transform for vanishing point detection, Proc. Int'l Conf. Computer Vision, 1993, pp. 529-533.
19. Princen J., Illingworth J., and Kittler J. A formal definition of the Hough Transform: properties and relationships. Journal of Mathematical Imaging Vision 1 (1992) 153-168
20. Srihari S., and Govindaraju V. Analysis of textual images using the Hough Transform. Machine Vision and Applications 2 (1989) 141-153
21. V. Shapiro, Fast and precise angular projection evaluation, in: L. Latecki, D. Mount, A. Wu (Eds.), Vision Geometry XII, vol. 5300, Proc. SPIE-IS&T Electronic Imaging, San Jose, CA, 2004, pp. 31-42.
22. Akhtar M., and Atiquzzaman M. Determination of line length using Hough Transform. Electronics Letters 28 (1992) 94-96
23. Miyatake T., Matsushima H., and Ejiri M. Contour representation of binary images using run-type direction codes. Machine Vision and Applications 9 (1997) 193-200
24. V. Shapiro, Super-resolution capabilities of the Hough-Green Transform. Proc. ICAIP'03, Lecture Notes in Computer Science, vol. 2756, Springer, Berlin, 2003, pp. 673-680.
25. Shapiro V., Kavardjikov V., and Atanassov S. An approach to automatic analysis of Young's Fringes in Speckle-Photography. Applied Optics 32 (1993) 4378-4387
26. Kesidis A., and Paramarkos N. On the inverse Hough Transform. IEEE Trans. Pattern Analysis Machine Intelligence 21 (1999) 1329-1343
27. Ji Q., and Xie Y. Randomised Hough Transform with error propagation for line and circle detection. Pattern Analysis and Application 6 (2003) 55-64
28. J. Song, M. Cai, M. Lyu, S. Cai, A new approach for line recognition in large-seize images using Hough Transform, Proc. 16 Int'l Conf. Pattern Recognition, 2002, pp. 10033-10036.
29. Kalviainen H., and Hirvonen P. An extension to the Randomized Hough Transform Exploiting Connectivity. Pattern Recognition Letters 18 (1997) 77-85
30. Princen J., Illingworth J., and Kittler J. Hypothesis testing: a framework for analyzing and optimizing Hough Transform performance. IEEE Trans. Pattern Analysis Machine Intelligence 16 (1994) 329-341
31. Shapiro V. Effective Computation of the Hough-Green Transform. Proc. of Computer Vision Pattern Recognition and Image Processing (2003), Cary, NC pp. 837-840
32. Ren M., Yang J., and Sun H. Tracing boundary contours in a binary image. Image Vision and Computing 20 (2002) 125-131
33. Yang L., and Albregtsen F. Fast and exact computation of Cartesian geometric moments using discrete Green's theorem. Pattern Recognition 29 (1996) 1061-1073
34. Gonzales R., and Woods R. Digital Image Processing. second ed. (1993), Addison-Wesley, Reading, MA p.497
35. Kak A. Image reconstruction from projections. In: Ekstrom M.P. (Ed). Digital Image Processing Techniques (1984), Academic Press, London 111-171
36. Ledley R. Introduction to computerized tomography. Computers in biology and medicine 6 (1976) 239-246