Corrigendum to "Real-time line detection through an improved hough transform voting scheme" [Pattern Recognition 41 (1) 299-314] (DOI:10.1016/j.patcog.2007.04.003);Real-time line detection through an improved Hough transform voting scheme

Pattern Recognition

Volumn 41, Issue 1, 2008, Pages 299-314

  Fernandes, Leandro A F ^a   Oliveira, Manuel M ^a  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO SUL   (Brazil)

Author keywords

Collinear points; Hough transformation; Image processing; Pattern recognition; Real time line detection

Indexed keywords

COMPUTATION THEORY; HOUGH TRANSFORMS; IMAGE PROCESSING; PATTERN RECOGNITION; PIXELS; VOTING MACHINES;

COLLINEAR POINTS; GAUSSIAN KERNEL; VOTING MAP;

REAL TIME SYSTEMS;

EID: 34548041446     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.04.007     Document Type: Erratum

References (30)

1. Debevec P.E., Taylor C.J., and Malik J. Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach. Proceedings of the 23th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH-96) (1996), Addison-Wesley, Reading, MA 11-20
2. Hartley R.I., and Zisserman A. Multiple view geometry in computer vision (2000), Cambridge University Press, Cambridge, UK
3. Ferreira A., Fonseca M.J., Jorge J.A., and Ramalho M. Mixing images and sketches for retrieving vector drawings. Eurographics Multimedia Workshop (2004), Eurographics Association, China 69-75
4. Caprile B., and Torre V. Using vanishing points for camera calibration. Int. J. Comput. Vision (IJCV) 4 2 (1990) 127-139
5. Straforini M., Coelho C., and Campani M. Extraction of vanishing points from images of indoor and outdoor scenes. Image Vision Comput. 11 2 (1993) 91-99
6. Lew Yan Voon L.F., Bolland P., Laligant O., Gorria P., Gremillet B., and Pillet L. Gradient-based discrete Hough transform for the detection and localization of defects in nondestructive inspection. Proceedings of the V Machine Vision Applications in Industrial Inspection vol. 3029 (1997), SPIE, San Jose, CA 140-146
7. Lee H.-J., Ahn H.-J., Song J.-H., and Park R.-H. Hough transform for line and plane detection based on the conjugate formulation. Proceedings of the IX Machine Vision Applications in Industrial Inspection vol. 4301 (2001), SPIE, San Jose, CA 244-252
8. R. Strzodka, I. Ihrke, M. Magnor, A graphics hardware implementation of the generalized Hough transform for fast object recognition, scale, and 3D pose detection, in: Proceedings of the International Conference on Image Analysis and Processing, 2003, pp. 188-193.
9. Karnieli A., Meisels A., Fisher L., and Arkin Y. Automatic extraction and evaluation of geological linear features from digital remote sensing data using a Hough transform. Photogramm. Eng. Remote Sensing 62 5 (1996) 525-531
10. P.V.C. Hough, Methods and means for recognizing complex patterns, U.S. Patent 3.069.654, 1962.
11. Duda R.O., and Hart P.E. Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15 1 (1972) 11-15
12. Princen J., Illingworth J., and Kittler J. A hierarchical approach to line extraction based on the Hough transform. Graphical Model Image Process. 52 1 (1990) 57-77
13. Yacoub S.B., and Jolion J. Hierarchical line extraction. IEE Vision Image Signal Process. 142 1 (1995) 7-14
14. Chung K.-L., Chen T.-C., and Yan W.-M. New memory- and computation-efficient Hough transform for detecting lines. Pattern Recognition 37 (2004) 953-963
15. Ho C.G., Young R.C.D., Bradfield C.D., and Chatwin C.R. A fast Hough transform for the parametrisation of straight lines using Fourier methods. Real-Time Imaging 2 (2000) 113-127
16. L. Lin, V.K. Jain, Parallel architectures for computing the Hough transform and CT image reconstruction, in: International Conference on Application Specific Array Processors, 1994, pp. 152-163.
17. Albanesi M.G., Ferretti M., and Rizzo D. Benchmarking Hough transform architectures for real-time. Real-Time Imaging 6 2 (2000) 155-172
18. Mayasandra K., Salehi S., Wang W., and Ladak H.M. A distributed arithmetic hardware architecture for real-time Hough-transform-based segmentation. Can. J. Electr. Comput. Eng. 30 4 (2005) 201-205
19. Canny J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8 6 (1986) 679-698
20. Ballard D.H. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 2 (1981) 111-122
21. Leavers V.F., and Sandler M.B. An efficient radon transform. Proceedings of the 4th International Conference on Pattern Recognition (ICPR-88) (1988), Springer, Cambridge, UK 380-389
22. Illingworth J., and Kittler J.V. A survey of the Hough transform. Graphical Model Image Process. 44 1 (1988) 87-116
23. Leavers V.F. Survey: which Hough transform?. Graphical Model Image Process. 58 2 (1993) 250-264
24. Lin X., and Otobe K. Hough transform algorithm for real-time pattern recognition using an artificial retina camera. Opt. Express 8 9 (2001) 503-508
25. Costa L.F., and Sandler M. A binary Hough transform and its efficient implementation in a systolic array architecture. Pattern Recognition Lett. 10 (1989) 329-334
26. Pope A.R., and Lowe D.G. Vista: a software environment for computer vision research. Proceedings of Computer Vision and Pattern Recognition (CVPR'94) (1994), IEEE Computer Society, Seattle, WA 768-772
27. Lowe D.G. Three-dimensional object recognition from single two-dimensional images. Artif. Intell. 31 (1987) 355-395 (section 4.6)
28. Draper N.R., and Smith H. Applied regression analysis (1966), Wiley, New York
29. Parratt L.G. Probability and experimental errors in science (1961), Wiley, New York
30. E.W. Weisstein, Bivariate normal distribution, 〈http://mathworld.wolfram.com/BivariateNormalDistribution.html〉, MathWorld, Last Update: Jun 2004.