Abstract
The Hough transform is a method for the detection of many lines on a plane [1,2,3,4]. This method achieves line detection by converting the line fitting problem on an imaging plane to a peak search problem in an accumulator space using the voting procedure. Although the Hough transform provides a method for line detection, this transform can not detect line segments. For the detection of line segments, it is necessary to detect both endpoints of each line segment. The detection of pairs of endpoints of line segments is mainly performed using the point following procedure by local window operation along each line; that is, assuming the connectivity of digitized points, the algorithm follows a series of sample points which should lie on a line. The method is, however, equivalent to a whole area search in the worst case, because it is necessary to investigate the connectivity of all sample points in the region of interest, point by point.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ballard, D. and Brown, Ch. M., Computer Vision, Prentice-Hall; New Jersey, 1982.
Xu, L. and Oja, E., Randomized Hough Transform (RHT): Basic mechanism, algorithm, and computational complexities, CVGIP:Image Understanding, 57, 131–154, (1993).
Levers, V.F., Which Hough transform? CVGIP:Image Understanding, 58, 250–264, (1993).
Kälviäinen, H., Hirvonen, P., Xu, L., and Oja. E., Probabilistic and non-probabilistic Hough transforms: Overview and comparisons, Image and Vision Computing, 13, 239–252, (1995).
Sommerville, D.M.Y., Analytical Geometry of Three-dimensions, Cambridge University Press; Cambridge, 1934.
Cox, D., Little, J., and O’Shea, D., Ideals, Varieties, and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra, Springer-Verlag; New York, 1992.
Motwani, R. and Raghavan, P., Randomized Algorithms, Cambridge University Press; Cambridge, 1995.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer-Verlag/Wien
About this paper
Cite this paper
Imiya, A. (1997). Order of points on a line segment. In: Solina, F., Kropatsch, W.G., Klette, R., Bajcsy, R. (eds) Advances in Computer Vision. Advances in Computing Science. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6867-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6867-7_7
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83022-2
Online ISBN: 978-3-7091-6867-7
eBook Packages: Springer Book Archive