Abstract
Straight-line detection is important in several fields such as robotics,
remote sensing, and imagery. The objective of this paper is to present
several methods, old and new, used for straight-line detection. We
begin by reviewing the standard Hough transform (SHT), then three
new methods are suggested: the revisited Hough transform (RHT), the
parallel-axis transform (PAT), and the circle transform (CT). These
transforms utilize a point-line duality to detect straight lines
in an image. The RHT and the PAT should be faster than the SHT and
the CT because they use line segments whereas the SHT uses sinusoids
and CT uses circles. Moreover, the PAT, RHT, and CT use additions
and multiplications whereas the SHT uses trigonometric functions
(sine and cosine) for calculation. To compare the methods we analyze
the distribution of the frequencies in the accumulators and observe
the effect on the detection of false local maxima. We also compare
the robustness to noise of the four transforms. Finally, an example
with a real image is given.
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