Editing Hough transform (section)

==Limitations==
The Hough transform is only efficient if a high number of votes fall in the right bin, so that the bin can be easily detected amid the background noise. This means that the bin must not be too small, or else some votes will fall in the neighboring bins, thus reducing the visibility of the main bin.<ref>{{cite web|url=http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm |title=Image Transforms - Hough Transform |publisher=Homepages.inf.ed.ac.uk |access-date=2009-08-17}}</ref>

Also, when the number of parameters is large (that is, when we are using the Hough transform with typically more than three parameters), the average number of votes cast in a single bin is very low, and those bins corresponding to a real figure in the image do not necessarily appear to have a much higher number of votes than their neighbors. The complexity increases at a rate of <!--Big-O(A<sup>m - 2</sup>)--><math>\mathcal{O}\left({A^{m-2}}\right)</math> with each additional parameter, where <math>A</math> is the size of the image space and <math>m</math> is the number of parameters. (Shapiro and Stockman, 310) Thus, the Hough transform must be used with great care to detect anything other than lines or circles.

Finally, much of the efficiency of the Hough transform is dependent on the quality of the input data: the edges must be detected well for the Hough transform to be efficient. Use of the Hough transform on noisy images is a very delicate matter and generally, a denoising stage must be used before. In the case where the image is corrupted by speckle, as is the case in radar images, the [[Radon transform]] is sometimes preferred to detect lines, because it attenuates the noise through summation.