Approximating point-set images by line segments using a variation of the Hough transform

Thrift, Philip; Dunn, Stanley M.

doi:10.1016/s0734-189x(83)80050-4

Cited by 36 publications

(10 citation statements)

References 5 publications

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“…Modern forms of the Hough Transform, e.g. [6,10,15], provide some compensation for location errors in the data, thus improving the performance of the algorithm. These variants do not provide special treatment for location errors which are due to image digitization; this is justified by the ability of modern edge detection schemes to offer sub-pixel accuracy when the levels of other sources of image noise are low.…”

Section: A Variation Of the Hough Transform That Is Aimed At Detectinmentioning

confidence: 99%

“…These variants do not provide special treatment for location errors which are due to image digitization; this is justified by the ability of modern edge detection schemes to offer sub-pixel accuracy when the levels of other sources of image noise are low. In this paper, [6,10,15] and related versions arc referred to as "Analog Hough Transforms", since they are intended to detect straight lines in the analog "pre-image".…”

Section: A Variation Of the Hough Transform That Is Aimed At Detectinmentioning

confidence: 99%

“…Recently, Kiryati and Bruckstein [6] studied an extended Hough Transform [15] that provides compensation for edge-point location errors, and using a signaltheoretic analysis were finally able to show how its resolution depends on the quantization of the parameter space, i.e. on the number of accumulators.…”

Section: Analog Hough Transformmentioning

confidence: 99%

“…In particular, it has been suggested [6,15] that the indicator function (9) be replaced by where C(-) is an "influence function" to be discussed in the sequel. This results with a modified continuous-support Hough Transform (12) z(p,9) can be point-sampled by assigning accumulators to a discrete set of sampling points, and evaluating (12) only at these points.…”

Section: Analog Hough Transformmentioning

confidence: 99%

“…A A =a 2n (15) where 0.5 < a < 1 is a constant. Using the influence function (13) and the convention that its effective bandwidth is triple the square root of the normalized second order energy moment of its Fourier Transform,…”

Section: Analog Hough Transformmentioning

confidence: 99%

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Digital or analog Hough Transform?

Kiryati¹,

Lindenbaum²,

Bruckstein³

1990

Procedings of the British Machine Vision Conference 1990

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A variation of the Hough Transform that is aimed at detecting digital lines has been recently suggested. Other Hough algorithms are intended to detect straight lines in the anaThe Hough Transform [2,4] is a well known technique for recognizing predefined features in edge maps. In this paper, the Hough Transform for detecting straight lines is considered.Most Hough algorithms consist of an incrementation stage, in which each edge point "votes" for the parameter-pairs of all possible straight lines on which it can lie, and an exhaustive search for peaks. These correspond to large collinear sets of edge-points.Originally, the slope-intercept (m,b) parametrization of straight lines had been employed in the Hough Transform. It has the advantage that an edge point corresponds to a straight line in the parameter space, thus voting is simple. Its drawback is that the parameter space is unbounded, implying some theoretical and practical difficulties. With normal (p,0) parametrization of straight lines, as suggested by [2], an edge point corresponds to a sinusoid in the parameter space, thus voting is somewhat more complex. The normal parametrization has the advantage that a bounded image leads to a bounded parameter space. Other straight-line parametrizations have also been suggested, see [4,11,17].In most implementations of the Hough algorithm the parameter space is represented by a rectangular accumulator array, such that each accumulator corresponds to a rectangular, constant size domain in the parameter space. The quantization of the parameter space greatly influences the resolution and detection capabilities of the algorithm, as well as the computational and storage requirements; see [6,16].

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Section: A Variation Of the Hough Transform That Is Aimed At Detectinmentioning

confidence: 99%

Section: A Variation Of the Hough Transform That Is Aimed At Detectinmentioning

confidence: 99%