Probabilistic and non-probabilistic Hough transforms: overview and comparisons

Kälviäinen, Heikki; Hirvonen, Petri; Xu, Lei; Oja, Erkki

doi:10.1016/0262-8856(95)99713-b

Cited by 191 publications

(64 citation statements)

References 19 publications

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“…Thus, a point can contribute to more than one bin in the standard HT process. A general comparison between probabilistic and non-probabilistic HT variants can be found in [14].…”

Section: Introductionmentioning

confidence: 99%

Modeling sets of unordered points using highly eccentric ellipses

Gerogiannis

Nikou

Likas

2014

EURASIP J. Adv. Signal Process.

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An algorithm for modeling a set of unordered two-dimensional points by line segments is presented. The points are modeled by highly eccentric ellipses, and line segments are extracted by the major axes of these elongated ellipses. At first, a single ellipse is fitted to points which is then iteratively split to a large number of highly eccentric ellipses to cover the set of points. Then, a merge process follows in order to combine neighboring ellipses with almost collinear major axes to reduce the complexity of the model. Experimental results on various databases show that the proposed scheme is an efficient technique for modeling unordered sets of points and shapes by line segments. A computer vision application of the method is also presented regarding the detection of retinal fundus image features, such as end-points, junctions, and crossovers.

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“…Thus, a point can contribute to more than one bin in the standard HT process. A general comparison between probabilistic and non-probabilistic HT variants can be found in [14].…”

Section: Introductionmentioning

confidence: 99%

Modeling sets of unordered points using highly eccentric ellipses

Gerogiannis

Nikou

Likas

2014

EURASIP J. Adv. Signal Process.

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“…Due to its importance, many researchers have devoted themselves to developing good line segment detection methods. The methods proposed up to date can be categorized into following three approaches: Approach 1: Hough transform based approach [1,2,3]. The biggest advantage of this approach is to enable us to detect collinear edge pixels even though each of them is isolated.…”

Section: Introductionmentioning

confidence: 99%

A New Line Segment Grouping Method for Finding Globally Optimal Line Segments

Jang

Hong

2000

Biologically Motivated Computer Vision

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Abstract. In this paper we propose a new method for extracting line segments from edge images. Our method basically follows a line segment grouping approach. This approach has many advantages over a Hough transform based approach in a practical situation. However, since its process is purely local, it does not provide a mechanism for finding more favorable line segments from a global point of view. Our method overcomes the local nature of the conventional line segment grouping approach, while retaining most of its advantages, by incorporating some useful concepts of the Hough transform based approach into the line segment grouping approach. We performed a series of tests to compare the performance of our method with those of other six existing methods. Throughout the tests our method ranked almost highest both in detection rate and computation time.

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“…Unlike the Randomised Hough Transform (RHT) BMVC 1998 doi:10.5244/C.12. 26 class of algorithms (see [3] for an overview of the Hough Transform), the Probabilistic Hough Transform (PHT) and the Standard Hough Transform (SHT) share the same one-to-many voting pattern and the representation of the accumulator array.…”

Section: Introductionmentioning

confidence: 99%

Progressive Probabilistic Hough Transform

Galambos

1998

Procedings of the British Machine Vision Conference 1998

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In the paper we present the Progressive Probabilistic Hough Transform (PPHT). Unlike the Probabilistic Hough Transform [4] where Standard Hough Transform is performed on a pre-selected fraction of input points, PPHT minimises the amount of computation needed to detect lines by exploiting the difference in the fraction of votes needed to reliably detect lines with different numbers of supporting points. The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the Probabilistic Hough Transform; it is a function of the inherent complexity of data.The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is interleaved. The most salient features are likely to be detected first. Experiments show PPHT has, in many circumstances, advantages over the Standard Hough Transform.

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Probabilistic and non-probabilistic Hough transforms: overview and comparisons

Cited by 191 publications

References 19 publications

Modeling sets of unordered points using highly eccentric ellipses

Modeling sets of unordered points using highly eccentric ellipses

A New Line Segment Grouping Method for Finding Globally Optimal Line Segments

Progressive Probabilistic Hough Transform

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