A new curve detection method: Randomized Hough transform (RHT)

Xu, Lei; Oja, Erkki; Kultanen, P.

doi:10.1016/0167-8655(90)90042-z

Cited by 915 publications

(434 citation statements)

References 4 publications

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“…Since this method only uses a small part of image data, it significantly reduces the computation time and memory storage. In the Randomized Hough Transform, Xu et al [8] proposed a method for extracting curves from binarised edge images. For a curve expressed by an n parameter equation, they selected n pixels at random and mapped them into one point of the parameter space, instead of transforming one pixel into an n Ϫ 1 dimensional hypersurface in the parameter space as the standard HT and some of its variants do.…”

Section: Literature Researchmentioning

confidence: 99%

Randomised hough transform with error propagation for line and circle detection

Xie

2003

Pattern Analysis & Applications

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In this paper, we introduce a new Randomised Hough Transform aimed at improving curve detection accuracy and robustness, as well as computational efficiency. Robustness and accuracy improvement is achieved by analytically propagating the errors with image pixels to the estimated curve parameters. The errors with the curve parameters are then used to determine the contribution of pixels to the accumulator array. The computational efficiency is achieved by mapping a set of points near certain selected seed points to the parameter space at a time. Statistically determined, the seed points are points that are most likely located on the curves and that produce the most accurate curve estimation. Further computational advantage is achieved by performing progressive detection. Examples of detection of lines using the proposed technique are given in the paper. The concept can be extended to non-linear curves such as circles and ellipses.

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Section: Literature Researchmentioning

confidence: 99%

Randomised hough transform with error propagation for line and circle detection

Xie

2003

Pattern Analysis & Applications

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“…The di culty arises because most robust estimators, including RANSAC, are designed to extract a single model. Mode finding in parameter space and Randomized Hough Transform (RHT) [27], on the contrary, copes naturally with multiple structures, but cannot deal with high percentage of gross outliers, especially as the number of models grows and the distribution of inliers per model is uneven.…”

Section: J-linkagementioning

confidence: 99%

Image-consistent patches from unstructured points with J-linkage

Toldo

Fusiello

2013

Image and Vision Computing

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Going from unstructured cloud of points to surfaces is a challenging problem. However, as points are produced by a structure-and-motion pipeline, image-consistency is a powerful clue that comes to the rescue. In this paper we present a method for extracting planar patches from an unstructured cloud of points, based on the detection of image-consistent planar patches with J-linkage, a robust algorithm for multiple models fitting. The method integrates several constraints inside J-linkage, optimizes the position of the points with regard to image-consistency and deploys a hierarchical processing scheme that decreases the computational load. With respect to previous work this approach has the advantage of starting from sparse data. Several results show the e↵ectiveness of the proposed approach.

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“…The Hough transform and its randomized version [9] can be regarded as well as consensus-oriented algorithms. In these approaches the parameter space Θ is approximated as a quotient space Ξ = Θ/ ∼ in which models are represented as equivalence classes of similar structures.…”

Section: Consensus Analysismentioning

confidence: 99%

Multiple structure recovery with maximum coverage

Magri

Fusiello

2017

Machine Vision and Applications

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We present a general framework for geometric model fitting based on a set coverage formulation, that caters for intersecting structures and outliers in a simple and principled manner. The multi-model fitting problem is formulated in terms of the optimization of a consensus-based global cost function, which allows to sidestep the pitfalls of preference approaches based on clustering and to avoid the difficult trade-off between data fidelity and complexity of other optimization formulations. Two especially appealing characteristics of this method are the ease with which it can be implemented and its modularity with respect to the solver and to the sampling strategy. Few intelligible parameters need to be set and tuned, namely the inlier threshold and the number of desired models. The summary of the experiments is that our method compares favourably with its competitors overall, and it is always either the best performer or almost on par with the best performer in specific scenarios.

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A new curve detection method: Randomized Hough transform (RHT)

Cited by 915 publications

References 4 publications

Randomised hough transform with error propagation for line and circle detection

Randomised hough transform with error propagation for line and circle detection

Image-consistent patches from unstructured points with J-linkage

Multiple structure recovery with maximum coverage

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