Journal ArticleDOI
A probabilistic algorithm for computing Hough transforms
James R. Bergen,Haim Shvaytser +1 more
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TLDR
An efficient probabilistic algorithm for a Monte-Carlo approximation to the Hough transform that requires substantially less computation and storage than the standard Houghtransform when applied to patterns that are easily recognized by humans.About:
This article is published in Journal of Algorithms.The article was published on 1991-12-01. It has received 80 citations till now. The article focuses on the topics: Hough transform & Scale-invariant feature transform.read more
Citations
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Journal ArticleDOI
A survey of Hough Transform
TL;DR: A survey of Hough Transform and its variants, their limitations and the modifications made to overcome them, the implementation issues in software and hardware, and applications in various fields is done.
Journal ArticleDOI
Robust Detection of Lines Using the Progressive Probabilistic Hough Transform
TL;DR: The progressive probabilistic Hough transform minimizes 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.
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Robust clustering with applications in computer vision
TL;DR: A clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed that was successfully applied to several computer vision problems formulated in the feature space paradigm: multithresholding of gray level images, analysis of the Hough space, and range image segmentation.
Technical Report - Randomized Hough Transform: Improved Ellipse Detection with Comparison
TL;DR: An algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform is described, found to give improvements in accuracy, and a reduction in computation time and the number of false alarms detected.
Journal ArticleDOI
Randomized Hough transform: improved ellipse detection with comparison
TL;DR: In this paper, the authors describe an algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform (RHT) and compare it with three other Hough-based algorithms.
References
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Journal ArticleDOI
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Journal ArticleDOI
Use of the Hough transformation to detect lines and curves in pictures
Richard O. Duda,Peter E. Hart +1 more
TL;DR: It is pointed out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further, and how the method can be used for more general curve fitting.
Book
An introduction to probability theory
TL;DR: The authors introduce probability theory for both advanced undergraduate students of statistics and scientists in related fields, drawing on real applications in the physical and biological sciences, and make probability exciting." -Journal of the American Statistical Association
Journal ArticleDOI
A survey of the Hough transform
John Illingworth,Josef Kittler +1 more
TL;DR: This survey will provide a useful guide to quickly acquaint researchers with the main literature in this research area and it seems likely that the Hough transform will be an increasingly used technique.