Guaranteed Convergence of the Hough Transform

Soffer, Menashe; Kiryati, Nahum

doi:10.1006/cviu.1997.0557

Cited by 22 publications

(11 citation statements)

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“…Convergence is based on the well-behavedness of the objective function. Theoretical convergence properties in a related case were studied in [18]. Extension of their results is necessary.…”

Section: Discussionmentioning

confidence: 99%

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Heteroscedastic Hough Transform (HtHT): An Efficient Method for Robust Line Fitting in the ‘Errors in the Variables’ Problem

Kiryati

Bruckstein

2000

Computer Vision and Image Understanding

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“…Convergence is based on the well-behavedness of the objective function. Theoretical convergence properties in a related case were studied in [18]. Extension of their results is necessary.…”

Section: Discussionmentioning

confidence: 99%

“…One is the level of certainty that the coarse-to-fine grid search converges to the true global minimum of the objective function, i.e., to the best line. Convergence analysis of coarse-to-fine grid search in a similar robust line-fitting problem has been studied in [18]. The other issue is computational.…”

Section: Problem Statementmentioning

confidence: 99%

Heteroscedastic Hough Transform (HtHT): An Efficient Method for Robust Line Fitting in the ‘Errors in the Variables’ Problem

Kiryati

Bruckstein

2000

Computer Vision and Image Understanding

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“…Soffer and Kiryati [42] also examine continuous kernel functions. By considering the Hough transform as an optimization problem, they are able to examine under what conditions the Hough transform can be guaranteed to converge to the correct solution(s).…”

Section: Error In the Hough Transformmentioning

confidence: 99%

Constrained Hough Transforms for Curve Detection

Olson

1999

Computer Vision and Image Understanding

103

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“…1) the "bow-tie" method to compensate for uncertainty in edge orientations, (see [5]); 2) the use of Average Shifted Histograms (see [14]) to compensate for problems in accurately estimating parameters due to the discretization of the parameter space; and 3) the many very recent techniques introduced in [6], [16], [15], [1], and [3].…”

Section: Center-finding Experimentsmentioning

confidence: 99%

A method to detect and characterize ellipses using the Hough transform

Bennett

Burridge

Saito

1999

IEEE Trans. Pattern Anal. Machine Intell.

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Abstract-In this paper we describe a new technique for detecting and characterizing ellipsoidal shapes automatically from any type of image. This technique is a single pass algorithm which can extract any group of ellipse parameters or characteristics which can be computed from those parameters without having to detect all five parameters for each ellipsoidal shape. Moreover, the method can explicitly incorporate any a priori knowledge the user may have concerning ellipse parameters. The method is based on techniques from Projective Geometry and on the Hough Transform. This technique can significantly reduce interpretation and computation time by automatically extracting only those features or geometric parameters of interest from images and making exact use of a priori information.

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Guaranteed Convergence of the Hough Transform

Cited by 22 publications

References 20 publications

Heteroscedastic Hough Transform (HtHT): An Efficient Method for Robust Line Fitting in the ‘Errors in the Variables’ Problem

Heteroscedastic Hough Transform (HtHT): An Efficient Method for Robust Line Fitting in the ‘Errors in the Variables’ Problem

Constrained Hough Transforms for Curve Detection

A method to detect and characterize ellipses using the Hough transform

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