On the detection of structures in noisy pictures

Melvin Cohen; Godfried T. Toussaint

doi:10.1016/0031-3203(77)90020-6

On the detection of structures in noisy pictures

Melvin Cohen, Godfried T. Toussaint

Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

Hough proposed an algorithm for detecting lines in pictures. His algorithm, based on a slope-intercept parameterization of lines, was improved by Duda and Hart through the use of angle-radius parameterization. When pictures contain random noise that cannot be removed, the Duda-Hart procedure can yield unsatisfactory results. This paper presents two modifications of that procedure which compensate for noise. One method is applicable when the distribution of the noise is known and the other can be used when it is not. The proposed modification is also illustrated for circle detection.

Original language	English (US)
Pages (from-to)	95-98
Number of pages	4
Journal	Pattern Recognition
Volume	9
Issue number	2
DOIs	https://doi.org/10.1016/0031-3203(77)90020-6
State	Published - Jul 1977

Keywords

Detection in noise
Geometrical probability
Pattern recognition
Picture processing

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/0031-3203(77)90020-6

Cite this

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title = "On the detection of structures in noisy pictures",

abstract = "Hough proposed an algorithm for detecting lines in pictures. His algorithm, based on a slope-intercept parameterization of lines, was improved by Duda and Hart through the use of angle-radius parameterization. When pictures contain random noise that cannot be removed, the Duda-Hart procedure can yield unsatisfactory results. This paper presents two modifications of that procedure which compensate for noise. One method is applicable when the distribution of the noise is known and the other can be used when it is not. The proposed modification is also illustrated for circle detection.",

keywords = "Detection in noise, Geometrical probability, Pattern recognition, Picture processing",

author = "Melvin Cohen and Toussaint, {Godfried T.}",

note = "Funding Information: Before describing our modifications it is instructive to review the procedure of Duda and Hart ~3). Their procedure, as well as ours, is based on the normal parameterization of a line. Such a parameterization * This work was supported by the National Research Council of Canada under grant NRC-A9293. An earlier version of this paper was presented at the CSCSI Workshop on Computational Studies of Intelligence, University of Ottowa, 28 29 May, 1975.",

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AU - Cohen, Melvin

AU - Toussaint, Godfried T.

N1 - Funding Information: Before describing our modifications it is instructive to review the procedure of Duda and Hart ~3). Their procedure, as well as ours, is based on the normal parameterization of a line. Such a parameterization * This work was supported by the National Research Council of Canada under grant NRC-A9293. An earlier version of this paper was presented at the CSCSI Workshop on Computational Studies of Intelligence, University of Ottowa, 28 29 May, 1975.

PY - 1977/7

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N2 - Hough proposed an algorithm for detecting lines in pictures. His algorithm, based on a slope-intercept parameterization of lines, was improved by Duda and Hart through the use of angle-radius parameterization. When pictures contain random noise that cannot be removed, the Duda-Hart procedure can yield unsatisfactory results. This paper presents two modifications of that procedure which compensate for noise. One method is applicable when the distribution of the noise is known and the other can be used when it is not. The proposed modification is also illustrated for circle detection.

AB - Hough proposed an algorithm for detecting lines in pictures. His algorithm, based on a slope-intercept parameterization of lines, was improved by Duda and Hart through the use of angle-radius parameterization. When pictures contain random noise that cannot be removed, the Duda-Hart procedure can yield unsatisfactory results. This paper presents two modifications of that procedure which compensate for noise. One method is applicable when the distribution of the noise is known and the other can be used when it is not. The proposed modification is also illustrated for circle detection.

KW - Detection in noise

KW - Geometrical probability

KW - Pattern recognition

KW - Picture processing

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On the detection of structures in noisy pictures

Abstract

Keywords

ASJC Scopus subject areas

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