Circle Detection Based on Voting for Maximum Compatibility

Yuanqi SU; Yuehu LIU; Xiao HUANG

doi:10.1587/transinf.E95.D.1636

Circle Detection Based on Voting for Maximum Compatibility

Yuanqi SU, Yuehu LIU, Xiao HUANG

Full Text Views

0

Share
Cite this

Summary :

We present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent and -dependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precision-recall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.

Publication: IEICE TRANSACTIONS on Information Vol.E95-D No.6 pp.1636-1645

Publication Date: 2012/06/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E95.D.1636

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

Cite this

Copy

Yuanqi SU, Yuehu LIU, Xiao HUANG, "Circle Detection Based on Voting for Maximum Compatibility" in IEICE TRANSACTIONS on Information, vol. E95-D, no. 6, pp. 1636-1645, June 2012, doi: 10.1587/transinf.E95.D.1636.
Abstract: We present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent and -dependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precision-recall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E95.D.1636/_p

Copy

@ARTICLE{e95-d_6_1636,
author={Yuanqi SU, Yuehu LIU, Xiao HUANG, },
journal={IEICE TRANSACTIONS on Information},
title={Circle Detection Based on Voting for Maximum Compatibility},
year={2012},
volume={E95-D},
number={6},
pages={1636-1645},
abstract={We present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent and -dependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precision-recall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.},
keywords={},
doi={10.1587/transinf.E95.D.1636},
ISSN={1745-1361},
month={June},}

Copy

TY - JOUR
TI - Circle Detection Based on Voting for Maximum Compatibility
T2 - IEICE TRANSACTIONS on Information
SP - 1636
EP - 1645
AU - Yuanqi SU
AU - Yuehu LIU
AU - Xiao HUANG
PY - 2012
DO - 10.1587/transinf.E95.D.1636
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2012
AB - We present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent and -dependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precision-recall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.
ER -