IEICE TRANSACTIONS on Information
Self-Clustering Symmetry Detection
Summary :
This paper presents a self-clustering algorithm to detect symmetry in images. We combine correlations of orientations, scales and descriptors as a triple feature vector to evaluate each feature pair while low confidence pairs are regarded as outliers and removed. Additionally, all confident pairs are preserved to extract potential symmetries since one feature point may be shared by different pairs. Further, each feature pair forms one cluster and is merged and split iteratively based on the continuity in the Cartesian and concentration in the polar coordinates. Pseudo symmetric axes and outlier midpoints are eliminated during the process. Experiments demonstrate the robustness and accuracy of our algorithm visually and quantitatively.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.9 pp.2359-2362
- Publication Date
- 2012/09/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.2359
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Bei HE, Guijin WANG, Chenbo SHI, Xuanwu YIN, Bo LIU, Xinggang LIN, "Self-Clustering Symmetry Detection" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 9, pp. 2359-2362, September 2012, doi: 10.1587/transinf.E95.D.2359.
Abstract: This paper presents a self-clustering algorithm to detect symmetry in images. We combine correlations of orientations, scales and descriptors as a triple feature vector to evaluate each feature pair while low confidence pairs are regarded as outliers and removed. Additionally, all confident pairs are preserved to extract potential symmetries since one feature point may be shared by different pairs. Further, each feature pair forms one cluster and is merged and split iteratively based on the continuity in the Cartesian and concentration in the polar coordinates. Pseudo symmetric axes and outlier midpoints are eliminated during the process. Experiments demonstrate the robustness and accuracy of our algorithm visually and quantitatively.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.2359/_p
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@ARTICLE{e95-d_9_2359,
author={Bei HE, Guijin WANG, Chenbo SHI, Xuanwu YIN, Bo LIU, Xinggang LIN, },
journal={IEICE TRANSACTIONS on Information},
title={Self-Clustering Symmetry Detection},
year={2012},
volume={E95-D},
number={9},
pages={2359-2362},
abstract={This paper presents a self-clustering algorithm to detect symmetry in images. We combine correlations of orientations, scales and descriptors as a triple feature vector to evaluate each feature pair while low confidence pairs are regarded as outliers and removed. Additionally, all confident pairs are preserved to extract potential symmetries since one feature point may be shared by different pairs. Further, each feature pair forms one cluster and is merged and split iteratively based on the continuity in the Cartesian and concentration in the polar coordinates. Pseudo symmetric axes and outlier midpoints are eliminated during the process. Experiments demonstrate the robustness and accuracy of our algorithm visually and quantitatively.},
keywords={},
doi={10.1587/transinf.E95.D.2359},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Self-Clustering Symmetry Detection
T2 - IEICE TRANSACTIONS on Information
SP - 2359
EP - 2362
AU - Bei HE
AU - Guijin WANG
AU - Chenbo SHI
AU - Xuanwu YIN
AU - Bo LIU
AU - Xinggang LIN
PY - 2012
DO - 10.1587/transinf.E95.D.2359
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2012
AB - This paper presents a self-clustering algorithm to detect symmetry in images. We combine correlations of orientations, scales and descriptors as a triple feature vector to evaluate each feature pair while low confidence pairs are regarded as outliers and removed. Additionally, all confident pairs are preserved to extract potential symmetries since one feature point may be shared by different pairs. Further, each feature pair forms one cluster and is merged and split iteratively based on the continuity in the Cartesian and concentration in the polar coordinates. Pseudo symmetric axes and outlier midpoints are eliminated during the process. Experiments demonstrate the robustness and accuracy of our algorithm visually and quantitatively.
ER -
English
