A convolutional approach to reflection symmetry

Marcelo Cicconet; Vighnesh Birodkar; Mads Lund; Michael Werman; Davi Geiger

doi:10.1016/j.patrec.2017.03.022

A convolutional approach to reflection symmetry

Marcelo Cicconet, Vighnesh Birodkar, Mads Lund, Michael Werman, Davi Geiger

The Rachel and Selim Benin School of Engineering and Computer Science

The Hebrew University of Jerusalem

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

Abstract

We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. We release code and a new, larger image database.

Original language	English
Pages (from-to)	44-50
Number of pages	7
Journal	Pattern Recognition Letters
Volume	95
DOIs	https://doi.org/10.1016/j.patrec.2017.03.022
State	Published - 1 Aug 2017

Keywords

Mirror symmetry
Reflection symmetry

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patrec.2017.03.022

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title = "A convolutional approach to reflection symmetry",

abstract = "We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. We release code and a new, larger image database.",

keywords = "Mirror symmetry, Reflection symmetry",

author = "Marcelo Cicconet and Vighnesh Birodkar and Mads Lund and Michael Werman and Davi Geiger",

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AU - Cicconet, Marcelo

AU - Birodkar, Vighnesh

AU - Lund, Mads

AU - Werman, Michael

AU - Geiger, Davi

PY - 2017/8/1

Y1 - 2017/8/1

N2 - We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. We release code and a new, larger image database.

AB - We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. We release code and a new, larger image database.

KW - Mirror symmetry

KW - Reflection symmetry

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U2 - 10.1016/j.patrec.2017.03.022

DO - 10.1016/j.patrec.2017.03.022

M3 - مقالة

SN - 0167-8655

VL - 95

SP - 44

EP - 50

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

ER -

A convolutional approach to reflection symmetry

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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