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

Computer Science

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 (US)
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 - Aug 1 2017

Keywords

Mirror symmetry
Reflection symmetry

ASJC Scopus subject areas

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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SP - 44

EP - 50

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

ER -

A convolutional approach to reflection symmetry

Abstract

Keywords

ASJC Scopus subject areas

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