Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild

Christopher Funk, Yanxi Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Humans take advantage of real world symmetries for various tasks, yet capturing their superb symmetry perception mechanism with a computational model remains elusive. Motivated by a new study demonstrating the extremely high inter-person accuracy of human perceived symmetries in the wild, we have constructed the first deeplearning neural network for reflection and rotation symmetry detection (Sym-NET), trained on photos from MS-COCO (Microsoft-Common Object in COntext) dataset with nearly 11K consistent symmetry-labels from more than 400 human observers. We employ novel methods to convert discrete human labels into symmetry heatmaps, capture symmetry densely in an image and quantitatively evaluate Sym-NET against multiple existing computer vision algorithms. On CVPR 2013 symmetry competition testsets and unseen MSCOCO photos, Sym-NET significantly outperforms all other competitors. Beyond mathematically well-defined symmetries on a plane, Sym-NET demonstrates abilities to identify viewpoint-varied 3D symmetries, partially occluded symmetrical objects, and symmetries at a semantic level.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages793-803
Number of pages11
ISBN (Electronic)9781538610329
DOIs
StatePublished - Dec 22 2017
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: Oct 22 2017Oct 29 2017

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2017-October
ISSN (Print)1550-5499

Other

Other16th IEEE International Conference on Computer Vision, ICCV 2017
CountryItaly
CityVenice
Period10/22/1710/29/17

Fingerprint

Labels
Computer vision
Semantics
Neural networks

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Funk, C., & Liu, Y. (2017). Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 (pp. 793-803). [8237354] (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2017.92
Funk, Christopher ; Liu, Yanxi. / Beyond Planar Symmetry : Modeling Human Perception of Reflection and Rotation Symmetries in the Wild. Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 793-803 (Proceedings of the IEEE International Conference on Computer Vision).
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Funk, C & Liu, Y 2017, Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild. in Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017., 8237354, Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 793-803, 16th IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 10/22/17. https://doi.org/10.1109/ICCV.2017.92

Beyond Planar Symmetry : Modeling Human Perception of Reflection and Rotation Symmetries in the Wild. / Funk, Christopher; Liu, Yanxi.

Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 793-803 8237354 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Funk C, Liu Y. Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 793-803. 8237354. (Proceedings of the IEEE International Conference on Computer Vision). https://doi.org/10.1109/ICCV.2017.92