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International Symposium on Visual Computing

ISVC 2018: Advances in Visual Computing pp 139–146Cite as

Rotation Symmetry Object Classification Using Structure Constrained Convolutional Neural Network

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11241))

Abstract

Rotation symmetry is a salient visual clue in describing and recognizing an object or a structure in an image. Recently, various rotation symmetry detection methods have been proposed based on key point feature matching scheme. However, hand crafted representation of rotation symmetry structure has shown limited performance. On the other hand, deep learning based approach has been rarely applied to symmetry detection due to the huge diversity in the visual appearance of rotation symmetry patterns. In this work, we propose a new framework of convolutional neural network based on two core layers: rotation invariant convolution (RI-CONV) layer and symmetry structure constrained convolution (SSC-CONV) layer. Proposed network learns structural characteristic from image samples regardless of their appearance diversity. Evaluation is conducted on 32,000 images (after augmentation) of our rotation symmetry classification data set.

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Author information

Authors and Affiliations

  1. Kyunghee University, Yong-in Gyeonggi, 17104, Korea

    Seunghwa Yu & Seugnkyu Lee

Authors
  1. Seunghwa Yu
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  2. Seugnkyu Lee
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Corresponding author

Correspondence to Seugnkyu Lee .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, USA

    Richard Boyle

  3. University of Nevada, Reno, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, USA

    Darko Koracin

  5. DARPA, Arlington, USA

    Matt Turek

  6. University of Utah, Salt Lake City, USA

    Srikumar Ramalingam

  7. National University of Defense Technology, Changsha, China

    Kai Xu

  8. Microsoft Research Asia, Beijing, China

    Stephen Lin

  9. Bosch Research, Farmington Hills, MI, USA

    Bilal Alsallakh

  10. University of North Carolina at Charlotte, Charlotte, USA

    Jing Yang

  11. Microsoft Research, Redmond, USA

    Eduardo Cuervo

  12. University of Colorado at Colorado Springs, Colorado Springs, USA

    Jonathan Ventura

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Cite this paper

Yu, S., Lee, S. (2018). Rotation Symmetry Object Classification Using Structure Constrained Convolutional Neural Network. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_13

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  • DOI: https://doi.org/10.1007/978-3-030-03801-4_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03800-7

  • Online ISBN: 978-3-030-03801-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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