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Adaptive Deflation Stopped by Barrier Structure for Equating Shape Topologies Under Topological Noise

  • Asli Genctav
  • Sibel Tari
Chapter
Part of the Association for Women in Mathematics Series book series (AWMS, volume 12)

Abstract

Using level sets of a pair of transformations, we adaptively bring two shapes to be matched to a comparable topology prior to a correspondence search. One of the transformations readily provides a central structure for each shape. We utilize the central structure as a reference volume for scale normalization. By adaptively dilating the central structure with the help of the second transformation, we construct what we refer to as the barrier structure. The barrier structure is used to automatically stop topology equating adaptive deflations. Illustrative experiments using different datasets demonstrate that our approach provides robust solutions for the topological noise caused by localized touches or spurious links that connect different shape parts.

Notes

Acknowledgements

The work is funded by TUBITAK under grant 112E208. We thank Adrian Hilton for sharing the flashkick sequence [13] and i3DPost Multi-View Human Action datasets [7].

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Copyright information

© The Author(s) and the Association for Women in Mathematics 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey

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