Cyclic Diffeomorphic Transformer Nets For Contour Alignment.

Ilya Kaufman, Ron Shapira Weber, Oren Freifeld

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Shape analysis is a key task in image processing. A common method for representing a 2D shape is via a polygon, where the latter is a discretized version of the contour outlining the shape. However, due to the problem of curve reparameterization (i. e., points “sliding” along the contour), even if several shapes are very similar, their representations might be misleadingly far from each other. This misalignment problem confounds shape analysis. As a remedy, we propose a deep-learning framework, based on the recently-proposed diffeomorphic transformers nets. The proposed method handles either a single class (in an unsupervised manner) or multiple classes (in a semi-supervised manner), and is amenable to the warp-around effect exhibited in closed contours. Moreover, unlike typical alignment methods unrelated to learning, the proposed method aligns not only the original (“training”) shapes but also generalizes to test shapes (even if no class labels are given during the test). Our code is publicly available at https://git;
Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781665441155
StatePublished - 23 Aug 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: 19 Sep 202122 Sep 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States


  • Contour alignment
  • Deep learning
  • Diffeomorphisms
  • Nonlinear time warping
  • Shape analysis

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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