Dense correspondence using multilevel segmentation and affine transformation

Sungil Choi, Kihong Park, Seungryong Kim, Kwanghoon Sohn

Research output: Contribution to journalConference articlepeer-review


Establishing dense correspondence fields between images is an important issue with many computer vision and computational photography applications. Although there have been significant advances in estimating dense correspondence fields, it is still difficult to find reliable correspondence fields between a pair of images because of their geometric and photometric variations. In this paper, we propose an unified framework for establishing dense correspondences, consisting of sparse matching, multilevel segmentation, and derivation of affine transformations. Dense correspondence fields are estimated via winner-takes-all (WTA) optimization by utilizing affine transformations, derived from spare matching and multilevel segmentation. The proposed method reduces a size of label search space dramatically, and further extends the dimension of label search space, by leveraging affine transformation with the multilevel segmentation scheme. Our robust dense correspondence estimation is evaluated on extensive experiments, which show that our approach outperforms the state-of-the-art methods both qualitatively and quantitatively.

Original languageEnglish
JournalIS and T International Symposium on Electronic Imaging Science and Technology
Publication statusPublished - 2016
EventImage Processing: Machine Vision Applications IX 2016 - San Francisco, United States
Duration: 2016 Feb 142016 Feb 18

Bibliographical note

Publisher Copyright:
© 2016 Society for Imaging Science and Technology.

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics


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