ANCC flow: Adaptive normalized cross-correlation with evolving guidance aggregation for dense correspondence estimation

Seungryong Kim, Dongbo Min, Kwanghoon Sohn

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

Abstract

Adaptive normalized cross-correlation (ANCC) cost function works well between images under photometric distortions, but its heavy computational burden often limits its applications. To overcome this limitation, this paper proposes a robust and efficient computational framework, called ANCC flow, designed for establishing dense correspondences between images under severe photometric variations. We first simplify the weight of ANCC in an asymmetric manner by considering a source image weight only. It is then efficiently computed by applying constant-time edge-aware filters without loss of its matching accuracy. Additionally, to deal with a large discrete label space effectively, which is a challenging issue in a flow field estimation, we propose a randomized label space sampling strategy similar to PatchMatch filer (PMF) optimization. The robustness of the asymmetric ANCC and the cost filter is further enhanced through an evolving weight computation, where a flow field computed in a previous iteration is utilized to build current edge-aware weights. Experimental results demonstrate the outstanding performance of ANCC flow in many cases of dense correspondence estimations under severe photometric and geometric variations.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages3454-3458
Number of pages5
ISBN (Electronic)9781467399616
DOIs
Publication statusPublished - 2016 Aug 3
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: 2016 Sep 252016 Sep 28

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
CountryUnited States
CityPhoenix
Period16/9/2516/9/28

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All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Kim, S., Min, D., & Sohn, K. (2016). ANCC flow: Adaptive normalized cross-correlation with evolving guidance aggregation for dense correspondence estimation. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (pp. 3454-3458). [7533001] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7533001