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.
|Title of host publication||2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2016 Aug 3|
|Event||23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States|
Duration: 2016 Sep 25 → 2016 Sep 28
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Other||23rd IEEE International Conference on Image Processing, ICIP 2016|
|Period||16/9/25 → 16/9/28|
Bibliographical notePublisher Copyright:
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing