Video matching
- 1 August 2004
- proceedings article
- Published by Association for Computing Machinery (ACM)
- Vol. 23 (3) , 592-599
- https://doi.org/10.1145/1186562.1015765
Abstract
This paper describes a method for bringing two videos (recorded at different times) into spatiotemporal alignment, then comparing and combining corresponding pixels for applications such as background subtraction, compositing, and increasing dynamic range. We align a pair of videos by searching for frames that best match according to a robust image registration process. This process uses locally weighted regression to interpolate and extrapolate high-likelihood image correspondences, allowing new correspondences to be discovered and refined. Image regions that cannot be matched are detected and ignored, providing robustness to changes in scene content and lighting, which allows a variety of new applications.Keywords
This publication has 18 references indexed in Scilit:
- Interactive digital photomontageACM Transactions on Graphics, 2004
- High dynamic range videoACM Transactions on Graphics, 2003
- A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence AlgorithmsInternational Journal of Computer Vision, 2002
- Video texturesPublished by Association for Computing Machinery (ACM) ,2000
- A pixel dissimilarity measure that is insensitive to image samplingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998
- Effective Corner MatchingPublished by British Machine Vision Association and Society for Pattern Recognition ,1998
- The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow FieldsComputer Vision and Image Understanding, 1996
- The computation of optical flowACM Computing Surveys, 1995
- A Combined Corner and Edge DetectorPublished by British Machine Vision Association and Society for Pattern Recognition ,1988
- Random sample consensusCommunications of the ACM, 1981