Rationalizing Efficient Compositional Image Alignment

Author:

Muñoz Enrique,Márquez-Neila Pablo,Baumela Luis

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Reference35 articles.

1. Amberg, B., & Vetter, T. (2009). On compositional imge alignment, with an application to active appearance models. In Proceedings of Computer Vision and Pattern Recognition Conference.

2. Baker, S., & Matthews, I. (2001). Equivalence and efficiency of image alignment algorithms. In Proceedings of Computer Vision and Pattern Recognition Conference (vol. 1, pp. 1090–1097). IEEE.

3. Baker, S., & Matthews, I. (2004). Lucas-kanade 20 years on: A unifiying framework. International Journal of Computer Vision, 56(3), 221–255.

4. Baker, S., Patil, R., Cheung, G., & Matthews, I. (2004). Lucas-kanade 20 years on: Part 5. Technical Report CMU-RI-TR-04-64, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.

5. Bartoli, A. (2008). Groupwise geometric and photometric direct image registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 2098–2108.

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2. Robust Registration of Dynamic Facial Sequences;IEEE Transactions on Image Processing;2017-04

3. Image alignment by parameter hypersurface learning;Electronics Letters;2016-09

4. A Unified Framework for Compositional Fitting of Active Appearance Models;International Journal of Computer Vision;2016-06-09

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