Name
Abstract | ||
|---|---|---|
Aligning video sequences is a fundamental yet still unsolved component for a broad range of applications in computer graphics and vision. Most classical image processing methods cannot be directly applied to related video problems due to the high amount of underlying data and their limit to small changes in appearance. We present a scalable and robust method for computing a non-linear temporal video alignment. The approach autonomously manages its training data for learning a meaningful representation in an iterative procedure each time increasing its own knowledge. It leverages on the nature of the videos themselves to remove the need for manually created labels. While previous alignment methods similarly consider weather conditions, season and illumination, our approach is able to align videos from data recorded months apart. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICMLA.2017.0-173 | 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) |
Keywords | Field | DocType |
deep learning,synchronization,video understanding,large scale,self-supervised | Training set,Computer vision,Synchronization,Computer science,Data synchronization,Image processing,Robustness (computer science),Artificial intelligence,Artificial neural network,Computer graphics,Machine learning,Scalability | Conference |
ISBN | Citations | PageRank |
978-1-5386-1419-8 | 1 | 0.39 |
References | Authors | |
27 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick Wieschollek | 1 | 26 | 3.21 |
| Ido Freeman | 2 | 1 | 0.39 |
| Hendrik P. A. Lensch | 3 | 1471 | 96.59 |