@inbook {12503,
	title = {Aligning Spatio-Temporal Signals on a Special Manifold},
	booktitle = {Computer Vision {\textendash} ECCV 2010Computer Vision {\textendash} ECCV 2010},
	series = {Lecture Notes in Computer Science},
	volume = {6315},
	year = {2010},
	month = {2010///},
	pages = {547 - 560},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	abstract = {We investigate the spatio-temporal alignment of videos or features/signals extracted from them. Specifically, we formally define an alignment manifold and formulate the alignment problem as an optimization procedure on this non-linear space by exploiting its intrinsic geometry. We focus our attention on semantically meaningful videos or signals, e.g., those describing or capturing human motion or activities, and propose a new formalism for temporal alignment accounting for executing rate variations among realizations of the same video event. By construction, we address this static and deterministic alignment task in a dynamic and stochastic manner: we regard the search for optimal alignment parameters as a recursive state estimation problem for a particular dynamic system evolving on the alignment manifold. Consequently, a Sequential Importance Sampling iteration on the alignment manifold is designed for effective and efficient alignment. We demonstrate the performance on several types of input data that arise in vision problems.},
	isbn = {978-3-642-15554-3},
	url = {http://dx.doi.org/10.1007/978-3-642-15555-0_40},
	author = {Ruonan Li and Chellapa, Rama},
	editor = {Daniilidis,Kostas and Maragos,Petros and Paragios,Nikos}
}
