@inproceedings {8310,
	title = {Learning Invariant Visual Shape Representations from Physics},
	author = {Mathias Franzius AND Heiko Wersing},
	year = {2010},
	month = {September},
	abstract = {3D shape determines an object{\textquoteright}s physical properties to a large degree. In this article, we introduce an autonomous learning system for categorizing 3D shape of simulated objects from single views. The system extends an unsupervised bottom-up learning architecture based on the slowness principle with top-down information derived from the physical behavior of objects. The unsupervised bottom-up learning leads to pose invariant representations. Shape specificity is then integrated as top-down information from the movement trajectories of the objects. As a result, the system can categorize 3D object shape from a single static object view without supervised postprocessing.},
	publisher = {Springer},
	url = {http://dx.doi.org/10.1007/978-3-642-15825-4\_38},
	booktitle = {ICANN (3)},
	pages = {298-302},
	series = {Lecture Notes in Computer Science}
}