Learning invariance from transformation sequences
Neural Computation
Slow feature analysis: unsupervised learning of invariances
Neural Computation
Extracting Slow Subspaces from Natural Videos Leads to Complex Cells
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Slow feature analysis: a theoretical analysis of optimal free responses
Neural Computation
Gender and age estimation from synthetic face images
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Learning invariant visual shape representations from physics
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
The slowness principle: SFA can detect different slow components in non-stationary time series
International Journal of Innovative Computing and Applications
On the relation of slow feature analysis and laplacian eigenmaps
Neural Computation
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Primates are very good at recognizing objects independently of viewing angle or retinal position and outperform existing computer vision systems by far. But invariant object recognition is only one prerequisite for successful interaction with the environment. An animal also needs to assess an object's position and relative rotational angle. We propose here a model that is able to extract object identity, position, and rotation angles, where each code is independent of all others. We demonstrate the model behavior on complex three-dimensional objects under translation and in-depth rotation on homogeneous backgrounds. A similar model has previously been shown to extract hippocampal spatial codes from quasi-natural videos. The rigorous mathematical analysis of this earlier application carries over to the scenario of invariant object recognition.