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
Recognition invariance obtained by extended and invariant features
Neural Networks - 2004 Special issue Vision and brain
Learning Appearance Manifolds from Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning viewpoint invariant object representations using a temporal coherence principle
Biological Cybernetics
Learning invariant object recognition in the visual system with continuous transformations
Biological Cybernetics
Accurate interpolation in appearance-based pose estimation
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Learning invariant visual shape representations from physics
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Handwritten digit recognition with nonlinear fisher discriminant analysis
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Rapid online learning of objects in a biologically motivated recognition architecture
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Real web community based automatic image annotation
Computers and Electrical Engineering
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Primates are very good at recognizing objects independent of viewing angle or retinal position, and they 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. We demonstrate the model behavior on complex three-dimensional objects under translation and rotation in depth on a homogeneous background. A similar model has previously been shown to extract hippocampal spatial codes from quasi-natural videos. The framework for mathematical analysis of this earlier application carries over to the scenario of invariant object recognition. Thus, the simulation results can be explained analytically even for the complex high-dimensional data we employed.