Slow feature analysis: unsupervised learning of invariances
Neural Computation
Learning viewpoint invariant object representations using a temporal coherence principle
Biological Cybernetics
Invariant Object Recognition with Slow Feature Analysis
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Functional object class detection based on learned affordance cues
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Invariant object recognition and pose estimation with slow feature analysis
Neural Computation
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3D shape determines an object'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.