Learning invariance from transformation sequences
Neural Computation
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Slow feature analysis: unsupervised learning of invariances
Neural Computation
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This paper aims to tackle two fundamental problems faced by multiple object recognition systems: invariance and transformation estimation. A neural normalization approach is adopted, which allows for the subsequent incorporation of invariant features. Two new approaches are introduced: dynamic inputs (DI) and attraction force analysis (AFA). The DI concept refers to a cloud of inputs that is allowed to change its configuration in order to latch onto objects thus creating object-based reference frames. AFA is used in order to provide clouds with transformation estimations thus maximizing the efficiency with which they can latch onto objects. AFA analyzes the length and angular properties of the correspondences that are found between stored-patterns and the information conveyed by clouds. The solution provides significant invariance and useful estimations pertaining to translation, scale, rotation and combinations of these. The estimations provided are also considerably resistant to other factors such as deformation, noise, occlusion and clutter.