Learning Visual Invariance

Invariance is a necessary feature of a visual system able to recognize real objects in all their possible appearance. It is also the processing step most problematic to understand in biological systems, and most difficult to simulate in computational models. This work investigates the possibility to achieve viewpoint invariance without adopting any explicit theorical solution to the problem, but simply by exposing a hierarchical architecture of self-organizing artificial cortical maps to series of images under various viewpoints.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Alessio Plebe
• Keywords: Artificial Cortical Maps, ESANN 2006, ESANN 2007, Explicit Theorical Solution, Viewpoint Invariance