Abstract
A component-based approach to visual object recognition rooted in supervised learning allows for a vision system that is more robust against changes in an object's pose or illumination. Learning figures prominently in the study of visual systems from the viewpoints of visual neuroscience and computer vision. Whereas visual neuroscience concentrates on mechanisms that let the cortex adapt its circuitry and learn a new task, computer vision aims at devising effectively trainable systems. Vision systems that learn and adapt are one of the most important trends in computer vision research. They might offer the only solution to developing robust, reusable vision systems.
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