Novelty detection using growing neural gas for visuo-spatial memory

Detecting visual changes in environments is an important computation with many applications in robotics and computer vision. Security cameras, remotely operated vehicles, and sentry robots could all benefit from robust change detection capability. We conjecture that if one has a mobile camera system the number of visual scenes that are experienced is limited (compared to the space of all possible scenes) and that the scenes do not frequently undergo major changes between observations. These assumptions can be exploited to ease the task of change detection and reduce the computational complexity of processing visual information by utilizing memory to store previous computations.

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