Multisensor occlusion reasoning
Mark R. Stevens, J. Ross Beveridge · 2002
Most model-based object recognition algorithms attempt to match stored model features to features extracted from imagery. The better the match, the more likely it is that the object is present in the scene. Problems arise when objects are occluded because matches will be incomplete. It is rare for an object recognition algorithm to employ knowledge to explain the absence of occluded features. The work presented here illustrates an approach to object recognition which propagates evidence of occlusion from range to optical sensors and thereby explains missing features.