IEEE Transactions on Pattern Analysis and Machine Intelligence
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
International Journal of Computer Vision
Learning Active Basis Model for Object Detection and Recognition
International Journal of Computer Vision
Extraction of buildings footprint from LiDAR altimetry data with the hermite transform
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
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Recognition of occluded objects in computer vision is a very hard problem. In this work we propose an algorithm to construct a structure of a model using learned active basis models, then use it to do inference over the most probable detected parts of an object, to allow partial recognition using the standard sum-max-maps algorithm used for active basis. We tested our method and present some improvements on occluded face detection using our algorithm, we also present some experiments with other partially occluded objects.