Abstract
Quickly learning and recognising familiar objects seems almost automatic for humans, yet it remains a challenge for machines. This paper describes an integrated object recognition system including several novel algorithmic contributions using a SIFT feature appearance-based approach to rapidly learn incremental 3D representations of objects as aspect-graphs. A fast recognition scheme applying geometric and temporal constraints localizes and identifies the pose of 3D objects in a video sequence. The system is robust to significant variation in scale, orientation, illumination, partial deformation, occlusion, focal blur and clutter and recognises objects at near real-time video rates.
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Hamid, S., Hengst, B. (2009). Learning and Recognition of 3D Visual Objects in Real-Time. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_16
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DOI: https://doi.org/10.1007/978-3-642-10439-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10438-1
Online ISBN: 978-3-642-10439-8
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