Robust model-based scene interpretation by multilayered context information
Computer Vision and Image Understanding
Toward humanoid manipulation in human-centred environments
Robotics and Autonomous Systems
Towards using multiple cues for robust object recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
An Integrated Method for Multiple Object Detection and Localization
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Recognition of objects of a living room of class through a pyramidal method
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Image and Vision Computing
Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Toward a unified probabilistic framework for object recognition and segmentation
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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We present a framework for learning object representations for fast recognition of a large number of different objects. Rather than learning and storing feature representationsseparately for each object, we create a finite set of representative features and share these features within and between different object models. In contrast to traditional recognition methods that scale linearly with the number of objects, the shared features can be exploited by bottom-up search algorithms which require a constant number of feature comparisons for any number of objects. We demonstrate the feasibility of this approach on a novel database of 50 everyday objects in cluttered real-world scenes. Using Gabor wavelet-response features extracted only at corner points, our system achieves good recognition results despite substantial occlusion and background clutter.