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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Active object recognition integrating attention and viewpoint control
Computer Vision and Image Understanding
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Graphical Models and Image Processing
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International Journal of Computer Vision
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
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Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Sequential Accumulation of Evidence
International Journal of Computer Vision - Special issue: Research at McGill University
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
International Journal of Computer Vision
Efficient Discriminant Viewpoint Selection for Active Bayesian Recognition
International Journal of Computer Vision
Probabilistic Object Recognition Using Multidimensional Receptive Field Histograms
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Signal Processing
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IEEE Transactions on Signal Processing
Partial eigenvalue decomposition of large images using spatial temporal adaptive method
IEEE Transactions on Image Processing
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
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While prior relevant research in active object recognition/pose estimation has mostly focused on single-camera systems, we propose two multi-camera solutions to this problem that can enhance object recognition rate, particularly in the presence of occlusion. In the proposed methods, multiple cameras simultaneously acquire images from different view angles of an unknown, randomly occluded object belonging to a set of a priori known objects. By processing the available information within a recursive Bayesian framework at each step, the recognition algorithms attempt to classify the object, if its identity/pose can be determined with a high confidence level. Otherwise, the algorithms would compute the next most informative camera positions for capturing more images. The principle component analysis (PCA) is used to produce a measurement vector based on the acquired images. Occlusions in the images are handled by a novel probabilistic modelling approach that can increase the robustness of the recognition process with respect to structured noise. The camera positions at each recognition step are selected based on two statistical metrics quantifying the quality of the observations, namely the mutual information (MI) and the Cramer-Rao lower bound (CRLB). While the former has also been used in a prior relevant work, the latter is new in the context of object recognition. Extensive Monte Carlo experiments conducted with a two-camera system demonstrate the effectiveness of the proposed approaches.