CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier Independent Viewpoint Selection for 3-D Object Recognition
Mustererkennung 2000, 22. DAGM-Symposium
Viewpoint Selection-A Classifier Independent Learning Approach
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Learning Temporal Context in Active Object Recognition Using Bayesian Analysis
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Transinformation for Active Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Handling camera movement constraints in reinforcement learning based active object recognition
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Aspects of optimal viewpoint selection and viewpoint fusion
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Cost integration in multi-step viewpoint selection for object recognition
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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In the last few years the research in 3-D object recognition has focused more and more on active approaches. In contrast to the passive approaches of the past decades where a decision is based on one image, active techniques use more than one image from different viewpoints for the classification and localization of an object. In this context several tasks have to be solved. First, how to choose the different viewpoint and how to fusion the multiple views. In this paper we present an approach for the fusion of multiple views within a continuous pose space. We formally define the fusion as a recursive density propagation problem and we show how to use the Condensation algorithm for solving it. The experimental results show that this approach is well suited for the fusion of multiple views in active object recognition.