Viewpoint Selection-A Classifier Independent Learning Approach

Authors:
F. Deinzer;J. Denzler;H. Niemann
Affiliations:
-;-;-
Venue:
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Year:
2000

Citing 0
Cited 2

Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Fusion of Multiple Views for Active Object Recognition

Proceedings of the 23rd DAGM-Symposium on Pattern Recognition

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Abstract

This paper deals with an aspect of active object recognition for improving the classification and localization results by choosing optimal next views at an object. The knowledge of 驴good驴 next views at an object is learned automatically and unsupervised from the results of the used classifier. For that purpose methods of reinforcement learning are used in combination with numerical optimization. The major advantages of the presented approach are its classifier independence and that the approach does not require a priori assumptions about the objects. The presented results for synthetically generated images show that our approach is well suited for choosing optimal views at objects.