Fast Object and Pose Recognition Through Minimum Entropy Coding

Authors:
Gunter Westphal;Rolf P. Wurtz
Affiliations:
Ruhr-Universität Bochum, Germany;Ruhr-Universität Bochum, Germany
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Year:
2004

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Abstract

We present a pattern recognizer to classify a variety of objects and their pose on a table from real world images. Learning of weights in a linear discriminant is based on estimating the relative information contributed by a set of features to the final decision. Evaluation of the discriminant is very fast, allowing for about three decisions per second on datasets without segmentation difficulties like the COIL-100 database. Experiments on that database yield high recognition rates and good generalisation over pose.