Fast object and pose recognition through minimum entropy coding

Author

Westphal, Günter ; Würtz, Rolf P.

Author_Institution

Inst. fur Neuroinformatik, Ruhr-Univ., Bochum, Germany

Volume

3

fYear

2004

fDate

23-26 Aug. 2004

Firstpage

53

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.

Keywords

feature extraction; image classification; image coding; minimum entropy methods; object recognition; statistical analysis; unsupervised learning; COIL-100 database; feature extraction; image classification; image segmentation; linear discriminant method; minimum entropy coding; object recognition; pattern recognition; pose recognition; unsupervised learning; Entropy coding; Feature extraction; Image recognition; Layout; Multi-layer neural network; Neural networks; Object recognition; Pattern recognition; Spatial databases; Visual databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-2128-2

Type

conf

DOI

10.1109/ICPR.2004.1334467

Filename

1334467