Feature extraction based on canonical correlation analysis for appearance parameter estimation

Michael Reiter; Thomas Melzer

doi:10.1117/12.420919

4 April 2001 Feature extraction based on canonical correlation analysis for appearance parameter estimation

Michael Reiter, Thomas Melzer

Proceedings Volume 4301, Machine Vision Applications in Industrial Inspection IX; (2001) https://doi.org/10.1117/12.420919
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

Abstract

We propose a new approach to building appearance models of 3D objects which is based on Canonical Correlation Analysis (CCA). In appearance based modeling, instead of building an explicit object model (e.g., 3D geometrical object model), a low dimensional object representation is obtained from a set of images. In standard appearance models typically Principal Component Analysis (PCA) is used for feature extraction. In our experiments we compare the performance of standard appearance models based on PCA and models based on CCA for 3D pose estimation. Results indicate that, while getting by with a smaller number linear features, CCA-based models perform consistently better.

Citation Download Citation

Michael Reiter and Thomas Melzer "Feature extraction based on canonical correlation analysis for appearance parameter estimation", Proc. SPIE 4301, Machine Vision Applications in Industrial Inspection IX, (4 April 2001); https://doi.org/10.1117/12.420919

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