Statistical classifiers in computer vision

Hornegger J, Paulus D, Niemann H (1997)


Publication Type: Conference contribution, Conference Contribution

Publication year: 1997

Original Authors: Hornegger Joachim, Paulus Dietrich, Niemann Heinrich

Publisher: Springer

City/Town: Heidelberg

Pages Range: -

Event location: Potsdam DE

URI: http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/1997/Hornegger97-SCI.pdf

Abstract

This paper introduces a unified Bayesian approach to 3–D computer vision using segmented image features. The theoretical part summarizes the basic requirements of statistical object recognition systems. Non–standard types of models are introduced using parametric probability density functions, which allow the implementation of Bayesian classifiers for object recognition purposes. The importance of model densities is demonstrated by concrete examples. Normally distributed features are used for automatic learning, localization, and classification. The contribution concludes with the experimental evaluation of the presented theoretical approach. 

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How to cite

APA:

Hornegger, J., Paulus, D., & Niemann, H. (1997). Statistical classifiers in computer vision. In Proceedings of the Data Highways and Information Flooding, a Challenge for Classification and Data Analysis (pp. -). Potsdam, DE: Heidelberg: Springer.

MLA:

Hornegger, Joachim, Dietrich Paulus, and Heinrich Niemann. "Statistical classifiers in computer vision." Proceedings of the Data Highways and Information Flooding, a Challenge for Classification and Data Analysis, Potsdam Heidelberg: Springer, 1997. -.

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