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