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.
The authors wish to thank the German Research Foundation (DFG), who partially funded the work reported here under grant SFB 182.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Hornegger, J., Paulus, D., Niemann, H. (1998). Statistical Classifiers in Computer Vision. In: Balderjahn, I., Mathar, R., Schader, M. (eds) Classification, Data Analysis, and Data Highways. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72087-1_33
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DOI: https://doi.org/10.1007/978-3-642-72087-1_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63909-1
Online ISBN: 978-3-642-72087-1
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