Probabilistic modeling and recognition of 3-D objects

Hornegger J, Niemann H (2000)


Publication Type: Journal article, Original article

Publication year: 2000

Journal

Original Authors: Hornegger J., Niemann H.

Publisher: Springer Verlag (Germany)

City/Town: Dordrecht, Netherlands

Book Volume: 39

Pages Range: 229-251

Journal Issue: 3

DOI: 10.1023/A:1026515828914

Abstract

This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical tool for stochastic modeling. The algorithmic part introduces methods for automatic model generation, localization, and recognition of objects. 2-D images are used for learning the statistical appearance of 3-D objects; both the depth information and the matching between image and model features are missing for model generation. The implied incomplete data estimation problem is solved by the Expectation Maximization algorithm. This leads to a novel class of algorithms for automatic model generation from projections. The estimation of pose parameters corresponds to a non-linear maximum likelihood estimation problem which is solved by a global optimization procedure. Classification is done by the Bayesian decision rule. This work includes the experimental evaluation of the various facets of the presented approach. An empirical evaluation of learning algorithms and the comparison of different pose estimation algorithms show the feasibility of the proposed probabilistic framework.

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How to cite

APA:

Hornegger, J., & Niemann, H. (2000). Probabilistic modeling and recognition of 3-D objects. International Journal of Computer Vision, 39(3), 229-251. https://doi.org/10.1023/A:1026515828914

MLA:

Hornegger, Joachim, and Heinrich Niemann. "Probabilistic modeling and recognition of 3-D objects." International Journal of Computer Vision 39.3 (2000): 229-251.

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