Hornegger J, Niemann H (2001)
Publication Type: Journal article, Original article
Publication year: 2001
Original Authors: Hornegger J., Niemann H.
Publisher: World Scientific Publishing
Book Volume: 15
Pages Range: 241-253
Journal Issue: 2
DOI: 10.1142/S0218001401000903
In this paper we consider the problem of object recognition and localization in a probabilistic framework. An object is represented by a parametric probability density, and the computation of pose parameters is implemented as a nonlinear parameter estimation problem. The presence of a probabilistic model allows for recognition according to Bayes rule. The introduced probabilistic model requires no prior segmentation but characterizes the statistical properties of observed intensity values in the image plane. A detailed discussion of the applied theoretical framework is followed by a concise experimental evaluation which demonstrates the benefit of the proposed approach.
APA:
Hornegger, J., & Niemann, H. (2001). A novel probabilistic model for object recognition and pose estimation. International Journal of Pattern Recognition and Artificial Intelligence, 15(2), 241-253. https://doi.org/10.1142/S0218001401000903
MLA:
Hornegger, Joachim, and Heinrich Niemann. "A novel probabilistic model for object recognition and pose estimation." International Journal of Pattern Recognition and Artificial Intelligence 15.2 (2001): 241-253.
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