Localization and classification based on projections

Journal article
(Original article)


Publication Details

Author(s): Hornegger J, Welker V, Niemann H
Journal: Pattern Recognition
Publisher: Elsevier
Publication year: 2002
Volume: 35
Journal issue: 6
Pages range: 1225-1235
ISSN: 0031-3203


Abstract


Due to the loss of range information, projections as input data for a 3-D object recognition algorithm are expected to increase the computational complexity. In this work, however, we demonstrate that this deficiency carries potential for complexity reduction of major vision problems. We show that projections provide a reduction of feature dimensions, and lead to structures exhibiting simple combinatorial properties. The theoretical framework is embedded in a probabilistic setting which deals with uncertainties and variations of observed features. In statistics marginal densities and the assumption of independency prove to be the key tools when one encounters projections. The examples discussed in this paper include feature matching, pose estimation as well as classification of 3-D objects. The final experimental evaluation demonstrates the practical importance of the marginalization concept and independency assumptions. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.



FAU Authors / FAU Editors

Hornegger, Joachim Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Niemann, Heinrich Prof. Dr.
Technische Fakultät


External institutions with authors

Philipps-Universität Marburg


How to cite

APA:
Hornegger, J., Welker, V., & Niemann, H. (2002). Localization and classification based on projections. Pattern Recognition, 35(6), 1225-1235. https://dx.doi.org/10.1016/S0031-3203(01)00122-4

MLA:
Hornegger, Joachim, Volkmar Welker, and Heinrich Niemann. "Localization and classification based on projections." Pattern Recognition 35.6 (2002): 1225-1235.

BibTeX: 

Last updated on 2019-20-04 at 21:50