A stochastic model for natural feature representation

Kumar, S., Ramos, F., Upcroft, B., Ridley, M., Ong, L., Sakkarieh, S. and Durrant-Whyte, H. (2005). A stochastic model for natural feature representation. In: 2005 7th International Conference on Information Fusion (FUSION). 7th International Conference on Information Fusion, Philadelphia, U.S., (1030-1037). 25-28 July, 2005. doi:10.1109/ICIF.2005.1591971


Author Kumar, S.
Ramos, F.
Upcroft, B.
Ridley, M.
Ong, L.
Sakkarieh, S.
Durrant-Whyte, H.
Title of paper A stochastic model for natural feature representation
Conference name 7th International Conference on Information Fusion
Conference location Philadelphia, U.S.
Conference dates 25-28 July, 2005
Proceedings title 2005 7th International Conference on Information Fusion (FUSION)
Journal name 2005 7th International Conference on Information Fusion, FUSION
Place of Publication New York, U.S.
Publisher IEEE
Publication Year 2005
Sub-type Fully published paper
DOI 10.1109/ICIF.2005.1591971
ISBN 0-7803-9286-8
Volume 2
Start page 1030
End page 1037
Total pages 8
Language eng
Abstract/Summary This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.
Subjects 0801 Artificial Intelligence and Image Processing
Keyword Data fusion
Natural feature representation
Isomap
Expectation Maximization
Q-Index Code E1

 
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Created: Mon, 18 Jan 2010, 16:08:02 EST by Tara Johnson on behalf of Faculty Of Engineering, Architecture & Info Tech