A stochastic model for natural feature representation
Kumar, S., Ramos, F., Upcroft, B., Ridley, M., Ong, L., Sakkarieh, S., & Durrant-Whyte, H. (2005) A stochastic model for natural feature representation. In 8th International Conference on Information Fusion, 2005, IEEE, Wyndham Philadelphia at Franklin Plaza Philadelphia, PA, USA.
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.
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|Item Type:||Conference Paper|
|Keywords:||Data fusion, Natural feature representation, Isomap, Expectation Maximization algorithm, Gaussian mixture model, data fusion algorithm, decentralized sensor network, nonGaussian likelihood model, nonlinear filtering algorithm, nonlinear manifold learning algorithm, stochastic model, unmanned aerial vehicle|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright IEEE 2005|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||10 May 2011 09:16|
|Last Modified:||13 Aug 2011 11:22|
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