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Published Articles >> Table of Contents >> Abstract
32nd Applied Imagery Pattern Recognition Workshop (AIPR'03)
p. 33
Eigenviews for Object Recognition in Multispectral Imaging Systems
Rajeev Ramanath, NC State University
Wesley E. Snyder, NC State University
Hairong Qi, The University of Tennessee
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AIPR.2003.1284245
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| Abstract |
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We address the problem of representing multispectral images
of objects using eigenviews for recognition purposes.
Eigenviews have long been used for object recognition and
pose estimation purposes in the grayscale and color image
settings. The purpose of this paper is two-fold: firstly to extend
the idealogies of eigenviews to multispectral images
and secondly to propose the use of dimensionality reduction
techniques other than those popularly used. Principal
Component Analysis (PCA) and its various kernel-based
flavors are popularly used to extract eigenviews. We propose
the use of Independent Component Analysis (ICA) and
Non-negative Matrix Factorization (NMF) as possible candidates
for eigenview extraction. Multispectral images of a
collection of 3D objects captured under different viewpoint
locations are used to obtain representative views (eigen-views)
that encode the information in these images. The
idea is illustrated with a collection of eight synthetic objects
imaged in both reflection and emission bands. A Nearest
Neighbor classifier is used to perform the classification
of an arbitrary view of an object. Classifier performance
under additive white Gaussian noise is also tested. The results
demonstrate that this system holds promise for use
in object recognition under the multispectral imaging setting
and also for novel dimensionality reduction techniques.
The number of eigenviews needed by various techniques to
obtain a given classifier accuracy is also calculated as a
measure of the performance of the dimensionality reduction
technique.
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 Additional Information
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Citation:
Rajeev Ramanath, Wesley E. Snyder, Hairong Qi,
"Eigenviews for Object Recognition in Multispectral Imaging Systems,"
aipr,
p. 33,
32nd Applied Imagery Pattern Recognition Workshop (AIPR'03),
2003
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