Eigendecomposition of Correlated Images Characterized by Three Parameters

Eigendecomposition of Correlated Images Characterized by Three Parameters

Most eigendecomposition algorithms operate on correlated images that are characterized by only one parameter. Hence they lack the required specifications of fully general 3D image data sets, in which the images need to be characterized by three parameters. In this paper, an extension of one of the f...

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Bibliographic Details
Published in:2006 IEEE Southwest Symposium on Image Analysis and Interpretation pp. 203 - 207
Main Authors: Saitwal, K., Maciejewski, A.A., Roberts, R.G.
Format: Conference Proceeding
Language:English
Published: IEEE 2006
Subjects:
Active appearance model
Application software
Collaborative work
Computational efficiency
Computer vision
Contracts
Government
Image processing
Pattern recognition
Singular value decomposition
Online Access:Get full text
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Summary:Most eigendecomposition algorithms operate on correlated images that are characterized by only one parameter. Hence they lack the required specifications of fully general 3D image data sets, in which the images need to be characterized by three parameters. In this paper, an extension of one of the fastest known eigendecomposition algorithms is successfully implemented to improve the computational efficiency of computing the eigendecomposition of such 3D image sets. This algorithm can be used in pattern recognition applications such as fully general 3D pose estimation of objects
ISBN:9781424400690
1424400694
DOI:10.1109/SSIAI.2006.1633751