Georgios Tzimiropoulos
Robust recognition of planar shapes under affine transforms using principal component analysis
Tzimiropoulos, Georgios; Mitianoudis, Nikolaos; Stathaki, Tania
Authors
Nikolaos Mitianoudis
Tania Stathaki
Abstract
A scheme, based on Principal Component Analysis (PCA), is proposed that can be used for the recognition of 2D planar shapes under affine transformations. A PCA step is first used to map the object boundary to its canonical form, reducing the problem of the non-uniform sampling of the object contour introduced by the affine transformation. Then, a PCAbased scheme is employed to train a set of basis functions on the signals extracted from the objects’ boundaries. The derived bases are used to analyze the boundary locally. Based on the theory of invariants and local boundary analysis, an novel invariant function is constructed. The performance of the proposed framework is compared with a standard wavelet-based approach with promising results.
Citation
Tzimiropoulos, G., Mitianoudis, N., & Stathaki, T. (2007). Robust recognition of planar shapes under affine transforms using principal component analysis. IEEE Signal Processing Letters, 14(10), https://doi.org/10.1109/LSP.2007.896434
Journal Article Type | Article |
---|---|
Publication Date | Jan 1, 2007 |
Deposit Date | Sep 25, 2015 |
Publicly Available Date | Sep 25, 2015 |
Journal | IEEE Signal Processing Letters |
Print ISSN | 1070-9908 |
Electronic ISSN | 1070-9908 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 14 |
Issue | 10 |
DOI | https://doi.org/10.1109/LSP.2007.896434 |
Keywords | Principal Component Analysis, affine transformation, invariants, shape recognition |
Public URL | https://nottingham-repository.worktribe.com/output/1018028 |
Publisher URL | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4303087&filter%3DAND%28p_IS_Number%3A4303057%29 |
Additional Information | © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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