Skip to main content

Research Repository

Advanced Search

Robust recognition of planar shapes under affine transforms using principal component analysis

Tzimiropoulos, Georgios; Mitianoudis, Nikolaos; Stathaki, Tania

Authors

Georgios Tzimiropoulos

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 Mar 28, 2024
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.

Files





Downloadable Citations