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

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...

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Main Authors: Tzimiropoulos, Georgios, Mitianoudis, Nikolaos, Stathaki, Tania
Format: Article
Published: IEEE 2007
Subjects:
Online Access:https://eprints.nottingham.ac.uk/30276/
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author Tzimiropoulos, Georgios
Mitianoudis, Nikolaos
Stathaki, Tania
author_facet Tzimiropoulos, Georgios
Mitianoudis, Nikolaos
Stathaki, Tania
author_sort Tzimiropoulos, Georgios
building Nottingham Research Data Repository
collection Online Access
description 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.
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spelling nottingham-302762020-05-04T20:29:16Z https://eprints.nottingham.ac.uk/30276/ Robust recognition of planar shapes under affine transforms using principal component analysis Tzimiropoulos, Georgios Mitianoudis, Nikolaos Stathaki, Tania 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. IEEE 2007 Article PeerReviewed Tzimiropoulos, Georgios, Mitianoudis, Nikolaos and Stathaki, Tania (2007) Robust recognition of planar shapes under affine transforms using principal component analysis. IEEE Signal Processing Letters, 14 (10). pp. 723-726. ISSN 1070-9908 Principal Component Analysis affine transformation invariants shape recognition http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4303087&filter%3DAND%28p_IS_Number%3A4303057%29 doi:10.1109/LSP.2007.896434 doi:10.1109/LSP.2007.896434
spellingShingle Principal Component Analysis
affine transformation
invariants
shape recognition
Tzimiropoulos, Georgios
Mitianoudis, Nikolaos
Stathaki, Tania
Robust recognition of planar shapes under affine transforms using principal component analysis
title Robust recognition of planar shapes under affine transforms using principal component analysis
title_full Robust recognition of planar shapes under affine transforms using principal component analysis
title_fullStr Robust recognition of planar shapes under affine transforms using principal component analysis
title_full_unstemmed Robust recognition of planar shapes under affine transforms using principal component analysis
title_short Robust recognition of planar shapes under affine transforms using principal component analysis
title_sort robust recognition of planar shapes under affine transforms using principal component analysis
topic Principal Component Analysis
affine transformation
invariants
shape recognition
url https://eprints.nottingham.ac.uk/30276/
https://eprints.nottingham.ac.uk/30276/
https://eprints.nottingham.ac.uk/30276/