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Computational Intelligence for Brain Tumors Detection Read chapter Read first chapter

2021 | OriginalPaper | Chapter

5. Partial Contour Matching Based on Affine Curvature Scale Space Descriptors

Authors : Sinda Elghoul, Faouzi Ghorbel

Published in: New Approaches for Multidimensional Signal Processing

Publisher: Springer Singapore

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Abstract

In real applications, the same object may have been presented by different shapes due to the moment and the angles of image acquisition, which does not guarantee a complete contour extraction without being disturbed by the noise or the distortions. In this paper, we propose a new method to match partially occluded shape based on affine curvature scale space. Firstly, an affine curve re-parameterization is defined, inspired by the properties of affine curvature scale space (ACSS) shape descriptor. Then, the different parts will be matched in order to minimize the \( L_{2} \) distance by the calculation of the pseudo-inverse matrix to estimate the translation and the linear transformation based on the affine curve matching (ACM) algorithm. Finally, a matching curve algorithm is obtained according to any planar affine transformation and in any partial occluded case. Experiments are conducted on multi-view curve dataset.

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Metadata
Title
Partial Contour Matching Based on Affine Curvature Scale Space Descriptors
Authors
Sinda Elghoul
Faouzi Ghorbel
Copyright Year
2021
Publisher
Springer Singapore
Book
New Approaches for Multidimensional Signal Processing

Print ISBN: 978-981-334-675-8

Electronic ISBN: 978-981-334-676-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-4676-5_5