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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

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

verfasst von : Sinda Elghoul, Faouzi Ghorbel

Erschienen in: New Approaches for Multidimensional Signal Processing

Verlag: 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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Vorheriges Kapitel Tracking of Domestic Animals in Thermal Videos by Tensor Decompositions
Nächstes Kapitel Vision-Based Line Tracking Control and Stability Analysis of Unicycle Mobile Robots
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Metadaten
Titel
Partial Contour Matching Based on Affine Curvature Scale Space Descriptors
verfasst von
Sinda Elghoul
Faouzi Ghorbel
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
New Approaches for Multidimensional Signal Processing

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

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

Copyright-Jahr: 2021

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

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