Affine invariant descriptors using Fourier series

Oirrak, Ahmed El; Daoudi, Mohamed; Aboutajdine, Driss

doi:10.1016/s0167-8655(02)00027-2

Cited by 24 publications

(15 citation statements)

References 6 publications

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“…On these synthetic tests we got perfect precisionrecall curves even under very challenging conditions such as severe foreshortening. El Oirrak et al also propose an affine invariant normalization of FDs [12], [13] but we do not see any significant difference between their work and the work of Arbter et al…”

Section: A Fd Matching Methodscontrasting

confidence: 68%

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Patch Contour Matching by Correlating Fourier Descriptors

Larsson

Felsberg

Forssén

2009

2009 Digital Image Computing: Techniques and Applications

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Abstract-Fourier descriptors (FDs) is a classical but still popular method for contour matching. The key idea is to apply the Fourier transform to a periodic representation of the contour, which results in a shape descriptor in the frequency domain. Fourier descriptors have mostly been used to compare object silhouettes and object contours; we instead use this well established machinery to describe local regions to be used in an object recognition framework. We extract local regions using the Maximally Stable Extremal Regions (MSER) detector and represent the external contour by FDs. Many approaches to matching FDs are based on the magnitude of each FD component, thus ignoring the information contained in the phase. Keeping the phase information requires us to take into account the global rotation of the contour and shifting of the contour samples. We show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours. We compare our correlation based matching against affine-invariant Fourier descriptors (AFDs) and demonstrate that our correlation based approach outperforms AFDs on real world data.

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Section: A Fd Matching Methodscontrasting

confidence: 68%

“…Since the auto-correlation function r 11 is real-valued and has its maximum at 0, the estimates for ∆l and φ are obtained according to (11) and (12). For the least-squares error, we obtain due to the normalized FDs…”

Section: B Correlation-based Fd Matchingmentioning

confidence: 99%

Patch Contour Matching by Correlating Fourier Descriptors

Larsson

Felsberg

Forssén

2009

2009 Digital Image Computing: Techniques and Applications

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“…On these synthetic tests we got perfect precision-recall curves even under very challenging conditions such as severe foreshortening. El Oirrak et al also propose a similar affine invariant normalization of FDs [16].…”

Section: Fd Matching Methodsmentioning

confidence: 99%

Correlating Fourier descriptors of local patches for road sign recognition

2011

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The Fourier descriptors (FDs) is a classical but still popular method for contour matching. The key idea is to apply the Fourier transform to a periodic representation of the contour, which results in a shape descriptor in the frequency domain.Fourier descriptors are most commonly used to compare object silhouettes and object contours; we instead use this well established machinery to describe local regions to be used in an object recognition framework. Many approaches to matching FDs are based on the magnitude of each FD component, thus ignoring the information contained in the phase. Keeping the phase information requires us to take into account the global rotation of the contour and shifting of the contour samples.We show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours. We compare our correlation based matching against affine-invariant Fourier descriptors (AFDs) and WARP matched FDs and demonstrate that our correlation based approach outperforms AFDs and WARP on real data. As a practical application we demonstrate the proposed correlation based matching on a road sign recognition task.

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“…Ces dernières années ont vu le développement de plusieurs approches pour décrire les formes des courbes 2D (Michor, Mumford, 2006 ;Klassen et al, 2004) et des courbes 3D (Samir et al, 2006). Nous pouvons citer les approches basées sur les descripteurs de Fourier (Székely et al, 1995 ;El Oirrak et al, 2002), celles basées sur les moments (Tanaka et al, 1998) ou celles basées sur les courbures multi-échelles (Mahmoudi, Daoudi, 2007). Cependant, il semble qu'aucune de ces méthodes ne s'est imposée ni du point de vue conceptuel ni du point de vue calculatoire, pour décrire la forme des courbes.…”

Section: Analyse Riemannienne Des Surfaces Facialesunclassified

Sélection de caractéristiques géométriques pour la reconnaissance faciale 3D

Ballihi¹,

Amor²,

Daoudi³

et al. 2012

Traitement du signal

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RÉSUMÉ. Dans ce travail nous proposons de coupler la géométrie Riemannienne avec les techniques d'apprentissage pour une biométrie faciale 3D efficace et robuste aux changements d'expressions. Nous représentons localement la forme des surfaces faciales par des collections de courbes 3D. Nous appliquons des techniques d'apprentissage afin de déterminer les courbes les plus pertinentes à la reconnaissance d'identité. Les résultats obtenus sur FRGC v2 confirment Extended abstractBiometric recognition aims to use behavioral and/or physiological characteristics of people to recognize them or to verify their identities. While some biometric modalities, such as fingerprints and iris, have already reached very high level of accuracy, they have a limited use in non-cooperative scenarios. On the other hand, the less-intrusive modalities like the face and gait have not reached the desired levels of accuracy. Since face recognition is contact-less and less intrusive, it has emerged as a more attractive and natural biometric for security applications. Unfortunately, the 2D-based face recognition technologies still face difficult challenges such as changes in illumination conditions, pose variations, occlusions, and facial expressions. In the last few years, face recognition using the shape of face surface has become a major area of research due to its theoretical robustness to challenges such as illumination and pose. Several approaches have been proposed and applied to deal with deformations caused by changes in facial expressions. In this paper, we focus on approaches that either use curve-based representations for faces or use a feature-selection technique to optimize recognition rates: (a) Curve-based approaches and (b) Feature selection-based approaches.The state-of-the-art techniques seek to analyze variability caused by facial deformations and propose methods that are robust to such shape variations. Achieving good performances in automatic 3D face recognition is an important issue when developing intelligent systems. In this paper we propose a fully automatic and unified framework for face recognition by representing a 3D facial surface by a collection of two types of curves: radial curves and iso-level curves. Furthermore, to improve performance of our identity recognition approach, we propose a geometric feature-selection approach that selects the most relevant curves by using the well-known Adaboost algorithm.The proposed framework combines machine learning techniques (Boosting) and Riemannian geometry-based shape analysis to select relevant facial curves extracted from 3D facial surfaces. The resulting set of curves provides a compact signature of 3D face, which significantly reduces the computational cost and the storage requirements for face recognition. After a 3D scan acquisition and preprocessing procedure (see Section 3) in order to extract the face area and correct some imperfections such as hole filling and spikes removing, we extract both radial and iso-level facial curves from the 3D surface. Then, acco...

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Affine invariant descriptors using Fourier series

Cited by 24 publications

References 6 publications

Patch Contour Matching by Correlating Fourier Descriptors

Patch Contour Matching by Correlating Fourier Descriptors

Correlating Fourier descriptors of local patches for road sign recognition

Sélection de caractéristiques géométriques pour la reconnaissance faciale 3D

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