Seif Eddine NAFFOUTI - Academia.edu (original) (raw)

Papers by Seif Eddine NAFFOUTI

Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-ri... more Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-rigid three-dimensional shapes and Classical Multidimensional Scaling (Classical MDS) method in object classification which we quote, in particular, the example of Jian Sun et al. (2009) [1]. However, in this paper, the main focuses on classification that we propose a concise and provably factorial method by invoking Principal Component Analysis (PCA) as a classifier to improve the scheme of 3D shape classification. To avoid losing or disordering information after extracting features from the mesh, PCA is used instead of the Classical MDS to discriminate-as much as possible-feature points for each 3D shape in several poses. To demonstrate the practical relevance of this scheme, we present, illustrate and compare several assessments of the two proposed methods for non-rigid three-dimensional shapes classification based on heat diffusion. Across a collection of shapes, our results analysis show that the proposed contribution outperforms the classification method without PCA.

Signal Processing-image Communication, Oct 1, 2017

We propose a novel 3D shape descriptor, called the Advanced Global Point Signature (AGPS), which ... more We propose a novel 3D shape descriptor, called the Advanced Global Point Signature (AGPS), which is based on spectral analysis and is obtained by linear combination of some scaled eigenfunctions of the Laplace-Beltrami operator. Since it is built upon the concept of Global Point Signature, AGPS inherits several useful properties such as robustness to noise, stability and scale invariance. An AGPS-based method for extracting salient features from semi-rigid objects represented by triangular mesh surfaces is introduced. Due to its discriminative power, the associated AGPS values with each point remain extremely stable, which allows for simple and efficient shape characterization and robust salient point extraction. To assert our method regarding its robustness against noise and topological modifications, experiments on multiple benchmark datasets under unfavorable circumstances were performed. The method is also compared to state of the art methods for shape classification and retrieval.

Signal, Image and Video Processing, Jan 22, 2018

This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm opt... more This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. The variance parameter and its setting mode play a central role in this kernel. In order to circumvent a purely arbitrary choice of the internal parameters of the WKS algorithm, we present a four-step feature descriptor framework in an effort to further improve the classical wave kernel signature (WKS) by acting on its variance parameter. The advantage of the enhanced method comes from the tuning of the variance parameter using MPSO and the selection of the first vector from the constructed OWKS at its first energy scale, thus giving rise to substantially better matching and retrieval accuracy for deformable 3D shape. The special choice of this vector is to extremely reinforce the stability for efficient salient features extraction method from the 3D meshes. Experimental results demonstrate the effectiveness of our proposed shape classification and retrieval approach in comparison with state-of-the-art methods. For instance, in terms of the nearest neighbor (NN) metric, the OWKS achieves a 96.9% score, with performance improvements of 83.5 and 90.4% over the baseline methods WKS and heat kernel signature, respectively. Keywords Shapes and features classification • Shape matching • Shape retrieval • Optimized wave kernel signature • Heuristic optimization 1 Introduction A shape descriptor should be discriminative and insensitive to deformations and noises. Most of them are constructed from the spectral decomposition of the Laplace-Beltrami operator (LBO) associated with the shape [1-4]. These descriptors accomplish state-of-the-art performances in many shape analysis tasks, such as segmentation, registration, shape matching, and retrieval [5-7]. An example of spectral shape analysis is the global point signature (GPS) of a point on a shape, as proposed by Rus-B Seif Eddine Naffouti

The Visual Computer, Jul 1, 2022

Digital watermarking has attracted increasing attentions as it has been the current solution to c... more Digital watermarking has attracted increasing attentions as it has been the current solution to copyright protection and content authentication in today's digital transformation, which has become an issue to be addressed in multimedia technology. In this paper, we propose an advanced image watermarking system based on the discrete wavelet transform (DWT) in combination with the singular value decomposition (SVD). Firstly, at the sender side, DWT is applied on a grayscale cover image and then eigendecomposition is performed on original HH (high-high) components. Similar operation is done on a grayscale watermark image. Then, two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (iDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered watermark image and obtained perfect normalized correlation. Simulation consequences indicate that the presented scheme can satisfy the needs of visual imperceptibility and also has high security and strong robustness against many common attacks and signal processing operations. The proposed digital image watermarking system is also compared to state-of-the-art methods to confirm the reliability and supremacy.

HAL (Le Centre pour la Communication Scientifique Directe), 2016

This paper presents an improved wave kernel signature (WKS) using the modified particle swarm opt... more This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations, experiments show that the method is discriminative and robust to data perturbed by various noises. The algorithm is evaluated by its capability to differentiate between the salient feature points and to match efficiently between similar geometric structures for the same shape in different poses.

Le travail présenté dans ce mémoire a été effectué en cotutelle entre l'Université de Bourgogne a... more Le travail présenté dans ce mémoire a été effectué en cotutelle entre l'Université de Bourgogne au sein du Laboratoire d'Electronique, Informatique et Image (LE2I 1), site Le Creusot, d'une part, et l'Université de Monastir au sein du département Génie Electrique à l'unité de recherche des Etudes des Systèmes Industriels et des Energies Renouvelables (ESIER) à l'Ecole Nationale d'Ingénieurs de Monastir (ENIM 2), d'autre part ; et s'inscrit dans la thématique de recherche Instrumentation et Informatique de l'Image. Cette thèse a été supportée par le parrainage du programme européen Vision Image et Robotiques (VIBOT 3) (Erasmus Mundus).

The Visual Computer

Digital watermarking has attracted increasing attentions as it has been the current solution to c... more Digital watermarking has attracted increasing attentions as it has been the current solution to copyright protection and content authentication in today's digital transformation, which has become an issue to be addressed in multimedia technology. In this paper, we propose an advanced image watermarking system based on the discrete wavelet transform (DWT) in combination with the singular value decomposition (SVD). Firstly, at the sender side, DWT is applied on a grayscale cover image and then eigendecomposition is performed on original HH (high-high) components. Similar operation is done on a grayscale watermark image. Then, two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (iDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered watermark image and obtained perfect normalized correlation. Simulation consequences indicate that the presented scheme can satisfy the needs of visual imperceptibility and also has high security and strong robustness against many common attacks and signal processing operations. The proposed digital image watermarking system is also compared to state-of-the-art methods to confirm the reliability and supremacy.

Cette thèse porte sur la reconnaissance et l’appariement de formes 3D pour des systèmes intellige... more Cette thèse porte sur la reconnaissance et l’appariement de formes 3D pour des systèmes intelligents de vision par ordinateur. Elle décrit deux contributions principales à ce domaine. La première contribution est une implémentation d'un nouveau descripteur de formes construit à la base de la géométrie spectrale de l'opérateur de Laplace-Beltrami ; nous proposons une signature de point globale avancée (AGPS). Ce descripteur exploite la structure intrinsèque de l'objet et organise ses informations de manière efficace. De plus, AGPS est extrêmement compact puisque seulement quelques paires propres étaient nécessaires pour obtenir une description de forme précise. La seconde contribution est une amélioration de la signature du noyau d'onde ; nous proposons une signature du noyau d'onde optimisée (OWKS). La perfectionnement est avec un algorithme heuristique d'optimisation par essaim de particules modifié pour mieux rapprocher une requête aux autres formes apparte...

2016 4th International Conference on Control Engineering & Information Technology (CEIT), 2016

This paper presents an improved wave kernel signature (WKS) using the modified particle swarm opt... more This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations, experiments show that the method is discriminative and robust to data perturbed by various noises. The algorithm is evaluated by its capability to differentiate between the salient feature points and to match efficiently between similar geometric structures for the same shape in different poses.

Cette these porte sur la reconnaissance et l’appariement de formes 3D pour des systemes intellige... more Cette these porte sur la reconnaissance et l’appariement de formes 3D pour des systemes intelligents de vision par ordinateur. Elle decrit deux contributions principales a ce domaine. La premiere contribution est une implementation d'un nouveau descripteur de formes construit a la base de la geometrie spectrale de l'operateur de Laplace-Beltrami ; nous proposons une signature de point globale avancee (AGPS). Ce descripteur exploite la structure intrinseque de l'objet et organise ses informations de maniere efficace. De plus, AGPS est extremement compact puisque seulement quelques paires propres etaient necessaires pour obtenir une description de forme precise. La seconde contribution est une amelioration de la signature du noyau d'onde ; nous proposons une signature du noyau d'onde optimisee (OWKS). La perfectionnement est avec un algorithme heuristique d'optimisation par essaim de particules modifie pour mieux rapprocher une requete aux autres formes apparte...

Signal, Image and Video Processing, 2018

This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm opt... more This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. The variance parameter and its setting mode play a central role in this kernel. In order to circumvent a purely arbitrary choice of the internal parameters of the WKS algorithm, we present a four-step feature descriptor framework in an effort to further improve the classical wave kernel signature (WKS) by acting on its variance parameter. The advantage of the enhanced method comes from the tuning of the variance parameter using MPSO and the selection of the first vector from the constructed OWKS at its first energy scale, thus giving rise to substantially better matching and retrieval accuracy for deformable 3D shape. The special choice of this vector is to extremely reinforce the stability for efficient salient features extraction method from the 3D meshes. Experimental results demonstrate the effectiveness of our proposed shape classification and retrieval approach in comparison with state-of-the-art methods. For instance, in terms of the nearest neighbor (NN) metric, the OWKS achieves a 96.9% score, with performance improvements of 83.5 and 90.4% over the baseline methods WKS and heat kernel signature, respectively. Keywords Shapes and features classification • Shape matching • Shape retrieval • Optimized wave kernel signature • Heuristic optimization 1 Introduction A shape descriptor should be discriminative and insensitive to deformations and noises. Most of them are constructed from the spectral decomposition of the Laplace-Beltrami operator (LBO) associated with the shape [1-4]. These descriptors accomplish state-of-the-art performances in many shape analysis tasks, such as segmentation, registration, shape matching, and retrieval [5-7]. An example of spectral shape analysis is the global point signature (GPS) of a point on a shape, as proposed by Rus-B Seif Eddine Naffouti

Signal Processing: Image Communication, 2017

We propose a novel 3D shape descriptor, called the Advanced Global Point Signature (AGPS), which ... more We propose a novel 3D shape descriptor, called the Advanced Global Point Signature (AGPS), which is based on spectral analysis and is obtained by linear combination of some scaled eigenfunctions of the Laplace-Beltrami operator. Since it is built upon the concept of Global Point Signature, AGPS inherits several useful properties such as robustness to noise, stability and scale invariance. An AGPS-based method for extracting salient features from semi-rigid objects represented by triangular mesh surfaces is introduced. Due to its discriminative power, the associated AGPS values with each point remain extremely stable, which allows for simple and efficient shape characterization and robust salient point extraction. To assert our method regarding its robustness against noise and topological modifications, experiments on multiple benchmark datasets under unfavorable circumstances were performed. The method is also compared to state of the art methods for shape classification and retrieval.

14th International Conference on Sciences and Techniques of Automatic Control & Computer Engineering - STA'2013, 2013

ABSTRACT

2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2015

Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-ri... more Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-rigid three-dimensional shapes and Classical Multidimensional Scaling (Classical MDS) method in object classification which we quote, in particular, the example of Jian Sun et al. (2009) [1]. However, in this paper, the main focuses on classification that we propose a concise and provably factorial method by invoking Principal Component Analysis (PCA) as a classifier to improve the scheme of 3D shape classification. To avoid losing or disordering information after extracting features from the mesh, PCA is used instead of the Classical MDS to discriminate-as much as possible-feature points for each 3D shape in several poses. To demonstrate the practical relevance of this scheme, we present, illustrate and compare several assessments of the two proposed methods for non-rigid three-dimensional shapes classification based on heat diffusion. Across a collection of shapes, our results analysis show that the proposed contribution outperforms the classification method without PCA.

Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-ri... more Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-rigid three-dimensional shapes and Classical Multidimensional Scaling (Classical MDS) method in object classification which we quote, in particular, the example of Jian Sun et al. (2009) [1]. However, in this paper, the main focuses on classification that we propose a concise and provably factorial method by invoking Principal Component Analysis (PCA) as a classifier to improve the scheme of 3D shape classification. To avoid losing or disordering information after extracting features from the mesh, PCA is used instead of the Classical MDS to discriminate-as much as possible-feature points for each 3D shape in several poses. To demonstrate the practical relevance of this scheme, we present, illustrate and compare several assessments of the two proposed methods for non-rigid three-dimensional shapes classification based on heat diffusion. Across a collection of shapes, our results analysis show that the proposed contribution outperforms the classification method without PCA.

Signal Processing-image Communication, Oct 1, 2017

We propose a novel 3D shape descriptor, called the Advanced Global Point Signature (AGPS), which ... more We propose a novel 3D shape descriptor, called the Advanced Global Point Signature (AGPS), which is based on spectral analysis and is obtained by linear combination of some scaled eigenfunctions of the Laplace-Beltrami operator. Since it is built upon the concept of Global Point Signature, AGPS inherits several useful properties such as robustness to noise, stability and scale invariance. An AGPS-based method for extracting salient features from semi-rigid objects represented by triangular mesh surfaces is introduced. Due to its discriminative power, the associated AGPS values with each point remain extremely stable, which allows for simple and efficient shape characterization and robust salient point extraction. To assert our method regarding its robustness against noise and topological modifications, experiments on multiple benchmark datasets under unfavorable circumstances were performed. The method is also compared to state of the art methods for shape classification and retrieval.

Signal, Image and Video Processing, Jan 22, 2018

This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm opt... more This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. The variance parameter and its setting mode play a central role in this kernel. In order to circumvent a purely arbitrary choice of the internal parameters of the WKS algorithm, we present a four-step feature descriptor framework in an effort to further improve the classical wave kernel signature (WKS) by acting on its variance parameter. The advantage of the enhanced method comes from the tuning of the variance parameter using MPSO and the selection of the first vector from the constructed OWKS at its first energy scale, thus giving rise to substantially better matching and retrieval accuracy for deformable 3D shape. The special choice of this vector is to extremely reinforce the stability for efficient salient features extraction method from the 3D meshes. Experimental results demonstrate the effectiveness of our proposed shape classification and retrieval approach in comparison with state-of-the-art methods. For instance, in terms of the nearest neighbor (NN) metric, the OWKS achieves a 96.9% score, with performance improvements of 83.5 and 90.4% over the baseline methods WKS and heat kernel signature, respectively. Keywords Shapes and features classification • Shape matching • Shape retrieval • Optimized wave kernel signature • Heuristic optimization 1 Introduction A shape descriptor should be discriminative and insensitive to deformations and noises. Most of them are constructed from the spectral decomposition of the Laplace-Beltrami operator (LBO) associated with the shape [1-4]. These descriptors accomplish state-of-the-art performances in many shape analysis tasks, such as segmentation, registration, shape matching, and retrieval [5-7]. An example of spectral shape analysis is the global point signature (GPS) of a point on a shape, as proposed by Rus-B Seif Eddine Naffouti

The Visual Computer, Jul 1, 2022

Digital watermarking has attracted increasing attentions as it has been the current solution to c... more Digital watermarking has attracted increasing attentions as it has been the current solution to copyright protection and content authentication in today's digital transformation, which has become an issue to be addressed in multimedia technology. In this paper, we propose an advanced image watermarking system based on the discrete wavelet transform (DWT) in combination with the singular value decomposition (SVD). Firstly, at the sender side, DWT is applied on a grayscale cover image and then eigendecomposition is performed on original HH (high-high) components. Similar operation is done on a grayscale watermark image. Then, two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (iDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered watermark image and obtained perfect normalized correlation. Simulation consequences indicate that the presented scheme can satisfy the needs of visual imperceptibility and also has high security and strong robustness against many common attacks and signal processing operations. The proposed digital image watermarking system is also compared to state-of-the-art methods to confirm the reliability and supremacy.

HAL (Le Centre pour la Communication Scientifique Directe), 2016

This paper presents an improved wave kernel signature (WKS) using the modified particle swarm opt... more This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations, experiments show that the method is discriminative and robust to data perturbed by various noises. The algorithm is evaluated by its capability to differentiate between the salient feature points and to match efficiently between similar geometric structures for the same shape in different poses.

Le travail présenté dans ce mémoire a été effectué en cotutelle entre l'Université de Bourgogne a... more Le travail présenté dans ce mémoire a été effectué en cotutelle entre l'Université de Bourgogne au sein du Laboratoire d'Electronique, Informatique et Image (LE2I 1), site Le Creusot, d'une part, et l'Université de Monastir au sein du département Génie Electrique à l'unité de recherche des Etudes des Systèmes Industriels et des Energies Renouvelables (ESIER) à l'Ecole Nationale d'Ingénieurs de Monastir (ENIM 2), d'autre part ; et s'inscrit dans la thématique de recherche Instrumentation et Informatique de l'Image. Cette thèse a été supportée par le parrainage du programme européen Vision Image et Robotiques (VIBOT 3) (Erasmus Mundus).

The Visual Computer

Digital watermarking has attracted increasing attentions as it has been the current solution to c... more Digital watermarking has attracted increasing attentions as it has been the current solution to copyright protection and content authentication in today's digital transformation, which has become an issue to be addressed in multimedia technology. In this paper, we propose an advanced image watermarking system based on the discrete wavelet transform (DWT) in combination with the singular value decomposition (SVD). Firstly, at the sender side, DWT is applied on a grayscale cover image and then eigendecomposition is performed on original HH (high-high) components. Similar operation is done on a grayscale watermark image. Then, two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (iDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered watermark image and obtained perfect normalized correlation. Simulation consequences indicate that the presented scheme can satisfy the needs of visual imperceptibility and also has high security and strong robustness against many common attacks and signal processing operations. The proposed digital image watermarking system is also compared to state-of-the-art methods to confirm the reliability and supremacy.

Cette thèse porte sur la reconnaissance et l’appariement de formes 3D pour des systèmes intellige... more Cette thèse porte sur la reconnaissance et l’appariement de formes 3D pour des systèmes intelligents de vision par ordinateur. Elle décrit deux contributions principales à ce domaine. La première contribution est une implémentation d'un nouveau descripteur de formes construit à la base de la géométrie spectrale de l'opérateur de Laplace-Beltrami ; nous proposons une signature de point globale avancée (AGPS). Ce descripteur exploite la structure intrinsèque de l'objet et organise ses informations de manière efficace. De plus, AGPS est extrêmement compact puisque seulement quelques paires propres étaient nécessaires pour obtenir une description de forme précise. La seconde contribution est une amélioration de la signature du noyau d'onde ; nous proposons une signature du noyau d'onde optimisée (OWKS). La perfectionnement est avec un algorithme heuristique d'optimisation par essaim de particules modifié pour mieux rapprocher une requête aux autres formes apparte...

2016 4th International Conference on Control Engineering & Information Technology (CEIT), 2016

This paper presents an improved wave kernel signature (WKS) using the modified particle swarm opt... more This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations, experiments show that the method is discriminative and robust to data perturbed by various noises. The algorithm is evaluated by its capability to differentiate between the salient feature points and to match efficiently between similar geometric structures for the same shape in different poses.

Cette these porte sur la reconnaissance et l’appariement de formes 3D pour des systemes intellige... more Cette these porte sur la reconnaissance et l’appariement de formes 3D pour des systemes intelligents de vision par ordinateur. Elle decrit deux contributions principales a ce domaine. La premiere contribution est une implementation d'un nouveau descripteur de formes construit a la base de la geometrie spectrale de l'operateur de Laplace-Beltrami ; nous proposons une signature de point globale avancee (AGPS). Ce descripteur exploite la structure intrinseque de l'objet et organise ses informations de maniere efficace. De plus, AGPS est extremement compact puisque seulement quelques paires propres etaient necessaires pour obtenir une description de forme precise. La seconde contribution est une amelioration de la signature du noyau d'onde ; nous proposons une signature du noyau d'onde optimisee (OWKS). La perfectionnement est avec un algorithme heuristique d'optimisation par essaim de particules modifie pour mieux rapprocher une requete aux autres formes apparte...

Signal, Image and Video Processing, 2018

This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm opt... more This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. The variance parameter and its setting mode play a central role in this kernel. In order to circumvent a purely arbitrary choice of the internal parameters of the WKS algorithm, we present a four-step feature descriptor framework in an effort to further improve the classical wave kernel signature (WKS) by acting on its variance parameter. The advantage of the enhanced method comes from the tuning of the variance parameter using MPSO and the selection of the first vector from the constructed OWKS at its first energy scale, thus giving rise to substantially better matching and retrieval accuracy for deformable 3D shape. The special choice of this vector is to extremely reinforce the stability for efficient salient features extraction method from the 3D meshes. Experimental results demonstrate the effectiveness of our proposed shape classification and retrieval approach in comparison with state-of-the-art methods. For instance, in terms of the nearest neighbor (NN) metric, the OWKS achieves a 96.9% score, with performance improvements of 83.5 and 90.4% over the baseline methods WKS and heat kernel signature, respectively. Keywords Shapes and features classification • Shape matching • Shape retrieval • Optimized wave kernel signature • Heuristic optimization 1 Introduction A shape descriptor should be discriminative and insensitive to deformations and noises. Most of them are constructed from the spectral decomposition of the Laplace-Beltrami operator (LBO) associated with the shape [1-4]. These descriptors accomplish state-of-the-art performances in many shape analysis tasks, such as segmentation, registration, shape matching, and retrieval [5-7]. An example of spectral shape analysis is the global point signature (GPS) of a point on a shape, as proposed by Rus-B Seif Eddine Naffouti

Signal Processing: Image Communication, 2017

We propose a novel 3D shape descriptor, called the Advanced Global Point Signature (AGPS), which ... more We propose a novel 3D shape descriptor, called the Advanced Global Point Signature (AGPS), which is based on spectral analysis and is obtained by linear combination of some scaled eigenfunctions of the Laplace-Beltrami operator. Since it is built upon the concept of Global Point Signature, AGPS inherits several useful properties such as robustness to noise, stability and scale invariance. An AGPS-based method for extracting salient features from semi-rigid objects represented by triangular mesh surfaces is introduced. Due to its discriminative power, the associated AGPS values with each point remain extremely stable, which allows for simple and efficient shape characterization and robust salient point extraction. To assert our method regarding its robustness against noise and topological modifications, experiments on multiple benchmark datasets under unfavorable circumstances were performed. The method is also compared to state of the art methods for shape classification and retrieval.

14th International Conference on Sciences and Techniques of Automatic Control & Computer Engineering - STA'2013, 2013

ABSTRACT

2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2015

Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-ri... more Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-rigid three-dimensional shapes and Classical Multidimensional Scaling (Classical MDS) method in object classification which we quote, in particular, the example of Jian Sun et al. (2009) [1]. However, in this paper, the main focuses on classification that we propose a concise and provably factorial method by invoking Principal Component Analysis (PCA) as a classifier to improve the scheme of 3D shape classification. To avoid losing or disordering information after extracting features from the mesh, PCA is used instead of the Classical MDS to discriminate-as much as possible-feature points for each 3D shape in several poses. To demonstrate the practical relevance of this scheme, we present, illustrate and compare several assessments of the two proposed methods for non-rigid three-dimensional shapes classification based on heat diffusion. Across a collection of shapes, our results analysis show that the proposed contribution outperforms the classification method without PCA.