List of publications

Journals:

Hamid Laga and Hedi Tabia "Modeling and Exploring Co-variations in the Geometry and Configuration of Man-made 3D Shape Families" Computer Graphics Forum , 2017.
Abstract: We introduce co-variation analysis as a tool for modeling the way part geometries and configurations co-vary across a family of man-made 3D shapes. While man-made 3D objects exhibit large geometric and structural variations, the geometry, structure, and configuration of their individual components usually do not vary independently from each other but in a correlated fashion. The size of the body of an airplane, for example, constrains the range of deformations its wings can undergo to ensure that the entire object remains a functionally-valid airplane. These co-variation constraints, which are often non-linear, can be either physical, and thus they can be explicitly enumerated, or implicit to the design and style of the shape family. In this article, we propose a data-driven approach, which takes pre-segmented 3D shapes with known component-wise correspondences and learns how various geometric and structural properties of their components co-vary across the set. We demonstrate, using a variety of 3D shape families, the utility of the proposed co-variation analysis in various applications including 3D shape repositories exploration and shape editing where the propagation of deformations is guided by the co-variation analysis. We also show that the framework can be used for context-guided orientation of objects in 3D scenes.

Walid Hariri, Hedi Tabia, Nadir Farah, Abdallah Benouareth and David Declercq "3D facial expression recognition using kernel methods on Riemannian manifold" Engineering Applications of Artificial Intelligence , Volume 64, September 2017, Pages 25–32.
Abstract: Automatic human Facial Expressions Recognition (FER) is becoming of increased interest. FER finds its applications in many emerging areas such as affective computing and intelligent human computer interaction. Most of the existing work on FER has been done using 2D data which suffers from inherent problems of illumination changes and pose variations. With the development of 3D image capturing technologies, the acquisition of 3D data is becoming a more feasible task. The 3D data brings a more effective solution in addressing the issues raised by its 2D counterpart. State-of-the-art 3D FER methods are often based on a single descriptor which may fail to handle the large inter-class and intra-class variability of the human facial expressions. In this work, we explore, for the first time, the usage of covariance matrices of descriptors, instead of the descriptors themselves, in 3D FER. Since covariance matrices are elements of the non-linear manifold of Symmetric Positive Definite (SPD) matrices, we particularly look at the application of manifold-based classification to the problem of 3D FER. We evaluate the performance of the proposed framework on the BU-3DFE and the Bosphorus datasets, and demonstrate its superiority compared to the state-of-the-art methods.

Hedi Tabia and Hamid Laga "Multiple Vocabulary Coding for 3D Shape Retrieval Using Bag of Covariances" Pattern Recognition Letters , Volume 95, 1 August 2017, Pages 78–84.
Abstract: Bag of Covariance matrices (BoC) have been recently introduced as an extension of the standard Bag of Words (BoW) to the space of positive semi-definite matrices, which has a Riemannian structure. BoC descriptors can be constructed with various Riemannian metrics and using various quantization approaches. Each construction results in some quantization errors, which are often reduced by increasing the vocabulary size. This, however, results in a signature that is not compact, increasing both the storage and computation complexity. This article demonstrates that a compact signature, with minimum distortion, can be constructed by using multiple vocabulary based coding. Each vocabulary is constructed from a different quantization method of the covariance feature space. The proposed method also extracts non-linear dependencies between the different BoC signatures to compose the final compact signature. Our experiments show that the proposed approach can boost the performance of the BoC descriptors in various 3D shape classification and retrieval tasks.

Hedi Tabia and Hamid Laga "Learning shape retrieval from different modalities" Neurocomputing , Volume 253, 30 August 2017, Pages 24–33.
Abstract: We propose in this paper a new framework for 3D shape retrieval using queries of different modalities, which can include 3D models, images and sketches. The main scientific challenge is that different modalities have different representations and thus lie in different spaces. Moreover, the features that can be extracted from 2D images or 2D sketches are often different from those that can be computed from 3D models. Our solution is a new method based on Convolutional Neural Networks (CNN) that embeds all these entities into a common space. We propose a novel 3D shape descriptor based on local CNN features encoded using vectors of locally aggregated descriptors instead of conventional global CNN. Using a kernel function computed from 3D shape similarity, we build a target space in which wild images and sketches can be projected via two different CNNs. With this construction, matching can be performed in the common target space between same entities (sketch–sketch, image–image and 3D shape–3D shape) and more importantly across different entities (sketch-image, sketch-3D shape and image-3D shape). We demonstrate the performance of the proposed framework using different benchmarks including large scale SHREC 3D datasets.

Diogo Carbonera Luvizon, Hedi Tabia and David Picard "Learning features combination for human action recognition from skeleton sequences" Pattern Recognition Letters , 2017.
Abstract: Human action recognition is a challenging task due to the complexity of human movements and to the variety among the same actions performed by distinct subjects. Recent technologies provide the skeletal representation of human body extracted in real time from depth maps, which is a high discriminant information for efficient action recognition. In this context, we present a new framework for human action recognition from skeleton sequences. We propose extracting sets of spatial and temporal local features from subgroups of joints, which are aggregated by a robust method based on the VLAD algorithm and a pool of clusters. Several feature vectors are then combined by a metric learning method inspired by the LMNN algorithm with the objective to improve the classi cation accuracy using the nonparametric k-NN classi er. We evaluated our method on three public datasets, including the MSR-Action3D, the UTKinect-Action3D, and the Florence 3D Actions dataset. As a result, the proposed framework performance overcomes the methods in the state of the art on all the experiments.

Alexandre Perez, Hedi Tabia, David Declercq and Alain Zanotti "Using the conflict in Dempster-Shafer evidence theory as a rejection criterion in classifier outputs combination for 3D human action recognition" Image and Vision Computing , Volume 55, Part 2, November 2016, Pages 149–157.
Abstract: In this paper, we propose a comprehensive solution to 3D human action recognition including feature extraction, classification, and multiple classifier combination. We effectively present two feature extraction methods, four different types of well-known classifiers, and four multiple classifier combination strategies including a specially designed belief based method. In order to enhance the recognition accuracy, we propose a new rejection criterion based on the conflict from the information sources: the classifier outputs. We test our method on the MSRAction 3D dataset. Discarding examples using the conflict based criterion shows superior results than other combination approaches. Moreover this criterion allows choosing a tradeoff between the performance and rejection rate.

Hedi Tabia, Christophe Riedinger and Michel Jordan "Automatic reconstruction of heritage monuments from old architecture documents" Journal of Electronic Imaging, 26(1), 011006 (Nov 01, 2016).
Abstract: Generating automatically three-dimensional (3-D) models of heritage monuments such as medieval buildings is a very challenging task, particularly when only archival documents of that buildings remain. This paper presents a set of algorithms dedicated to the 3-D modeling of historical buildings from a collection of old architecture plans, including floor plans, elevations, and cutoffs. Image processing algorithms help to detect and localize main structures of the building from the floor plans (thick and thin walls, openings). The extrusion of the walls allow us to build a first 3-D model. We compute height informations and add textures to the model by analyzing the elevation images from the same collection of documents. We applied this pipeline to 18th century plans of the Château de Versailles and show results for two different parts of the Ch\^ateau.

Halim Benhabiles and Hedi Tabia "Convolutional neural network for pottery retrieval" Journal of Electronic Imaging, 26(1), 011005 (Oct 25, 2016).
Abstract: The effectiveness of the convolutional neural network (CNN) has already been demonstrated in many challenging tasks of computer vision, such as image retrieval, action recognition, and object classification. This paper specifically exploits CNN to design local descriptors for content-based retrieval of complete or nearly complete three-dimensional (3-D) vessel replicas. Based on vector quantization, the designed descriptors are clustered to form a shape vocabulary. Then, each 3-D object is associated to a set of clusters (words) in that vocabulary. Finally, a weighted vector counting the occurrences of every word is computed. The reported experimental results on the 3-D pottery benchmark show the superior performance of the proposed method.

Walid Hariri, Hedi Tabia, Nadir Farah, Abdallah Benouareth and David Declercq "3D Face recognition using covariance based descriptors" Pattern Recognition Letters , March (2016).
Abstract: In this paper, we propose a new 3D face recognition method based on covariance descriptors. Unlike feature-based vectors, covariance-based descriptors enable the fusion and the encoding of different types of features and modalities into a compact representation. The covariance descriptors are symmetric positive definite matrices which can be viewed as an inner product on the tangent space of ($Sym_d^{+}$) the manifold of Symmetric Positive Definite (SPD) matrices. In this article, we study geodesic distances on the $Sym_d^{+}$ manifold and use them as metrics for 3D face matching and recognition. We evaluate the performance of the proposed method on the FRGCv2 and the GAVAB databases and demonstrate its superiority compared to other state of the art methods.

Christophe Riedinger, Hedi Tabia and Michel Jordan "3D Restitution of cultural heritage monuments from ancient floor plans" TRAITEMENT DU SIGNAL , volume 32, no 1, p. 87-108 (2015).
Abstract: In this paper, we present a complete set of algorithms for analyzing old ground plans of historical monuments, in order to build a 3D restitution of the corresponding monument. First, some image processing algorithms are used to analyze ground plans and extract a lot of informations such as thick and thin walls, openings, etc. The 3D model is then build by extruding the ground plans, and refined by adding textures from cut-offs and elevation images of the same collection of old plans. We applied our algorithms to ground plans of the Château de Versailles (XVIIIst century), providing us 3D models of two parts of the castle.

Hedi Tabia and Hamid Laga "Covariance-based Descriptors for efficient 3D shape matching, retrieval and classification" IEEE Transactions on Multimedia , volume 17(9), 1591-1603 (2015).
Abstract: State-of-the-art 3D shape classification and retrieval algorithms, hereinafter referred to as shape analysis, are often based on comparing signatures or descriptors that capture the main geometric and topological properties of 3D objects. None of the existing descriptors, however, achieves best performance on all shape classes. In this article, we explore, for the first time, the usage of covariance matrices of descriptors, instead of the descriptors themselves, in 3D shape analysis. Unlike histogrambased techniques, covariance-based 3D shape analysis enables the fusion and encoding of different types of features and modalities into a compact representation. Covariance matrices, however, are elements of the non-linear manifold of Symmetric Positive Definite (SPD) matrices and thus L2 metrics are not suitable for their comparison and clustering. In this article, we study geodesic distances on the Riemannian manifold of SPD matrices and use them as metrics for 3D shape matching and recognition. We then (1) introduce the concepts of Bag of Covariance matrices (BoC) and spatially-sensitive BoC as a generalization to the Riemannian manifold of SPD matrices of the traditional Bag of Features framework, and (2) generalize the standard kernel methods for supervised classification of 3D shapes to the space of covariance matrices. We evaluate the performance of the proposed Bag of Covariance matrices framework and covariance-based kernel methods and demonstrate their superiority compared to their descriptor-based couterparts in various 3D shape matching, retrieval and classification setups.

Zhouhui Lian, Afzal Godil, Benjamin Bustos, Mohamed Daoudi, Jeroen Hermans, Shun Kawamura, Yukinori Kurita, Guillaume Lavoué, Hien Van Nguyen, Ryutarou Ohbuchi, Yuki Ohkita, Yuya Ohishi, Fatih Porikli, Martin Reuter, Ivan Sipiran, Dirk Smeets, Paul Suetens, Hedi Tabia, Dirk Vandermeulen "A comparison of methods for non-rigid 3D shape retrieval" Pattern Recognition, , volume 46, number 1, 2013.
Abstract: Non-rigid 3D shape retrieval has become an active and important research topic in content-based 3D object retrieval. The aim of this paper is to measure and compare the performance of state-of-the-art methods for non-rigid 3D shape retrieval. The paper develops a new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using six commonly utilized measures. Models and evaluation tools of the new benchmark are publicly available on our web site [1].

Hedi Tabia, Mohamed Daoudi, Jean-Philippe Vandeborre and Olivier Colot "A parts-based approach for automatic 3D-shape categorization using belief functions" ACM Transactions on Intelligent Systems and Technology , volume 4(2), 33 (2013).
Abstract: Grouping 3D-objects into (semantically) meaningful categories is a challenging and important problem in 3D-mining and shape processing. Here, we present a novel approach to categorize 3D-objects. The method described in this paper, is a belief function based approach and consists of two stages. The training stage, where 3D-objects in the same category are processed and a set of representative parts is constructed, and the labeling stage, where unknown objects are categorized. The experimental results obtained on the Tosca- Sumner and the Shrec07 datasets show that the system efficiently performs in categorizing 3D-models.

Hedi Tabia, Mohamed Daoudi, Olivier Colot and Jean-Philippe Vandeborre "3D-object retrieval based on vector quantization of invariant descriptors" Journal of Electronic Imaging , volume 21, issue 2, April - June 2012.
Abstract: In this paper, a novel method for 3D-shape retrieval using Bag-of-Feature techniques (BoF) is proposed. This method is based on vector quantization of invariant descriptors of 3D-object patches. Firstly, it starts by selecting and then describing a set of points from the 3D-object. Such descriptors have the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster (word) in that vocabulary. Finally, a weighted vector counting the occurrences of every word is computed. These results clearly demonstrate that the method is robust to non-rigid and deformable shapes, in which the class of transformations may be very wide due to the capability of such shapes to bend and assume different forms

Hedi Tabia, Mohamed Daoudi, Olivier Colot and Jean-Philippe Vandeborre "A New 3D-Matching Method of Nonrigid and Partially Similar Models Using Curve Analysis" IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 33, number 4, April 2011.
Abstract: The 3D-shape matching problem plays a crucial role in many applications, such as indexing or modeling, by example. Here, we present a novel approach to matching 3D objects in the presence of nonrigid transformation and partially similar models. In this paper, we use the representation of surfaces by 3D curves extracted around feature points. Indeed, surfaces are represented with a collection of closed curves, and tools from shape analysis of curves are applied to analyze and to compare curves. The belief functions are used to define a global distance between 3D objects. The experimental results obtained on the TOSCA and the SHREC07 data sets show that the system performs efficiently in retrieving similar 3D models.

International conferences:

Alexandre Perez, Hedi Tabia, David Declercq and Alain Zanotti "Feature covariance for human action recognition" The sixth International Conference on Image Processing Theory, Tools and Applications (IPTA'16), Oulu, Finland, 2016.
Abstract: In this paper, we present a novel method for human action recognition using covariance features. Computationally efficient action features are extracted from the skeleton of the subject performing the action. They aim to capture relative positions and motion over time of the joints. These features are encoded into a compact representation using a covariance matrix. We evaluate the performance of the proposed method and demonstrate its superiority compared to related state-of- the-art methods on various datasets including: the MSR Action 3D, the MSR Daily Activity 3D and the UTKinect-Action dataset.

Walid Hariri, Hedi Tabia, Nadir Farah, Abdallah Benouareth and David Declercq "Hierarchical covariance description for 3D face matching and recognition under expression variation" International Conference on 3D Imaging (IC3D), Liege, Belgium, 2016.
Abstract: In this paper, we propose an hierarchical covariance descrip- tion for 3D face matching and recognition under expression variation. Unlike feature-based vectors, covariance-based de- scriptors enable the fusion and the encoding of different types of features and modalities into a compact representation. The efficiency of covariance descriptors however may depend on the size of its region of definition. Co-varying features in a small region do not capture sufficient properties of the face. Large regions on the other hand only capture coarse features, which may not be sufficiently discriminative. In this paper, we propose to represent a 3D face using a set feature points. Around each feature point, we consider three covariance description levels. In our experiment, we demonstrate the utility of this representation and present challenging results on different datasets including the BU-3DFE and the GAVAB datasets.

Bo Li, Yijuan Lu, Fuqing Duan, Shuilong Dong, Yachun Fan, Lu Qian, Hamid Laga, Haisheng Li, Yuxiang Li, Peng Liu, Maks Ovsjanikov, Hedi Tabia, Yuxiang Ye, Huanpu Yin, Ziyu Xue "SHREC'16 Track: 3D Sketch-Based 3D Shape Retrieval" Eurographics Workshop on 3D Object Retrieval, Lisbon, Portugal, 2016.
Abstract: Sketch-based 3D shape retrieval has unique representation availability of the queries and vast applications. Therefore, it has received more and more attentions in the research community of content-based 3D object retrieval. However, sketch-based 3D shape retrieval is a challenging research topic due to the semantic gap existing between the inaccurate representation of sketches and accurate representation of 3D models. In order to enrich and advance the study of sketch-based 3D shape retrieval, we initialize the research on 3D sketch-based 3D model retrieval and collect a 3D sketch dataset based on a developed 3D sketching interface which facilitates us to draw 3D sketches in the air while standing in front of a Microsoft Kinect. The objective of this track is to evaluate the performance of different 3D sketch-based 3D model retrieval algorithms using the hand-drawn 3D sketch query dataset and a generic 3D model target dataset. The benchmark contains 300 sketches that are evenly divided into 30 classes, as well as 1 258 3D models that are classified into 90 classes. In this track, nine runs have been submitted by five groups and their retrieval performance has been evaluated using seven commonly used retrieval performance metrics. We wish this benchmark, the comparative evaluation results and the corresponding evaluation code will further promote sketch-based 3D shape retrieval and its applications.

Christophe Riedinger, Michel Jordan, Hedi Tabia. "3D models over the centuries: From old floor plans to 3D representation" International Conference on 3D Imaging (IC3D), Liege, Belgium, 2014.
Abstract: This paper presents a set of algorithms dedicated to the 3D modeling of historical buildings from a collection of old architecture plans, including floor plans, elevations and cutoffs. Image processing algorithms help to detect and localize main structures of the building from the floor plans (thick and thin walls, openings). The extrusion of the walls allow us to build a first 3D model. We compute height informations and add textures to the model by analyzing the elevation images from the same collection of documents. We applied this pipeline to XVIIIth century plans of the Château de Versailles, and show results for two different parts of the Château.

Hedi Tabia and Ngoc-Son Vu. "3D Shape classification using information fusion" International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, 2014.
Abstract: The intent of 3D-model classification is to find categories of similar objects according to their shapes. This task is a challenging and important problem in 3D-mining and shape processing. In this paper, we present a novel method to categorize 3D-objects based on view-based descriptors. The proposed method goes into two stages. The first stage corresponds to the training in which 3D-objects in the same category are processed and a set of representative 2D views is selected, The second stage corresponds to the labelling in which unknown objects are classified using a belief based classifier. The experimental results obtained on the Shrec07 datasets show that the system efficiently performs in categorizing 3D-models.

Halim Benhabiles, Hedi Tabia, Jean-Philippe Vandeborre. "Belief-Function-Based Framework for Deformable 3D-Shape Retrieval" International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, 2014.
Abstract: The need for efficient tools to index and retrieve 3D content becomes even more acute. This paper presents a fully automatic 3D-object retrieval method. It consists of two main steps namely shape signature extraction to describe the shape of objects, and similarity computing to compute similarity between objects. In the first step (signature extraction), we use a shape descriptor called geodesic cords. This descriptor can be seen as a probability distribution sampled from a shape function. In the second step (similarity computing), a global distance, based on belief function theory, is computed between each pair wise of descriptors corresponding respectively to an object query and an object from a given database. Experiments on commonly-used benchmarks demonstrate that our method obtains competitive performance compared to 3D-object retrieval methods from the state-of-the-art.

Hedi Tabia, Hamid Laga, David Picard, Philippe-Henri Gosselin. " Covariance Descriptors for 3D Shape Matching and Retrieval" IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Columbus, Ohio, US, 2014.
Abstract: Several descriptors have been proposed in the past for 3D shape analysis, yet none of them achieves best performance on all shape classes. In this paper we propose a novel method for 3D shape analysis using the covariance matrices of the descriptors rather than the descriptors themselves. Covariance matrices enable efficient fusion of different types of features and modalities. They capture, using the same representation, not only the geometric and the spatial properties of a shape region but also the correlation of these properties within the region. Covariance matrices, however, lie on the manifold of Symmetric Positive Definite (SPD) tensors, a special type of Riemannian manifolds, which makes comparison and clustering of such matrices challenging. In this paper we study covariance matrices in their native space and make use of geodesic distances on the manifold as a dissimilarity measure. We demonstrate the performance of this metric on 3D face matching and recognition tasks. We then generalize the Bag of Features paradigm, originally designed in Euclidean spaces, to the Riemannian manifold of SPD matrices. We propose a new clustering procedure that takes into account the geometry of the Riemannian manifold. We evaluate the performance of the proposed Bag of Covariance Matrices framework on 3D shape matching and retrieval applications and demonstrate its superiority compared to descriptor-based techniques.

Hedi Tabia, David Picard, Hamid Laga, Philippe-Henri Gosselin. " Fast Approximation of Distance Between Elastic Curves using Kernels" British Machine Vision Conference (BMVC) United Kingdom. pp.11, 2013.
Abstract: Elastic shape analysis on non-linear Riemannian manifolds provides an efficient and elegant way for simultaneous comparison and registration of non-rigid shapes. In such formulation, shapes become points on some high dimensional shape space. A geodesic between two points corresponds to the optimal deformation needed to register one shape onto another. The length of the geodesic provides a proper metric for shape comparison. However, the computation of geodesics, and therefore the metric, is computationally very expensive as it involves a search over the space of all possible rotations and re- parameterization. This problem is even more important in shape retrieval scenarios where the query shape is compared to every element in the collection to search. In this paper, we propose a new procedure for metric approximation using the framework of kernel functions. We will demonstrate that this provides a fast approximation of the metric while preserving its invariance properties.

Hedi Tabia, David Picard, Hamid Laga and Philippe-Henri Gosselin. "3D Shape similarity using Vectors of locally Aggregated Tensors" In IEEE International Conference on Image Processing, Melbourne, Australia, September 2013.
Abstract: In this paper, we present an efficient 3D object retrieval method invariant to scale, orientation and pose. Our approach is based on the dense extraction of discriminative local descriptors extracted from 2D views. We aggregate the descriptors into a single vector signature using tensor products. The similarity between 3D models can then be efficiently computed with a simple dot product. Experiments on the SHREC12 commonly-used benchmark demonstrate that our approach obtains superior performance in searching for generic shapes.

Hedi Tabia, David Picard, Hamid Laga and Philippe-Henri Gosselin. "Compact Vectors of Locally Aggregated Tensors for 3D shape retrieval" In Eurographics Workshop on 3D Object Retrieval (3DOR). Girona, Spain, May 2013.
Abstract: During the last decade, a significant attention has been paid, by the computer vision and the computer graphics communities, to three dimensional (3D) object retrieval. Shape retrieval methods can be divided into three main steps: the shape descriptors extraction, the shape signatures and their associated similarity measures, and the machine learning relevance functions. While the first and the last points have vastly been addressed in recent years, in this paper, we focus on the second point; presenting a new 3D object retrieval method using a new coding/pooling technique and powerful 3D shape descriptors extracted from 2D views. For a given 3D shape, the approach extracts a very large and dense set of local descriptors. From these descriptors, we build a new shape signature by aggregating tensor products of visual descriptors. The similarity between 3D models can then be efficiently computed with a simple dot product. We further improve the compactness and discrimination power of the descriptor using local Principal Component Analysis on each cluster of descriptors. Experiments on the SHREC 2012 and the McGill benchmarks show that our approach outperforms the state-of-the-art techniques, including other BoF methods, both in compactness of the representation and in the retrieval performance.

Ahmed Maalej, Hedi Tabia, and Halim Benhabiles "Dynamic 3D Facial Expression Recognition Using Robust Shape Features" Scandinavian Conference on Image Analysis, Espoo, Finland, June 2013
Abstract: In this paper we present a novel approach for dynamic facial expression recognition based on 3D geometric facial features. Geodesic distances between correspondent 3D open curves are computed and used as features to describe the facial changes across sequences of 3D face scans. Hidden Markov Models (HMMs) are exploited to learn the curves shape variation trough a 3D frame sequences, and the trained models are used to classify six prototypic facial expressions. Our approach shows high performance, and an overall recognition rate of 94.45% is attained after a validation on the BU-4DFE database.

Halim Benhabiles, Olivier Aubreton, Hichem Barki and Hedi Tabia "Fast simplification with sharp feature preserving for 3D point clouds" The International Symposium on Programming and Systems (ISPS), Algiers, Algeria, April 2013
Abstract: This paper presents a fast point cloud simplification method that allows to preserve sharp edge points. The method is based on the combination of both clustering and coarse-to-fine simplification approaches. It consists to firstly create a coarse cloud using a clustering algorithm. Then each point of the resulting coarse cloud is assigned a weight that quantifies its importance, and allows to classify it into a sharp point or a simple point. Finally, both kinds of points are used to refine the coarse cloud and thus create a new simplified cloud characterized by high density of points in sharp regions and low density in flat regions. Experiments show that our algorithm is much faster than the last proposed simplification algorithm [SF09] which deals with sharp edge points preserving, and still produces similar results.

Hedi Tabia, Michèle Gouiffès, Lionel Lacassagne "Motion histogram quantification for human action recognition" 21th IEEE International Conference on Pattern Recognition (ICPR). Tsukuba, Japan. November, 2012.
Abstract: In this paper, we propose an approach for human activity categorizing based on the use of optical flow direction and magnitude features. The main contribution of this paper is the feature representation that mirrors the geometry of the human body and relationships between its moving regions when performing activities. The features are quantified using a quantization algorithm. We analyze the performance of two well-known classifiers: the Naïve Bayes and the SVM. The results show the effectiveness of our approach.

Hedi Tabia, Michèle Gouiffès, Lionel Lacassagne "Motion modeling for abnormal event detection in crowd scenes" International Symposium on signal, Images, Video and Communications, 4-6 July 2012 at University of Valenciennes, France.
Abstract: Since few years, video surveillance systems have become quite popular and have been undertaken in order to enhance our sense of security. In this context, this paper presents an approach for abnormal situation detection in video scenes. By analyzing the motion aspect of low level features instead of tracking segmented objects one by one, the proposed approach aims to produce a probabilistic model for normal situations. Abnormal ones are detected by comparing the real-time observation with usual situation parameters. The probabilistic model estimates abrupt changes and abnormal behaviors of a set of pixels selected from each frame in the scene. To demonstrate the interest of the methodology, we present the obtained results on videos that contain running of the bulls scenes, violence in areas where quiet must prevail and some collapsing events in real videos amassed by single camera.

Zhouhui Lian, Afzal Godil, Benjamin Bustos, Mohamed Daoudi, Jeroen Hermans, Shun Kawamura, Yukinori Kurita, Guillaume Lavoué, Hien Van Nguyen, Ryutarou Ohbuchi, Yuki Ohkita, Yuya Ohishi, Fatih Porikli, Martin Reuter, Ivan Sipiran, Dirk Smeets, Paul Suetens, Hedi Tabia, Dirk Vandermeulen "SHREC’11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes" Eurographics Workshop on 3D Object Retrieval 2011 .
Abstract: Non-rigid 3D shape retrieval has become an important research topic in content-based 3D object retrieval. The aim of this track is to measure and compare the performance of non-rigid 3D shape retrieval methods implemented by different participants around the world. The track is based on a new non-rigid 3D shape benchmark, which contains 600 watertight triangle meshes that are equally classified into 30 categories. In this track, 25 runs have been submitted by 9 groups and their retrieval accuracies were evaluated using 6 commonly-utilized measures.

Hedi Tabia, Mohamed Daoudi, Jean-Philippe Vandeborre and Olivier Colot "Non-rigid 3D shape classification using Bag-of-Feature techniques" IEEE International Conference on Multimedia and Expo (ICME), Barcelona, Spain, July 11-15, 2011.
Abstract: In this paper, we present a new method for 3D-shape categorization using Bag-of-Feature techniques (BoF). This method is based on vector quantization of invariant descriptors of 3D-object patches. We analyze the performance of two well-known classifiers: the Naïve Bayes and the SVM. The results show the effectiveness of our approach and prove that the method is robust to non-rigid and deformable shapes, in which the class of transformations may be very wide due to the capability of such shapes to bend and assume different forms.

Hedi Tabia, Mohamed Daoudi, Jean-Philippe Vandeborre and Olivier Colot "Deformable Shape Retrieval using Bag-of-Feature techniques" Electronic Imaging Conference, 3D Image Processing (3DIP) and Application-January 2011
Abstract: We present a novel method for 3D-shape matching using Bag-of-Feature techniques (BoF). The method starts by selecting and then describing a set of points from the 3D-object. Such descriptors have the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster (word) in that vocabulary. Finally, a BoF histogram counting the occurrences of every word is computed. These results clearly demonstrate that the method is robust to non-rigid and deformable shapes, in which the class of transformations may be very wide due to the capability of such shapes to bend and assume different forms.

Hedi Tabia, Mohamed Daoudi, Jean-Philippe Vandeborre and Olivier Colot "Local Visual Patch for 3D Shape Retrieval" ACM International Workshop on 3D Object Retrieval (in conjunction with ACM Multimedia 2010), Firenze, Italy, October 25, 2010.
Abstract: We present a novel method for 3D-object retrieval using Bag of Feature (BoF) approaches [8]. The method starts by selecting and then describing a set of points from the 3D-object. The proposed descriptor is an indexed collection of closed curves in R3 on the 3D-surface. Such descriptor has the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster (word) in that vocabulary. Finally, a BoF histogram counting the occurrences of every word is computed. In order to assess our method, we used shapes from the TOSCA and Sumner datasets. The results clearly demonstrate that the method is robust to many kind of transformations and produces higher precision compared with some state-of-the-art methods.

Hedi Tabia, Mohamed Daoudi, Jean-Philippe Vandeborre and Olivier Colot "3D-shape retrieval using curves and HMM" 20th IEEE International Conference on Pattern Recognition (ICPR 2010), Istanbul, Turkey, August 23-26, 2010.
Abstract: In this paper, we propose a new approach for 3D-shape matching. This approach encloses an off-line step and an on-line step. In the off-line one, an alphabet, of which any shape can be composed, is constructed. First, 3D-objects are subdivided into a set of 3D-parts. The subdivision consists to extract from each object a set of feature points with associated curves. Then the whole set of 3D-parts is clustered into different classes from a semantic point of view. After that, each class is modeled by a Hidden Markov Model (HMM). The HMM, which represents a character in the alphabet, is trained using the set of curves corresponding to the class parts. Hence, any 3D-object can be represented by a set of characters. The on-line step consists to compare the set of characters representing the 3D-object query and that of each object in the given dataset. The experimental results obtained on the TOSCA dataset show that the system efficiently performs in retrieving similar 3D-models.

French national conferences:

Hedi Tabia, Michèle Gouiffès, Lionel Lacassagne "Reconnaissance des activités humaines à partir des vecteurs de mouvement quantifiés" CORESA 2012 (COmpression et REprésentation des Signaux Audiovisuels)
Abstract: Dans cet article, nous proposons une approche pour la reconnaissance des activités humaines à partir des vidéos capturées à l’aide des caméras monoculaires. Nous présentons une méthode basée sur la quantification vectorielle de descripteurs de mouvement. Ces descripteurs sont calculés à partir de l’orientation et la magnitude des vecteurs du flux optique. Nous analysons les performances de deux classifieurs : le classifieur Bayesien Naïf et un classifieur basé sur les Séparateurs à Vaste Marge (SVM). Les résultats montrent l’efficacité de notre approche dans la classification des activités humaines sur la base de données KTH [1].

Hedi Tabia, Mohamed Daoudi, Jean-Philippe Vandeborre, Olivier Colot "Une approche pour la catégorisation des objets 3D basée sur la théorie des fonctions de croyance" COmpression et REprésentation des Signaux Audiovisuels, CORESA’2010, Oct. 2010
Abstract: Le groupement des objets 3D en catégories significatives est un problème très important dans le traitement des formes 3D. En introduisant une nouvelle technique de classification basée sur les fonctions de croyance, nous réussissons à catégoriser les objets 3D. Cette technique comporte deux étapes. Une première étape d’apprentissage, où les objets 3D d'une même catégorie sont traités et où un ensemble des parties représentatives de ces objets est construit, et une deuxième étape d'étiquetage, où des objets inconnus sont classifiés par catégorie. Le classifieur a été conçu et évalué sur une base de données de 400 objets 3D. Notre système atteint un taux de bonne reconnaissance de l’ordre de 85%.