Ouissem Henia

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Suzanne Koptur

Florida International University

David Seamon

Kansas State University

BORA YILDIRIM

Hacettepe University

Stephen Mackessy

University of Northern Colorado

Armando Marques-Guedes

UNL - New University of Lisbon

Merrill L Gassman

University of Illinois at Chicago

Dierle Nunes

PUC Minas

Titika Dimitroulia

National and Kapodistrian University of Athens

Ke Wu

École Polytechnique de Montréal

Jorge Jhoncon

Universidad Nacional de Educación "Enrique Guzmán y Valle

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Papers by Ouissem Henia

Real Time Vision Based Method for Finger Counting Through Shape Analysis with Convex Hull and PCA Techniques

International Review on Computers and Software (IRECOS), 2017

This paper presents a real time vision-based finger counting method combining convex-hull detecti... more This paper presents a real time vision-based finger counting method combining convex-hull detection and PCA techniques. The method starts by segmenting an input image to detect the area corresponding to the observed hand. For that purpose, a skin color detection method is used to differentiate the foreground containing the hand from the image background. Lighting variation can affect the accuracy of the segmentation which has an impact on the functioning of the proposed method. In order to deal with the lighting variation problem, HLS color space is used to represent the colors. Hand contour is then calculated and fingertips are detected through the detection of the convex-hull and convexity defects of the hand shape. The use of convex-hull algorithm is simple and gives accurate results when more than one finger is observed in the input image but the accuracy decreases when it comes to deal with the case where only one finger is observed. To overcome this problem, principal component analysis technique (PCA) is used to analyze the hand shape and detect the case where only one finger is observed with better accuracy. The proposed method could be utilized in Human Computer Interaction System (HCI) where the machine reacts to each detected number. Both real and synthetic images are used to test and demonstrate the potential of our method.

A New Depth-Based Function for 3D Hand Motion Tracking

Model-based methods to the tracking of an articulated hand in a video sequence generally use a co... more Model-based methods to the tracking of an articulated hand in a video sequence generally use a cost function to compare the hand pose with a parametric three-dimensional (3D) hand model. This comparison allows adapting the hand model parameters and it is thus possible to reproduce the hand gestures. Many proposed cost functions exploit either silhouette or edge features. Unfortunately, these functions cannot deal with the tracking of complex hand motion. This paper presents a new depth-based function to track complex hand motion such as opening and closing hand. Our proposed function compares 3D point clouds stemming from depth maps. Each hand point cloud is compared with several clouds of points which correspond to different model poses in order to obtain the model pose that is close to the hand one. To reduce the computational burden, we propose to compute a volume of voxels from a hand point cloud, where each voxel is characterized by its distance to that cloud. When we place a model point cloud inside this volume of voxels, it becomes fast to compute its distance to the hand point cloud. Compared with other well-known functions such as the directed Hausdorff distance(Huttenlocher et al., 1993), our proposed function is more adapted to the hand tracking problem and it is faster than the Hausdorff function.

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Minimisation hiérarchique pour le suivi des mouvements de la main

by M. Hariti, Ouissem Henia, and Saida Bouakaz

Les approches de suivi des mouvements de la main à base de modèle 3D peuvent être classifiées en ... more Les approches de suivi des mouvements de la main à base de modèle 3D peuvent être classifiées en deux catégories. La première catégorie utilise des filtres stochastiques comme le filtre de Kalman ou le filtre particulaire. La deuxième se base sur des méthodes déterministes définissant une fonction de dissimilarité qui compare les gestes de la main avec ceux du modèle 3D. La minimisation de cette fonction assure le suivi des mouvements de la main. Deux principaux problèmes surviennent lors de la minimisation. Le premier problème est celui des minimas locaux et le deuxième est celui du temps de calcul nécessaire pour se rapprocher suffisamment de la solution recherchée. Pour faire face à ces deux problèmes nous proposons une nouvelle fonction de dissimilarité qui est plus robuste face aux minimas locaux que d'autres fonctions très connues comme la fonction de Chanfrein[1]. Nous proposons aussi un algorithme de minimisation hiérarchique qui simplifie et améliore le suivi des mouvements de la main en diminuant les temps de calcul et en améliorant la robustesse face aux minimas locaux.

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A two-step minimization algorithm for model-based hand tracking

by Ouissem Henia, Saida Bouakaz, and M. Hariti

Model-based methods to the tracking of an articulated hand in a video sequence could be divided i... more Model-based methods to the tracking of an articulated hand in a video sequence could be divided in two categories. The first one, called stochastic methods, uses stochastic filters such as kalman or particle ones. The second category, named deterministic methods, defines a dissimilarity function to measure how well the hand model is aligned with the hand images of a video sequence. This dissimilarity function is then minimized to achieve the hand tracking. Two well-known problems are related to the minimization algorithms. The first one is that of local minima. The second problem is that of computing time required to reach the solution. These problems are compounded with the large number of degrees of freedom (DOF) of the hand (around 26). The choice of the function to be minimized and that of the minimization process can be an answer to these problems. In this paper two major contributions are presented. The first one defines a new dissimilarity function, which gives better results for hand tracking than other well-known functions like the directed chamfer or hausdorff distances. The second contribution proposes a minimization process that operates in two steps. The first one provides the global parameters of the hand, i.e. position and orientation of the palm, whereas the second step gives the local parameters of the hand, i.e. finger joint angles. Operating in two stages, the proposed two-step algorithm reduces the complexity of the minimization problem. Indeed, it seems more robust to local minima than a one-step algorithm and improves the computing time needed to get the desired solution.

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3D Hand Model Animation with a New Data-Driven Method

by Ouissem Henia and Saida Bouakaz

2011 Workshop on Digital Media and Digital Content Management, 2011

ABSTRACT This paper presents a data-driven method to track hand gesture and animate 3D hand model... more ABSTRACT This paper presents a data-driven method to track hand gesture and animate 3D hand model. The proposed method uses a new generation of active camera based on time of flight principle (The Swissranger4000). To achieve the tracking, the presented method exploits a database of hand gestures represented as 3D point clouds acquired from the Swissranger4000 video camera. In order to track a large number of hand poses with a database as small as possible we classify the hand gestures using a Principal Component Analysis (PCA). Applied to each point cloud, the PCA produces a new representation of the hand pose independent of the position and orientation in the 3D space. To explore fast and efficiently the database we use a comparison function based on 3D distance transform. Experimental results on real data demonstrate the potential of the method.