GRIFT Research Group

Tunisia

GRIFT Research Group

Tunisia
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Farhat M.,National School in Computer Science | M'Hiri S.,GRIFT Research Group | Tagina M.,National School in Computer Science
Procedia Engineering | Year: 2012

This article addresses the problem of the research of an unknown object in an unknown environment using visual servoing. The system is camera-in-hand system mounted directly on the robot manipulator, and a camera-fired system fixed on the top of the mobile platform and observes the scene from there. The method proposed is based on the epipolar constraint defined between the two cameras. While eye-in-hand camera covers the epipolar line, we compute the next best view to be observed. The system executes the task between two positions blindly, using a velocity control law which generates a safety movement for the manipulator robot. The proposed approach minimizes the computational time of the training and takes into account the safety and security of the environment. The paper first gives an overview of the existing approach for localizing an unknown object and evaluates some of those approaches and propose a novel approach. © 2012 The Authors.


Farhat M.,National School in Computer Science | Mhiri S.,GRIFT Research Group | Tagina M.,National School in Computer Science
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) | Year: 2015

Here, a comparative study of information fusion methods for instance object detection is proposed. Instance object detection is one of mean service that robots needs. Classical approaches are based on extracting discriminant and invariant features. However those features still have a limitation to represent all kinds of objects and satisfy all requirements (discrimination and invariance). Since no single feature can work well in various situations, we need to combine several features so that the robot can handle all kind of daily life objects. Our task consists in defining a strategy that can work on various objects and backgrounds without any prior knowledge. In this paper we propose a scheme to combine two descriptors using belief function theory. First, objects are extracted from image and described by two complementary descriptors: Dominant Color Descriptor for color description and Zernike Moments for shape description. Second, similarity indicators is computed between object of interest descriptors and each extracted object descriptors. Finally, those measures are combined into a belief functions in order to build a final decision about the object presence in the image taking the information uncertainty and imprecise into consideration. We have evaluated our approach with different methods of information fusion such as the weighted vote approach, the possibility theory and so forth. © Springer International Publishing Switzerland 2015.


Ben Othman I.,GRIFT Research Group | Ghorbel F.,GRIFT Research Group
2014 IEEE International Conference on Image Processing, ICIP 2014 | Year: 2014

Referring to the statistical point of view, we present in this work, a new criterion for evaluating neural networks stability compared to the Bayesian classifier. The stability comparison is performed by the error rate probability densities function estimated by the kernel-diffeomorphism semi-bounded Plug-in algorithm. The Bayesian and combination approaches for neural networks improve the performance and stability degree of the classical neural classifiers. © 2014 IEEE.


Othman I.B.,GRIFT Research Group | Ghorbel F.,GRIFT Research Group
NCTA 2014 - Proceedings of the International Conference on Neural Computation Theory and Applications | Year: 2014

In the industrial field, the artificial neural network classifiers are currently used and they are generally integrated of technologic systems which need efficient classifier. However, the lack of control over its mathematical formulation explains the instability of its classification results. In order to improve the prediction accuracy, most of researchers refer to the classifiers combination approach. This paper tries to illustrate the capability of an example of combined neural networks to improve the stability criterion of the single neural classifier. The stability comparison is performed by the error rate probability densities function estimated by a new variant of the kernel-diffeomorphism semi-bounded Plug-in algorithm.


Othman I.B.,GRIFT Research Group | Drira W.,GRIFT Research Group | El Ayeb F.,GRIFT Research Group | Ghorbel F.,GRIFT Research Group
VISAPP 2015 - 10th International Conference on Computer Vision Theory and Applications; VISIGRAPP, Proceedings | Year: 2015

In the industrial field, the artificial neural network classifiers are currently used and they are generally integrated of technologic systems which need efficient classifier. Statistical classifiers also have been developed in the same direction and different associations and optimization procedures have been proposed as Adaboost training or CART algorithm to improve the classification performance. However, the objective comparison studies between these novel classifiers stay marginal. In the present work, we intend to evaluate with a new criterion the classification stability between neural networks and some statistical classifiers based on the optimization Fischer criterion or the maximization of Patrick-Fischer distance orthogonal estimator. The stability comparison is performed by the error rate probability densities estimation which is valorised by the performed kernel-diffeomorphism Plug-in algorithm. The results obtained show that the statistical approaches are more stable compared to the neural networks. Copyright © 2015 SCITEPRESS - Science and Technology Publications All rights reserved.


El Ayeb F.,GRIFT Research Group | Ghorbel F.,GRIFT Research Group
ICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods | Year: 2013

Here, we intend to give a rule for the choice of the smoothing parameter of the orthogonal estimate of Patrick-Fisher distance in the sense of the Mean Integrate Square Error. The orthogonal series density estimate precision depends strongly on the choice of such parameter which corresponds to the number of terms in the series expansion used. By using series of random simulations, we illustrate the better performance of its dimensionality reduction in the mean of the misclassification rate. We show also its better behavior for real data. Different invariant shape descriptors describing handwritten digits are extracted from a large database. It serves to compare the proposed adjusted Patrick-Fisher distance estimator with a conventional feature selection method in the mean of the probability error of classification.

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