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53 results on '"Halim Benhabiles"'

51. Self-driven and direct spatio-temporal mechanisms for the vision-based parking slot surveillance

Author

Abhishek Jandial, Karim Hammoudi, Fadi Dornaika, Joseph Mouzna, and Halim Benhabiles

Subjects
Brightness, Geography, Parking guidance and information, Vision based, Robustness (computer science), Real-time computing, Novelty, Estimator, Operational capabilities, Self driven, Simulation
Abstract

A vision-based and straightforward parking space surveillance system is presented. In particular, the novelty of the approach lies in the use of self-driven and direct dissimilarity computing mechanisms based on image raw brightness. Notably, the parking occupancy is estimated in real-time by applying fast computed estimators that optimize a global dissimilarity measure over slot-centered parking images. Experimental results show the operational capabilities and the efficiency of the proposed system mechanisms.

Published
2016
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52. Belief-Function-Based Framework for Deformable 3D-Shape Retrieval

Author

Jean-Philippe Vandeborre, Hedi Tabia, and Halim Benhabiles

Subjects
Similarity (geometry), Geodesic, Heat kernel signature, business.industry, Active shape model, Feature extraction, Probability distribution, Pattern recognition, Function (mathematics), Artificial intelligence, Object (computer science), business, Mathematics
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.

Published
2014
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53. Fast simplification with sharp feature preserving for 3D point clouds

Author

Hedi Tabia, Olivier Aubreton, Hichem Barki, Halim Benhabiles, FOX MIIRE (LIFL), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), FOX MIIRE ( LIFL ), Laboratoire d'Informatique Fondamentale de Lille ( LIFL ), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique ( CNRS ) -Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Electronique, Informatique et Image ( Le2i ), and Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS )

Subjects
Theoretical computer science, business.industry, Sharp point, 0211 other engineering and technologies, Point cloud, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Cloud computing, 02 engineering and technology, [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Simple (abstract algebra), Feature (computer vision), 0202 electrical engineering, electronic engineering, information engineering, Point (geometry), Enhanced Data Rates for GSM Evolution, Cluster analysis, business, Algorithm, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, Mathematics
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 [1] which deals with sharp edge points preserving, and still produces similar results.

Published
2013
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