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International Conference on advanced Technologies for Signal and Image Processing (ATSIP)

作者： Nouha Dammak Yassine Ben Ayed MIRACL InfoRmation system and Advanced Computing Laboratory Sfax Tunisia MIRACL Information system and Advanced Computing Laboratory Sfax Tunisia

In this paper, classifying and indexing video genres using Support Vector Machines (SVMs) are based on only audio features. In fact, those segmentation parameters are extracted at block levels, which have a major benefit by capturing local temporal information. The main contribution of our study is to present a powerful combination between the two employed audio descriptors Mel Frequency Cepstral Coefficients (MFCC) and signal energy in order to classify three common video genres: several sports analysis and matches, both studio and fields news scenes over and above various multi-speaker and multiinstruments music clips. Validation of this approach was carried out on over 6 hours of video span token from YouTube and yielding a classification accuracy of 99.83%. Finally we discuss SVM kernels performance on our proposed dataset.

关键词： Support vector machines Kernel Accuracy Feature extraction Mel frequency cepstral coefficient Polynomials Testing

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Online pedestrian tracking with kalman filter and random ferns

Online pedestrian tracking with kalman filter and random fer...

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2014 2nd International Conference on Mechatronics, Robotics and Automation, ICMRA 2014

作者： Qu, Zhi Qiang Tu, Dan Lei, Jun College of Information System and Management National University of Defense Technology ChangSha Hunan China State Key Laboratory of Mathmatical Engineering and Advanced Computing WuXi Jiangsu China

ISBN: (纸本)9783038350781

Object tracking is one of the most important components in numerous applications of computer vision. Much progress has been made in recent years. The Tracking-Learning-Detection (TLD) algorithm achieves excellent performance on a set of challenging video sequences. However, classical TLD algorithm fails to track non-rigid pedestrian as complicated appearance, varying viewpoints, shape changes and occlusions. In this paper, we follow the TLD and propose a robust KMD framework which consists of the Kalman filter tracker, random ferns pedestrian detector and the pedestrian model. During the tracking process the tracker and detector are complementary: the Kalman filter tracker predicts the motion, the pedestrian detector searches the best-match appearance and the pedestrian model combines performance of both to determine final result and generate new samples for learning. Experimental results show our framework comparatively improves performance for pedestrian tracking in surveillance videos. © (2014) Trans Tech Publications, Switzerland.

关键词： Kalman filters

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Statistical parametric speech synthesis for Arabic language using ANN

Statistical parametric speech synthesis for Arabic language ...

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International Conference on advanced Technologies for Signal and Image Processing (ATSIP)

作者： Rebai Ilyes Yassine Ben Ayed MIRACL: Multimedia InfoRmation system and Advanced Computing Laboratory Sfax University Sfax Tunisie Universite de Sfax Sfax TN

Statistical parametric approach for speech synthesis becomes more popular over the concatenative approach due to the low size of the system and the high-quality speech. Moreover, few researches have been done in the field of speech synthesis for Arabic language with a poor quality of speech. In this paper, we propose a statistical parametric synthesis system for Arabic based on Artificial Neural Networks (ANN). Mel frequency Cepstral coefficients (MFCC), F0, energy and duration are the main components of our system. Speech waveform is generated from the predicted parameters F0, energy and MFCC. Different methods are proposed for this development process. In addition, we propose a method to solve the problem of discontinuities between neighboring segment boundaries in order to improve the speech quality. Experimental results of cepstral and prosodic parameters are given in this paper as well as the subjective evaluation.

关键词： Speech Mel frequency cepstral coefficient Artificial neural networks Speech synthesis Databases

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Lip tracking using particle filter and geometric model for visual speech recognition

Lip tracking using particle filter and geometric model for v...

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International Conference on Signal Processing and multimedia Applications (SIGMAP)

作者： Islem Jarraya Salah Werda Walid Mahdi Multimedia InfoRmation systems and Advanced Computing Laboratory (MIRACL) University of Sfax Sfax Tunisia

The automatic lip-reading is a technology which helps understanding messages exchanged in the case of a noisy environment or of elderly hearing impairment. To carry out this system, we need to implement three subsystems. There is a locating and tracking lips system, labial descriptors extraction system and a classification and speech recognition system. In this work, we present a spatio-temporal approach to track and characterize lip movements for the automatic recognition of visemes of the French language. First, we segment lips using the color information and a geometric model of lips. Then, we apply a particle filter to track lip movements. Finally, we propose to extract and classify the visual informations to recognize the pronounced viseme. This approach is applied with multiple speakers in natural conditions.

关键词： Mathematical model Lips Adaptation models Tracking Visualization Mouth Particle filters

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Dynamic and Adaptive Topology-Aware Load Balancing for Grids

Dynamic and Adaptive Topology-Aware Load Balancing for Grids

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IEEE International Conference on Computational Science and Engineering, CSE

作者： Haitham Barkallah Mariem Gzara Hanene Ben Abdallah Miracl (Multimedia InfoRmation systems and Advanced Computing Laboratory) University of Sfax Tunisia Higher School of Computer Sciences and Mathematics of Monastir University of Monastir Tunisia Faculty of Computing and Information Technology King Abdulaziz University Jeddah KSA

ISBN: (纸本)9781479979820

Jobs scheduling and load balancing functionalities are crucial for best Grid performance and utilization. In this paper, we present a dynamic and adaptive polyhedron topology-aware Grid load balancing method which we called Tun Grid. Its main objective is the extreme decentralization of the scheduling and the load balancing processes at the lowest processing and networking cost. It ensures load balancing through an adaptive local neighborhood propagation strategy of overload. Tun Grid was experimented using the Grid Sim simulator and compared to other methods. Experimental results are presented and discussed.

关键词： Topology Peer-to-peer computing Network topology Load management Educational institutions Heuristic algorithms

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A multi-objective genetic algorithm for model selection for support vector machines

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8862卷 809-819页

作者： Bouraoui, Amal Ayed, Yassine Ben Jamoussi, Salma Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL-Sfax University Sfax-Tunisia Technopole of Sfax: Av.Tunis Km 10 B.P. 242 Sfax3021 Tunisia

Selecting the proper Kernel function in SVMs and the specific parameters for that kernel is an important step in achieving a high performance learning machine. The objective of this research is to optimize SVMs parameters using different kernel functions. We cast this problem as a multi-objective optimization problem, where the classification accuracy, the number of support vectors and the margin define our objective functions. So, we introduce a method based on multi-objective evolutionary algorithm NSGA-II to solve this problem. We also introduce a multi-criteria selection operator for our NSGA-II. The proposed method is applied on some benchmark datasets. The experimental obtained results show the efficiency of the proposed method. © Springer International Publishing Switzerland 2014.

关键词： Support vector machines

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The OWL-S extension for the pervasive system

The OWL-S extension for the pervasive system

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IADIS International Conference on information systems 2013, IS 2013

作者： Achour, Fatma Jedidi, Anis Gargouri, Faiez Multimedia Information Systems and Advanced Computing Laboratory France

ISBN: (纸本)9789728939830

The goal of the semantic web, also known as a third generation, is to improve the web service research, discovery, selection, composition and integration. The pervasive systems correspond to a complete communication framework which allows the communicating objects to recognize and locate automatically. The pervasive system tasks are permitted by the semantic web services. The main interest of our research is to propose an adaptation framework of the application to the pervasive system based on semantic web services. In this paper, we present a part of our research work which represents the description of an OWL-S ontology extension to describe the pervasive system. This extension symbolizes the first level of data description in our pervasive system adaptation. © 2013 IADIS.

关键词： Web services

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Particle swarm optimization based method for Bayesian Network structure learning

Particle swarm optimization based method for Bayesian Networ...

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2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013

作者： Aouay, Saoussen Jamoussi, Salma Ayed, Yassine Ben MIRACL Multimedia InfoRmation System and Advanced Computing Laboratory Higher Institute of Computer Science and Multimedia Sfax BP 1030 Tunisia

ISBN: (纸本)9781467358149

Bayesian Networks (BNs) are good tools for representing knowledge and reasoning under conditions of uncertainty. In general, learning Bayesian Network structure from a data-set is considered a NP-hard problem, due to the search space complexity. A novel structure-learning method, based on PSO (Particle Swarm Optimization) and the K2 algorithm, is presented in this paper. To learn the structure of a bayesian network, PSO here is used for searching in the space of orderings. Then the fitness of each ordering is calculated by running the K2 algorithm and returning the score of the network consistent with it. The experimental results demonstrate that our approach produces better performance compared to others BN structure learning algorithms. © 2013 IEEE.

关键词： Bayesian networks

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Face pose tracking under arbitrary illumination changes

Face pose tracking under arbitrary illumination changes

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International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Ahmed Rekik Achraf Ben-Hamadou Walid Mahdi Pôle Technologique de Sfax Multimedia InfoRmation systems and Advanced Computing Laboratory (MIRACL) Sfax Tunisia Total Immersion SA R&D department Suresnes France

ISBN: (纸本)9781479976867

This paper presents a new method for 3D face pose tracking in arbitrary illumination change conditions using color image and depth data acquired by RGB-D cameras (e.g., Microsoft Kinect, Asus Xtion Pro Live, etc.). The method is based on an optimization process of an objective function combining photometric and geometric energy. The geometric energy is computed from depth data while the photometric energy is computed at each frame by comparing the current face texture to its corresponding in the reference face texture defined in the first frame. To handle the effect of changing lighting condition, we use a facial illumination model in order to solve which lighting variations has to be applied to the current face texture making it as close as possible to the reference texture. We demonstrate the accuracy and the robustness of our method in normal lighting conditions by performing a set of experiments on the Biwi Kinect head pose database. Moreover, the robustness to illumination changes is evaluated using a set of sequences for different persons recorded in severe lighting condition changes. These experiments show that our method is robust and precise under both normal and severe lighting conditions.

关键词： Face Lighting Three-dimensional displays Cameras Robustness Databases Color

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