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2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)最新文献

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Performance investigation of selected NoSQL databases for massive remote sensing image data storage 面向海量遥感影像数据存储的NoSQL数据库性能研究
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364508
Yosra Hajjaji, I. Farah
{"title":"Performance investigation of selected NoSQL databases for massive remote sensing image data storage","authors":"Yosra Hajjaji, I. Farah","doi":"10.1109/ATSIP.2018.8364508","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364508","url":null,"abstract":"Today's sensors are like eyes in the sky, thanks to the growth of satellite remote sensing technologies. Therefore, we see a steady evolution of the usage of different types of sensor, from airborne and satellites platforms which are generating large quantities of remote sensing image for divers applications such as; smart city, disaster management, military intelligence and others. As a result, the rate of growth in the amount of data by satellite is increasing dramatically. The velocity has exceeded 1TB per day and it will certainly increase in the future. However, it becomes crucial for these huge volume data to be stored. So, how to store and manage it efficiently becomes a real challenge because traditional ways have intensive issues; they are expensive and difficult to extend. Therefore, we need some scalable and parallel models for remote sensing data storage and processing. In this paper, we describe a scalable and distributed architecture for massive remote sensing data storage based on three No SQL databases (Apache Cassandra, Apache HBase, MongoBD). Also, a Hadoop-based framework is proposed to manage the big remote sensing data in a distributed and parallel manner.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"822 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127222634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Detection of forest strata volume using LiDAR data 利用激光雷达数据检测森林地层体积
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364496
Ameni Mkaouar, A. Kallel, R. Guidara, Zouhaier Ben Rabah
{"title":"Detection of forest strata volume using LiDAR data","authors":"Ameni Mkaouar, A. Kallel, R. Guidara, Zouhaier Ben Rabah","doi":"10.1109/ATSIP.2018.8364496","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364496","url":null,"abstract":"Light Detection And Ranging (LiDAR), which is an active remote sensing technique with a high spatial resolution, data was used to extract the 3D structure of the canopy and then to estimate some biophysical properties of the shrubs in a forested area. Several forest attributes, such as the Canopy Height Model (CHM) and the Digital Terrain Model (DTM), were reconstructed by our approach. The 3D structure of dominant trees and shrubs was also described, and the Leaf Area Index (LAI) of the shrubs was correctly estimated. Our developed methods were tested and evaluated using realistic vegetated scenes simulated on DART (Discrete Anisotropic Radiative transfer).","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134376547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Fault detection using UKF-based optimized EWMA method in wastewater treatment plant 基于ukf的优化EWMA方法在污水处理厂故障检测中的应用
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364489
Imen Baklouti Djmal, M. Mansouri, M. Nounou, H. Nounou, A. Hamida
{"title":"Fault detection using UKF-based optimized EWMA method in wastewater treatment plant","authors":"Imen Baklouti Djmal, M. Mansouri, M. Nounou, H. Nounou, A. Hamida","doi":"10.1109/ATSIP.2018.8364489","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364489","url":null,"abstract":"In this work, Unscented Kalman Filter (UKF) based Optimized exponentially weighted moving average (OEWMA) is suggested for fault detection (FD) in a Wastewater Treatment Plant (WWTP). UKF method is suggested, to compute the residual of the true and the estimated data of WWTP, in addition, the Optimized EWMA is utilised to the faults in a simulated WWTP. The FD techniques will be tested using simulated data, which are generated using the simulated COST benchmark BSM1 of wastewater treatment model, provided by the IWA Task Group of Control Strategies. The results of the detection of the UKF-based Optimized EWMA are appraised with two criteria of FD : the missed detection rate (MDR) and the false alarm rate (FAR).","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132629508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Frequent pattern mining for online handwriting recognition 频繁模式挖掘用于在线手写识别
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364482
C. Gmati, Oumaima Sliti, H. Hamam, Z. Lachiri
{"title":"Frequent pattern mining for online handwriting recognition","authors":"C. Gmati, Oumaima Sliti, H. Hamam, Z. Lachiri","doi":"10.1109/ATSIP.2018.8364482","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364482","url":null,"abstract":"In this paper, we address the problem of defining and modeling the handwriting signal using its geometrical and spatio-temporal features, in order to improve the recognition task. We use the frequent pattern methods to enhance the quality of the signature vector extracted from the handwritten character. Two types of frequent patterns are employed to represent the handwritten characters pertinently: the maximal and closed frequent patterns. We created a new database that contains words of two different letters. The generated results are very promising, through which we have demonstrated that the “minimum threshold”, which is an essential parameter in the frequent patterns mining algorithms, represent a key feature in the characters description.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116527100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Left ventricle detection in echocardiography videos 超声心动图视频中的左心室检测
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364476
Dhouha Attia, A. Benazza-Benyahia
{"title":"Left ventricle detection in echocardiography videos","authors":"Dhouha Attia, A. Benazza-Benyahia","doi":"10.1109/ATSIP.2018.8364476","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364476","url":null,"abstract":"In this paper, we propose a new fully automated approach of Left Ventricle (LV) segmentation in 2D echocardiography videos. The proposed segmentation method combines texture gradients in moving sequences. The novelty of our approach consists in exploiting simultaneously the intra-frame and the motion information. Experiments are carried out on apical echocardiographic sequences and indicate the benefit that can be drawn from the proposed method in terms of both accuracy and computational complexity.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114169531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Investigation of glottal flow parameters for voice pathology detection on SVD and MEEI databases 基于SVD和MEEI数据库的声门流动参数语音病理检测研究
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364517
Kadria Ezzine, M. Frikha
{"title":"Investigation of glottal flow parameters for voice pathology detection on SVD and MEEI databases","authors":"Kadria Ezzine, M. Frikha","doi":"10.1109/ATSIP.2018.8364517","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364517","url":null,"abstract":"This paper investigated the performance of glottal flow features for the voice pathology detection particularly begnin and malignant tumors in two distinct databases. Glottal features have been widely used over the last years in pattern recognition process. The purpose of this work was to find out the most relevant glottal flow features for detecting voice disorders from normal ones. In order to choose the discriminative features, two different selection measures were applied in this work. The experiments were carried out using two different databases, “MEEI” and “SVD”, American and German databases, respectively. These databases included normal and pathalogical utterances pronounced by male and female speakers. Only the sustained vowel /a/ was used in classification task. Artificial Neuron Network (ANN) and Support Vector Machines (SVM) were used to perform the classification of normal-pathological voice. The experimental results prove that there is clear difference in performance of these glottal features independently of the used databases. The top-features selected were also varied from one database to another. There is a high accuracies using the SVM classifier, but it remains less important compared to those obtained using the ANN. The best classification rates achieved are 99.27% and 93.66% for SVD and MEEI databases, respectively.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134376563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Segmentation of low-grade gliomas based on the growing region and level sets techniques 基于生长区域和水平集技术的低级别胶质瘤分割
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364479
R. Zaouche, Ahror Belaid, Bassel Solaiman, D. Salem, S. Tliba
{"title":"Segmentation of low-grade gliomas based on the growing region and level sets techniques","authors":"R. Zaouche, Ahror Belaid, Bassel Solaiman, D. Salem, S. Tliba","doi":"10.1109/ATSIP.2018.8364479","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364479","url":null,"abstract":"In this paper, we propose a novel semi-automatic segmentation method based on the local image properties. Its originality is twofold, the first stands on the intensity invariant of phase-local information for the purpose of low-grade gliomas segmentation in MR images. In a second time, a level set method driven is combined to growing region so as to improve tumor detection. Experiments were conducted on a set of medical images. A comparison between the obtained results and the manual segmentation collected from experts is performed. The preliminary results are interesting and encouraging.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122000024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Improved shewhart chart for damage detection of structural health monitoring systems 结构健康监测系统损伤检测的改进shewhart图
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364484
Marwa Chaabane, A. Hamida, M. Mansouri, H. Nounou, M. Nounou
{"title":"Improved shewhart chart for damage detection of structural health monitoring systems","authors":"Marwa Chaabane, A. Hamida, M. Mansouri, H. Nounou, M. Nounou","doi":"10.1109/ATSIP.2018.8364484","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364484","url":null,"abstract":"In this paper, our objective is to propose a state estimation-based damage detection method to enhance monitoring of structural health systems. The proposed method, which is called particle filter (PF)-based Shewhart chart, combines the benefits of the state estimation technique with the Shewhart control chart. The main advantages of PF-based Shewhart are twofold: (i) The PF is proposed to estimate the nonlinear states of a structural health monitoring (SHM) systems; (ii) The Shewhart chart is proposed to detect damages in the mean in the SHM systems. The performance of the proposed method is proved through the three degree of freedom spring-mass-dashpot system and the effectiveness of the proposed method in damage detection is showed based on the presented results.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126196654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Early smoke detection of forest wildfire video using deep belief network 基于深度信念网络的森林野火视频早期烟雾检测
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364446
Rabeb Kaabi, M. Sayadi, M. Bouchouicha, F. Fnaiech, E. Moreau, J. Ginoux
{"title":"Early smoke detection of forest wildfire video using deep belief network","authors":"Rabeb Kaabi, M. Sayadi, M. Bouchouicha, F. Fnaiech, E. Moreau, J. Ginoux","doi":"10.1109/ATSIP.2018.8364446","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364446","url":null,"abstract":"This paper presents a novel approach for smoke detection to overcome forest wildfires based on machine learning technique (Deep Belief Network). Video smoke detection is applied on many surveillance and security applications. Smoke detection method should have a high detection rate to have a strong detector of smoke detection. Deep Belief Network which is a stacked layers of Restricted Boltzman Machine is the technique that we used for smoke detection. This technique extracts and classify smoke and no smoke regions simultaneously. The effectiveness of our implemented smoke detection method is evaluated after calculating smoke detection rate, time of pre-traing and time of fine-tuning. The higher is the detection rate the better is the smoke method and the lowest is the time of pre-training and fine-tuning the speeder is the method for smoke detection.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116835166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 26
A novel approach based on high order tensor and multi-scale locals features for 3D face recognition 基于高阶张量和多尺度局部特征的三维人脸识别新方法
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364461
Mohcene Bessaoudi, M. Belahcene, A. Ouamane, S. Bourennane
{"title":"A novel approach based on high order tensor and multi-scale locals features for 3D face recognition","authors":"Mohcene Bessaoudi, M. Belahcene, A. Ouamane, S. Bourennane","doi":"10.1109/ATSIP.2018.8364461","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364461","url":null,"abstract":"This paper presents an efficient framework for verification using 3D information based on high order tensor representation in uncontrolled conditions. The 3D depth images are subdivided into sub-blocks and the Multi-Scale Local Binarised Statistical Image Features (MSBSIF) + Multi-Scale local phase quantization (MSLPQ) histograms are extracted and concatenated from each block and organized as a 3rd order tensor. Moreover, two steps of dimensionally reduction to the face tensor are used. Firstly, Multilinear Principal Component Analysis (MPCA) is used to project the face tensor in a new subspace features in which the dimension of each mode tensor is reduced. After that, Enhanced Fisher Model (EFM) is applied to discriminate the faces of diverse persons in the database. Finally, the corresponding is achieved based distance measurement. The proposed approach (MPCA+EFM) has been evaluated on the challenging face database Bosporus 3D. The experimental results demonstrate that our method attains a high authentication performance.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114716999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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