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2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)最新文献

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Target recognition from ISAR image using polar mapping and shape matrix 利用极坐标映射和形状矩阵对ISAR图像进行目标识别
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231528
J. Cexus, A. Toumi, Maroua Riahi
{"title":"Target recognition from ISAR image using polar mapping and shape matrix","authors":"J. Cexus, A. Toumi, Maroua Riahi","doi":"10.1109/ATSIP49331.2020.9231528","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231528","url":null,"abstract":"This paper is devoted to the study of an automatic target recognition method to classify Inverse Synthetic Aperture Radar (ISAR) images of moving targets. 2-D ISAR imagery generation allows to obtain a pertinent representation of the moving target reflectivity distribution employing radar imaging system. The proposed target recognition technique is based on a combined the polar representation with shape matrix description applied on ISAR image. These descriptors (features) are used to achieve the recognition task method using Neural Network and Support Vector Machine. Several simulations are provided to validate the performances of the proposed method for the automatic aircraft target recognition.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125848814","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
A survey on deep learning techniques used for breast cancer detection 用于乳腺癌检测的深度学习技术调查
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231684
Bochra Jaafar, H. Mahersia, Z. Lachiri
{"title":"A survey on deep learning techniques used for breast cancer detection","authors":"Bochra Jaafar, H. Mahersia, Z. Lachiri","doi":"10.1109/ATSIP49331.2020.9231684","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231684","url":null,"abstract":"Breast cancer represents the highest percentage of cancers that affect women with 450000 deaths each year in the world. In Tunisia, it represents 30% of cancers diagnosed in women, thus occupying the first place in front of that of the cervix. In fact, it is important to identify breast cancer at an initial phase to decrease the death rate. In mammograms, the automatic mass recognition and classification remains a significant challenge and plays a critical role in helping radiologists to make a precise diagnosis. Recent improvements in the analysis of biomedical images using neural networks based on deep learning can be utilized to improve the CAD systems (computer-assisted diagnostic) performance. This paper presents the main deep learning approaches used for mammographic images, which can help us to identify research problems in current studies.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126027390","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
Recognizing signs and doors for Indoor Wayfinding for Blind and Visually Impaired Persons 识别盲人和视障人士室内寻路标志和门
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231933
Mouna Afif, R. Ayachi, Yahia Said, E. Pissaloux, Mohamed Atri
{"title":"Recognizing signs and doors for Indoor Wayfinding for Blind and Visually Impaired Persons","authors":"Mouna Afif, R. Ayachi, Yahia Said, E. Pissaloux, Mohamed Atri","doi":"10.1109/ATSIP49331.2020.9231933","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231933","url":null,"abstract":"Indoor signage plays an essential component to find destination for blind and visually impaired people. In this paper, we propose an indoor signage and doors detection system in order to help blind and partially sighted persons accessing unfamiliar indoor environments. Our indoor signage and doors recognizer is builded based on deep learning algorithms. We developed an indoor signage detection system especially used for detecting four types of signage: exit, wc, disabled exit and confidence zone. Experiment results demonstrates the effectiveness and the high precision of the proposed recognition system. We obtained 99.8% as a recognition rate.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126779690","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: 8
Analysis of Coding and Transfer of Arien Video Sequences from H.264 Standard H.264标准Arien视频序列的编码与传输分析
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231819
D. Ammous, A. Kallel, F. Kammoun, N. Masmoudi
{"title":"Analysis of Coding and Transfer of Arien Video Sequences from H.264 Standard","authors":"D. Ammous, A. Kallel, F. Kammoun, N. Masmoudi","doi":"10.1109/ATSIP49331.2020.9231819","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231819","url":null,"abstract":"Nowday, UAV is new technologie among the next generation monitoring system which has the following properties: efficient, real time, secure and reliable. UAV occupied a great importance in many fileds especially national defense in order to protect the human. In fact, the size of transferred data is a basic demand for realtime video communication, partically for military service since the bandwidth limitation in UAV networks.Exprimental results show that the H264 standard outperforms MPEG 4 (Motion Picture Expert Group) encoder in termes of PSNR (Peak signal to noise ratio). The H264’PSNR gain is 6.88% better than MPEG 4. In addition, the H264 standard did not exceed 20 Mbits for wireless video transmission.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124992629","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
Soil Moisture Estimation at 500m using Sentinel-1: application to African sites 利用Sentinel-1估算500米的土壤湿度:在非洲站点的应用
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231733
Myriam Foucras, M. Zribi, A. Kallel
{"title":"Soil Moisture Estimation at 500m using Sentinel-1: application to African sites","authors":"Myriam Foucras, M. Zribi, A. Kallel","doi":"10.1109/ATSIP49331.2020.9231733","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231733","url":null,"abstract":"This paper proposes a change detection approach for the estimation of soil water content, at a spatial resolution of 0.5 km and a temporal resolution of 6 days. The algorithm proposes a soil moisture index between 0 and 1. 0 corresponds to the driest context, 1 corresponds to the wettest context. The approach is being tested on different study sites with sentinel-1 radar data. Unlike the classic change detection approach, the algorithm takes into account the effects of land use, vegetation development and the seasonal context. Results show a good correlation between satellite estimations and true measurements for the African studied regions.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132735341","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
Real time emotion recognition in video stream, using B-CNN and F-CNN 视频流中的实时情感识别，使用B-CNN和F-CNN
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231902
R. Guetari, A. Chetouani, Hedi Tabia, Nawrès Khlifa
{"title":"Real time emotion recognition in video stream, using B-CNN and F-CNN","authors":"R. Guetari, A. Chetouani, Hedi Tabia, Nawrès Khlifa","doi":"10.1109/ATSIP49331.2020.9231902","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231902","url":null,"abstract":"Despite the diversity of methods developed in recent years, the implementation of an efficient system for automatic recognition of facial emotions remains a technological challenge that has not been fully resolved. Many problems have not yet been resolved. The occlusion problem remains a challenge today for the research community, certain features characterizing several different emotions may seem similar, etc. High performing and precise techniques are therefore necessary to perfectly distinguish between two different emotions, even though they might be difficult to distinguish. The objective of this work is the development of an automatic method for recognizing basic facial emotions (joy, anger, sadness, disgust, surprise, fear and neutral) in video streams. The method of deep learning, known for its great performance in image classification, becomes essential. In order to be able to benefit from several feature maps at the same time, we propose to use two techniques: bilinear pooling (B-CNN), and Fusion Feature Net (F-CNN). This technique is more efficient and more precise than conventional techniques, whether based on deep learning or not.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132761087","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
The Secured AES designs against Fault Injection Attacks: A comparative Study 针对故障注入攻击的安全AES设计:比较研究
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231942
N. Benhadjyoussef, Mouna Karmani, M. Machhout
{"title":"The Secured AES designs against Fault Injection Attacks: A comparative Study","authors":"N. Benhadjyoussef, Mouna Karmani, M. Machhout","doi":"10.1109/ATSIP49331.2020.9231942","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231942","url":null,"abstract":"To protect the Advanced Encryption Standard (AES) against physical attacks known as fault injection attacks various fault detection schemes have been proposed. In this paper, a comparative study between the most well-known fault detection schemes in terms of fault detection capabilities and implementation cost has been proposed. In the considered study we implement, separately, the hardware, the temporal and the information redundancy for the 32-bit AES. These schemes are implemented on the Virtex-5 Xilinx FPGA board in order to evaluate their efficiency in terms of area, time costs and fault coverage","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"14 1-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134294638","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
fMRI Imaging Based Human Brain Parcellation Methods: A review 基于fMRI成像的人脑分割方法综述
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231946
Fatma Abdedayem, F. Kallel, Marwa Chaabane, A. Hamida, Lamia Sellami
{"title":"fMRI Imaging Based Human Brain Parcellation Methods: A review","authors":"Fatma Abdedayem, F. Kallel, Marwa Chaabane, A. Hamida, Lamia Sellami","doi":"10.1109/ATSIP49331.2020.9231946","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231946","url":null,"abstract":"In the context of functional magnetic resonance imaging (fMRI) images, human brain parcellation is a very critical issue for human brain network generation, analysis and functional connectivity researches. Thus, brain organization has been defined with specific topographies at different scales where brain is dividing into different areas or regions which are interconnecting closely between them and each one is represented by a node with specific local properties.Typically, a huge number of recent parcellation studies prove the important role of fMRI based approaches to parcelate the brain into different interest regions by using several distinct generated Atlases.In this paper, we present a comparison between different Atlases that represent the map of parcellation. More specifically, we study the initial steps which are applied later with graph theories that has been used in a huge number of previous works in order to detect and describe neurodegenerative diseases. In our work, we have started by calculating the mean correlation matrix using distinct Atlases. We have chosen popular Atlas such as probabilistic Atlas, YEO Atlas, Power Atlas, Crad Atlas, Fair Atlas and Dos Atlas.Our experimental results are based on testing fMRI images taken from the famous database for Alzheimer named ADNI.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133102883","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
Generation of fuzzy evidence numbers for the evaluation of uncertainty measures 不确定性测度评价的模糊证据数的生成
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231757
Sami Barhoumi, I. Kallel, Sonda Ammar Bouhamed, É. Bossé, B. Solaiman
{"title":"Generation of fuzzy evidence numbers for the evaluation of uncertainty measures","authors":"Sami Barhoumi, I. Kallel, Sonda Ammar Bouhamed, É. Bossé, B. Solaiman","doi":"10.1109/ATSIP49331.2020.9231757","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231757","url":null,"abstract":"Uncertainty is an important dimension to consider to evaluate the quality of information. In real world, information tends, usually, to be uncertain, vague and imprecise leading to different types of uncertainty, such as randomness, ambiguity and imprecision. Methods to quantify uncertainty, will help to quantify information quality. This paper presents a general measure of uncertainty framed into the fuzzy evidence theory named GM, quantifying in an aggregate way the three basic types of uncertainty: non-specificity, fuzziness and discord considered within the framework of Generalized Information Theory (GIT). Monte-Carlo simulations are used to study the behavior of GM with respect to the up-cited uncertainty types. Results show that the total uncertainty GM behave properly as we increase and decrease the various types of uncertainty.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114239178","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
The Effect of 3d-Mri Modalities Mixture in Glioma Delimitation 3d-Mri混合模式对胶质瘤划界的影响
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231656
Hana Bouchouicha, O. B. Sassi, A. Hamida, C. Mhiri, M. Dammak, K. B. Mahfoudh
{"title":"The Effect of 3d-Mri Modalities Mixture in Glioma Delimitation","authors":"Hana Bouchouicha, O. B. Sassi, A. Hamida, C. Mhiri, M. Dammak, K. B. Mahfoudh","doi":"10.1109/ATSIP49331.2020.9231656","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231656","url":null,"abstract":"today, image processing has become a very important issue in medical imaging field, which is constantly developing to facilitate the diagnosis of several diseases such as brain tumors, especially glioblastoma (GBM). The segmentation of glioblastoma tumors is an important early step in image analysis to characterize the tumor phenotypic features. This study describes a new approach for the detection and the delimitation of GBM using modalities mixture as a pre-processing step then Otsu multilevel thresholding and Neighborhood algorithm & maximum component. This proposed modalities mixture used three different MRI modalities which are Flair, T2 and T1. This approach has been tested on clinical database BRATS’2017. We report promising results. The Dice Similarity Coefficient metric for whole tumor was 0.88. the preprocessing step used increases the segmentation accuracy compared to the same technique without modalities mixture.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114826808","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
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