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

Preprocessing Latent-Fingerprint Images For Improving Segmentation Using Morphological Snakes 基于形态学蛇的潜在指纹图像预处理改进分割

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231908

Hajer Walhazi, Lamia Rzouga Haddada, A. Maalej, N. Amara

{"title":"Preprocessing Latent-Fingerprint Images For Improving Segmentation Using Morphological Snakes","authors":"Hajer Walhazi, Lamia Rzouga Haddada, A. Maalej, N. Amara","doi":"10.1109/ATSIP49331.2020.9231908","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231908","url":null,"abstract":"Latent fingerprints have played a critical role in identifying criminals and suspects. However, latent fingerprint identification is more complicated than plain and rolled fingerprints mainly due to poor ridge quality, complex background noise and overlapped structured noise in latent images. Subsequently, a latent-fingerprint image requires to be segmented to extract the fingerprint region from the background. The paper proposes a novel and efficient technique for latent-fingerprint segmentation. Our approach is based mainly on two fundamental ideas: i) applying the conversion from RGB color model to YCBCR color model and the Gaussian blur technique as a preprocessing before segmentation, and ii) using morphologic active contours without edges to define the fingerprint region based on an evolving contour that starts its rapid evolution in a stable state from the inside fingerprint. The technique is tested on two fingerprint databases: FVC2004 and NIST SD27. Our experimental results evaluate the miss-classified pixels and yield high segmentation accuracy.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"25 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":"129619606","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

Vehicle detection for intelligent traffic surveillance system 车辆检测用于智能交通监控系统

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231936

N. Abid, T. Ouni, M. Abid

引用次数: 3

Glioblastomas brain Tumor Segmentation using Optimized U-Net based on Deep Fully Convolutional Networks (D-FCNs) 基于深度全卷积网络的优化U-Net脑胶质瘤分割

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231681

Hiba Mzoughi, Ines Njeh, M. Slima, A. Hamida

{"title":"Glioblastomas brain Tumor Segmentation using Optimized U-Net based on Deep Fully Convolutional Networks (D-FCNs)","authors":"Hiba Mzoughi, Ines Njeh, M. Slima, A. Hamida","doi":"10.1109/ATSIP49331.2020.9231681","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231681","url":null,"abstract":"Manual segmentation during clinical diagnosis, is considered as time-consuming and depend to the neuroradiologists level of expertise, however due to the large spatial and structural variability of brain tumors in shapes and sizes besides to the tumor sub-region voxels’high in-homogeneity could make a reliable and accurate and automated segmentation a challenging task. We proposed in this paper, an efficient and fully automatic deep-learning approach for Gliomas ‘brain tumor segmentation in multi-sequences Magnetic Resonance imaging (MRI). The proposed method is an optimization on the U-Net based on Fully Convolutional Networks (FCNs) called ‘U-Net DFCN’ in which we introduced the fusion of multiple MRI modalities to incorporate features from different scales, furthermore, to address the problem of data heterogeneity due to difference in acquisition algorithms and MRI scanner technologies, we proposed an intensity normalization followed by data augmentation techniques in the preprocessing step which though not conventional (usual) in deep FCN-based segmentation approaches. Our method was evaluated on the Multimodal Brain Tumor Image Segmentation (BRATS 2018) training and validation datasets, experimental resulted showed the good performance of the proposed approach outperforming several recent state-of-the-art segmentation methods, achieving a Dice score Coefficient (DSC) of 0.88, 0.87 and 0.81 for complete tumor, tumor-core and enhancing-tumor respectively.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"114 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":"115789607","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

Speech spoofing detection using SVM and ELM technique with acoustic features 基于声学特征的SVM和ELM技术的语音欺骗检测

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231799

Raoudha Rahmeni, A. B. Aicha, Y. B. Ayed

引用次数: 6

Semi-automatic segmentation of intervertebral disc for diagnosing herniation using axial view MRI 轴位MRI椎间盘半自动分割诊断腰椎间盘突出

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231737

W. Mbarki, M. Bouchouicha, Sébastien Frizzi, Frederick Tshibasu, L. Farhat, M. Sayadi

{"title":"Semi-automatic segmentation of intervertebral disc for diagnosing herniation using axial view MRI","authors":"W. Mbarki, M. Bouchouicha, Sébastien Frizzi, Frederick Tshibasu, L. Farhat, M. Sayadi","doi":"10.1109/ATSIP49331.2020.9231737","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231737","url":null,"abstract":"We consider the problem of lower back pain and sciatica due to the loss of the disc’s height and the displacement of vertebrae. Our spine represents a combination of discs and vertebrae; between each two vertebrae, we can find an intervertebral disc. We will be interested in this paper to the lumbar discs, which are the most responsible for the lumbar herniation. Computer Aided Diagnosing (CAD) system for localizing herniated and normal intervertebral discs is a difficult task due to the method for treatment. Magnetic Resonance Imaging (MRI) are widely used to diagnose lower back pain and sciatica. We will be concentrated in this work on the T2-axial view MRI to successfully detect and classify the intervertebral discs which are the most important tasks to discuss in a system CAD. The originality of this paper consists in the development of a new method based on active contour and intuitionistic fuzzy C means (IFS) techniques to localize and extract disc from axial view MRI in order to find the type of herniated lumbar disc as foraminal, median or post lateral, we achieved 0.86 dice similarity index on 185 T2 axial MRI.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"11 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":"134005962","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

A review of drought monitoring using remote sensing and data mining methods 基于遥感和数据挖掘方法的干旱监测综述

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231697

R. Inoubli, Ali Ben Abbes, I. Farah, V. Singh, T. Tadesse, M. Sattari

{"title":"A review of drought monitoring using remote sensing and data mining methods","authors":"R. Inoubli, Ali Ben Abbes, I. Farah, V. Singh, T. Tadesse, M. Sattari","doi":"10.1109/ATSIP49331.2020.9231697","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231697","url":null,"abstract":"Today, drought has become part of the identity as well as the fate of many countries. In fact, drought is considered among the most damaging natural disasters. The severe consequences resulting from drought affect the nature and society at different levels. Proper and efficient management is not possible without accurate prediction of drought and the identification of its various aspects. Thus, the existence of a considerable body of literature on drought monitoring. However, significant growth of remote sensing databases as will an increased amount of available data related to drought have been detected. Therefore, a more adequate approach should be developed. During the past decades, Data Mining (DM) methods have been introduced for drought monitoring. According to the best of our knowledge, a review of drought monitoring using remote sensing data and DM methods is lacking. Thereby, the purpose of this paper is to review and discuss the applications of DM methods. This paper consolidates the finding of drought monitoring, models, tasks, and methodologies.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"39 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":"131505555","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: 9

Lightweight Hardware Architectures for the Piccolo Block Cipher in FPGA FPGA中短笛分组密码的轻量级硬件架构

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231586

Ayoub Mhaouch, W. Elhamzi, Mohamed Atri

引用次数: 10

A new features vector matching for big heterogeneous data in intrusion detection context 一种新的入侵检测大异构数据特征向量匹配方法

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231671

Marwa Elayni, F. Jemili, O. Korbaa, B. Solaiman

引用次数: 0

Comparison Study for Spinal Cord Segmentation Methods aiming to detect SC Atrophy in MRI images: case of Multiple Sclerosis 多发性硬化症MRI脊髓分割方法的对比研究

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231790

Besma Mnassri, M. Sahnoun, A. Hamida

引用次数: 1

Contrast-Enhanced Image Analysis for MRI Based Multiple Sclerosis Lesion Segmentation 基于MRI增强图像分析的多发性硬化症病灶分割

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231858

M. Sahnoun, F. Kallel, M. Dammak, O. Kammoun, C. Mhiri, K. B. Mahfoudh, A. Hamida

引用次数: 0