2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)最新文献_第6页

Sub-pixel Mapping Method based on Total Variation Minimization and Spectral Dictionary 基于总变差最小化和光谱字典的亚像素映射方法

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231682

Bouthayna Msellmi, Daniele Picone, Zouhaier Ben Rabah, M. Mura, I. Farah

{"title":"Sub-pixel Mapping Method based on Total Variation Minimization and Spectral Dictionary","authors":"Bouthayna Msellmi, Daniele Picone, Zouhaier Ben Rabah, M. Mura, I. Farah","doi":"10.1109/ATSIP49331.2020.9231682","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231682","url":null,"abstract":"In this paper we tackle the problem of data analysis over the higher dimensional space provided by hyperspectral images. Remote sensing data analysis is a complex task due to numerous factors such as the large spectral and spatial diversity. The latter is the key focus of attention in this paper.As a matter of fact, mixed pixels are often sources of uncertainty which affects the accuracy of several approaches whose target is to solve the sub-pixel problem. Although spectral un-mixing techniques can provide abundance fractions within mixed pixels to each class, their associated spatial distribution remains unknown. The set of techniques aimed to solve the above mentioned problem is commonly known as sub-pixel mapping (SPM); existing algorithms based on the spatial dependence assumption cannot solve these problems efficiently and cannot provide a unique configuration for the same problem. In the context of variational framework to solve inverse problems, various strategies were proposed to avoid their intrinsic ill-posedness in the form of regularization. Differently from previous approaches of literature, which apply spatial regularization individually for each class, the proposed method takes also into account spatial links among classes. In order to improve sub-pixel mapping accuracy and, consequently, enhance hyperspectral image classification, we propose a method based on a pre-constructed spectral dictionary and isotropic total variation minimization of classes within and between pixels (SMSD-ITV). Experimental results with real and simulated data sets show the attributes of using spectral dictionary with total variation as a prior model, which lead to improve sub-pixel mapping of different classes tacking into account spatial correlation between them.","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":"129819255","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

Robust GPS/Galileo/GLONASS Data Fusion Using Extended Kalman Filter 基于扩展卡尔曼滤波的GPS/Galileo/GLONASS数据融合

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231565

Tan Truong-Ngoc, A. Khenchaf, F. Comblet, Pierre Franck, Jean-Marc Champeyroux, O. Reichert

引用次数: 0

Possibilistic BRISK method for an efficient registration (PBRISK) 一种高效注册的可能性轻快方法(p轻快)

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231632

Wissal Ben Marzouka, B. Solaiman, A. Hammouda, Zouhour Ben Dhief, K. Bsaïes

{"title":"Possibilistic BRISK method for an efficient registration (PBRISK)","authors":"Wissal Ben Marzouka, B. Solaiman, A. Hammouda, Zouhour Ben Dhief, K. Bsaïes","doi":"10.1109/ATSIP49331.2020.9231632","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231632","url":null,"abstract":"This paper aims to present a possibilistic registration method using BRISK. BRISK method is a key point detector and descriptor. It is rotation and scale invariant, but it takes more time to detect the feature points and it suffers from the high number of outliers. The main idea of the proposed method is to apply the theory of possibilities for extracting primitives to obtaining an efficient registration. We explore the suitability of the BRISK method for the task of image registration by limiting the outlier’s number. The proposed method uses the semantic aspect of images for features detection as well as matching. This “semantic focussing process” allows reducing the quantity of information, as well as the noise effects during the matching process by the creation of a new space called “Semantic knowledge space” which contains a set of projections of images each presenting a single content called a “possibilistic maps The experiments as well as the comparative study carried out, using medical images, show the efficiency of the proposed method in terms of outliers’ reduction, noise robustness, time complexity and precision improved.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"40 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":"128177021","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

Quantification of left ventricular function in MRI: a review of current approaches MRI左心室功能的量化:当前方法的回顾

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231709

Wafa Baccouch, S. Oueslati, S. Labidi, Bassel Solaiman

{"title":"Quantification of left ventricular function in MRI: a review of current approaches","authors":"Wafa Baccouch, S. Oueslati, S. Labidi, Bassel Solaiman","doi":"10.1109/ATSIP49331.2020.9231709","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231709","url":null,"abstract":"Detecting and quantifying abnormalities in the movement of the heart walls such as hypokinesia, akinesia and dyskinesia and measuring their severity is a critical step in the assessment and treatment of ischemic and non-ischemic heart disease. These so-called contraction abnormalities are generally manifested by a decrease in the amplitude of the cardiac contraction reflecting hypokinesia and a complete absence of wall movement indicating akinesia. In case of dyskinesia, the wall is characterized by an abnormal movement, most often ventricular. In the non-pathological case, when the ventricle contracts in systole, it thickens and tends to approach the center of the cavity while in case of dyskinesia it tends to move away. In medical imaging, several methods for regional assessment of cardiac contractile function have been developed. The aim of this article is to review the most relevant approaches available in magnetic resonance imaging (MRI) such as parametric imaging, cardiac contour segmentation and deep learning. At the end of this study, we compared the previously mentioned approaches after explaining their principles, their advantages and disadvantages. The comparison showed that deep learning represents the most precise method in terms of segmentation and quantification of the contraction anomalies.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"42 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":"127453659","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

Performance Evaluation of Various Denoising Filters and segmentation methods for OCT images 各种OCT图像去噪滤波器和分割方法的性能评价

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231588

Abir Chaari, Khouloud Kammoun, I.F. Kallel, M. Frikha, S. Kammoun, J. Feki

引用次数: 0

Efficient Routing Protocol for Wireless Sensor Network based on Reinforcement Learning 基于强化学习的无线传感器网络高效路由协议

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231883

S. Bouzid, Y. Serrestou, K. Raoof, Mohamed Nazih Omri

引用次数: 9

Multi-Object tracking based on Kalman Filtering Combining Radar and Image Measurements 基于卡尔曼滤波的雷达与图像测量相结合的多目标跟踪

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231698

M. Tlig, M. Bouchouicha, M. Sayadi, E. Moreau

引用次数: 0

Unconstrained Face Verification Based on Monogenic Binary Pattern and Convolutional Neural Network 基于单基因二值模式和卷积神经网络的无约束人脸验证

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231764

Bilel Ameur, M. Belahcene, Sabeur Masmoudi, A. Hamida

{"title":"Unconstrained Face Verification Based on Monogenic Binary Pattern and Convolutional Neural Network","authors":"Bilel Ameur, M. Belahcene, Sabeur Masmoudi, A. Hamida","doi":"10.1109/ATSIP49331.2020.9231764","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231764","url":null,"abstract":"Unconstrained Face Verification is still an important problem worth researching. The major challenges such as illumination, pose, occlusion and expression can produce more complex variations in both shape and texture of the face. In this paper, we propose a method based on Monogenic Binary Pattern and Convolutional Neural Network (MBP-CNN) to improve the performance of face recognition system. For each facial image, the proposed method firstly extracts local features using Monogenic Binary Pattern (MBP) which is an excellent and powerful local descriptor compared to the well-recognized Gabor filtering-based LBP models. Then, we use Convolutional Neural Networks which is one of the best representative network architectures of deep learning in the literature, in order to extract more deep features. Thus, the developed MBP-CNN has robustness to variations of illumination, occlusion, pose, expression, texture and shape by combining Monogenic Binary Pattern and convolutional neural network. Moreover, MBP-CNN was more accurately represented by combining global and local information of facial images. Experiments demonstrate that our method provided competitive performance on the LFW database, compared to the others described in the state-of-the-art.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"21 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":"133554199","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

Early Diagnosis of Diabetic Retinopathy using Random Forest Algorithm 基于随机森林算法的糖尿病视网膜病变早期诊断

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231795

Nihel Zaaboub, A. Douik

引用次数: 3

High-level design of HEVC intra prediction algorithm HEVC帧内预测算法的高级设计

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231677

A. Atitallah, Manel Kammoun

引用次数: 0