Proceedings ELMAR-2014最新文献_第5页

Embeded fusion of visual and acoustic for active acoustic source detection with SGGMM 基于SGGMM的视声融合主动声源检测

Proceedings ELMAR-2014 Pub Date : 2014-10-16 DOI: 10.1109/ELMAR.2014.6923352

Riad Azzam, N. Aouf

引用次数: 1

A trinocular omnidirectional stereo vision system for high-precision RGB-D acquisition 用于高精度RGB-D采集的三目全向立体视觉系统

Proceedings ELMAR-2014 Pub Date : 2014-10-16 DOI: 10.1109/ELMAR.2014.6923350

M. Findeisen, L. Meinel, G. Hirtz

引用次数: 3

Virtual wireless penetration testing laboratory model 虚拟无线渗透测试实验室模型

Proceedings ELMAR-2014 Pub Date : 2014-10-16 DOI: 10.1109/ELMAR.2014.6923370

K. Skracic, J. Petrović, P. Pale, D. Tralic

引用次数: 2

The impact of internet use on education process 互联网使用对教育过程的影响

Proceedings ELMAR-2014 Pub Date : 2014-10-16 DOI: 10.1109/ELMAR.2014.6923368

Anita Prelas Kovačević, M. Spoljaric, D. Vuk

引用次数: 1

A novel approach for image steganalysis 一种新的图像隐写分析方法

Proceedings ELMAR-2014 Pub Date : 2014-10-16 DOI: 10.1109/ELMAR.2014.6923344

G. Bugár, V. Bánoci, M. Broda, D. Levický

引用次数: 2

Submarine optical cables: Impact on the marine environment 海底光缆:对海洋环境的影响

Proceedings ELMAR-2014 Pub Date : 2014-10-16 DOI: 10.1109/ELMAR.2014.6923320

I. Jurdana, R. Ivce, Darko Glažar

{"title":"Submarine optical cables: Impact on the marine environment","authors":"I. Jurdana, R. Ivce, Darko Glažar","doi":"10.1109/ELMAR.2014.6923320","DOIUrl":"https://doi.org/10.1109/ELMAR.2014.6923320","url":null,"abstract":"The submarine optical cables are one of the main parts of national and international telecommunication networks. Nowadays, the growing demand for bandwidth and transmission of vast amount of data on high bit rates causes expansion of terrestrial and submarine telecommunication network. The submarine cable network is an infrastructure which is vital to the global society. Therefore, coordinated efforts between cable industry, telecom operators and governments are required to ensure maximum protection and reliability of the networks. This paper presents submarine optical cables deployment, different types of cable configuration, installation, and protection. Despite the small physical size of optical cable activities, such as surveying of cable routes, laying, protecting and repairing submarine cables may cause pollution or harmful changes to the marine environment. The paper describes environmental effects of cable laying and maintenance. Because these activities mostly take place in coastal and territorial sea areas, they are subject to the domestic law and regulations of coastal countries. This paper gives a brief overview of the international law and conventions relating submarine cables. Disturbance and impact caused by cable laying are viewed in the context of improving preservation of the coast and seabed. Presented topic highlight the need to use and protect marine environment sustainably.","PeriodicalId":424325,"journal":{"name":"Proceedings ELMAR-2014","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128815655","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

Edge-preserving partial variable median filtering for fast noise reduction in CT slices 保持边缘的部分变量中值滤波在CT切片中的快速降噪

Proceedings ELMAR-2014 Pub Date : 2014-10-16 DOI: 10.1109/ELMAR.2014.6923349

T. Galba, Krešimir Romić, A. Baumgartner

引用次数: 2

Feature extraction and classification by machine learning methods for biometric recognition of face and iris 基于机器学习方法的人脸和虹膜生物特征识别特征提取与分类

Proceedings ELMAR-2014 Pub Date : 2014-10-16 DOI: 10.1109/ELMAR.2014.6923301

M. Oravec

{"title":"Feature extraction and classification by machine learning methods for biometric recognition of face and iris","authors":"M. Oravec","doi":"10.1109/ELMAR.2014.6923301","DOIUrl":"https://doi.org/10.1109/ELMAR.2014.6923301","url":null,"abstract":"Biometric recognition became an integral part of our living. This paper deals with machine learning methods for recognition of humans based on face and iris biometrics. The main intention of machine learning area is to reach a state when machines (computers) are able to respond without humans explicitly programming them. This area is closely related to artificial intelligence, knowledge discovery, data mining and neurocomputing. We present relevant machine learning methods with main focus on neural networks. Some aspects of theory of neural networks are addressed such as visualization of processes in neural networks, internal representations of input data as a basis for new feature extraction methods and their applications to image compression and classification. Machine learning methods can be efficiently used for feature extraction and classification and therefore are directly applicable to biometric systems. Biometrics deals with the recognition of people based on physiological and behavioral characteristics. Biometric recognition uses automated methods for recognition and this is why it is closely related to machine learning. Face recognition is discussed in this presentation - it covers the aspects of face detection, detection of facial features, classification in face recognition systems, state-of-the-art in biometric face recognition, face recognition in controlled and uncontrolled conditions and single-sample problem in face recognition. Iris recognition is analyzed from the point of view of state-of-the art in iris recognition, 2D Gabor wavelets, use of convolution kernels and possibilities for the design of new kernels. Software and hardware implementations of face and iris recognition systems are discussed and an implementation of a multimodal interface (face and iris part of a system) is presented. Also a contribution of Machine Learning Group working at FEI SUT Bratislava (http://www.uim.elf.stuba.sk/kaivt/MLgroup) to this research area is shown.","PeriodicalId":424325,"journal":{"name":"Proceedings ELMAR-2014","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122218299","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: 31

Foreground segmentation based on thermo-visible fusion 基于热可见光融合的前景分割

Proceedings ELMAR-2014 Pub Date : 2014-10-16 DOI: 10.1109/ELMAR.2014.6923326

Tarek Mouats, N. Aouf

引用次数: 0

Epicardial fat registration by local adaptive morphology-thresholding based 2D segmentation 基于局部自适应形态阈值的二维分割心外膜脂肪配准

Proceedings ELMAR-2014 Pub Date : 2014-10-16 DOI: 10.1109/ELMAR.2014.6923347

Vladimir Zlokolica, L. Velicki, M. Janev, David Mitrinović, D. Babin, N. Ralević, N. Cemerlić-Adić, R. Obradović, I. Galić

{"title":"Epicardial fat registration by local adaptive morphology-thresholding based 2D segmentation","authors":"Vladimir Zlokolica, L. Velicki, M. Janev, David Mitrinović, D. Babin, N. Ralević, N. Cemerlić-Adić, R. Obradović, I. Galić","doi":"10.1109/ELMAR.2014.6923347","DOIUrl":"https://doi.org/10.1109/ELMAR.2014.6923347","url":null,"abstract":"3D heart registration has become an important issue in cardio vascular diagnosis and treatment. This is mainly due to advanced medical imaging technologies that provide significant amount of data with high precision. One of the important features of the heart that has recently drawn attention is epicardial fat (surrounds the heart), which according to some preliminary studies can be correlated well with risk prediction of various cardiovascular diseases. Consequently, automatic detection and registration of epicardial fat is considered as important task for medical doctors to include as additional feature within the already existing software for medical imaging and visualization. In this paper, we analyze heart images obtained by 4D CT technology and propose a segmentation scheme that automatically extracts epcardial fat in each 2D slice in order to perform 3D epicardial fat registration and visualization. The segmentation algorithm first enhances input image after which it performs patch based labeling and clustering of the selected features. The experimental results indicate good epicardial fat registration performance in comparison to manual segmentation obtained by the medical doctors.","PeriodicalId":424325,"journal":{"name":"Proceedings ELMAR-2014","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114384457","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