TY - JOUR AU - Fried, Katalin AU - Fekete, István AU - Princz, Péter TI - Better understanding mathematics by algorithmic thinking and computer programming JF - TEACHING MATHEMATICS AND COMPUTER SCIENCE J2 - TEACH MATH COMP SCI VL - 18 PY - 2020 IS - 4 SP - 295 EP - 305 PG - 11 SN - 1589-7389 DO - 10.5485/TMCS.2020.0486 UR - https://m2.mtmt.hu/api/publication/31868340 ID - 31868340 N1 - 3in(EFOP-3.6.2-16-2017-00013) Támogató: EFOP Innovatív Informatikai és Infokommunikációs Megoldásokat Megalapozó Tematikus Kutatási Együttműködések LA - English DB - MTMT ER - TY - JOUR AU - Fekete, István AU - Gregorics, Tibor AU - Kovácsné Pusztai, Kinga Emese AU - Veszprémi, Anna TI - Programming Theorems and Their Applications JF - TEACHING MATHEMATICS AND COMPUTER SCIENCE J2 - TEACH MATH COMP SCI VL - 17 PY - 2020 IS - 2 SP - 213 EP - 241 PG - 29 SN - 1589-7389 DO - 10.5485/TMCS.2019.0466 UR - https://m2.mtmt.hu/api/publication/31272377 ID - 31272377 N1 - 3in(EFOP-3.6.2-16-2017-00013) Támogató: EFOP Innovatív Informatikai és Infokommunikációs Megoldásokat Megalapozó Tematikus Kutatási Együttműködések LA - English DB - MTMT ER - TY - JOUR AU - Kása, Zoltán AU - Fekete, István TI - Antal Iványi (1942–2017) JF - ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA J2 - ACTA UNIV SAP INFORM VL - 9 PY - 2017 SP - 5 EP - 16 PG - 12 SN - 1844-6086 UR - https://m2.mtmt.hu/api/publication/32718759 ID - 32718759 LA - Hungarian DB - MTMT ER - TY - BOOK AU - Fekete, István AU - Hunyadvári, László TI - Algoritmusok és adatszerkezetek ET - 0 PB - Digitális Tankönyvtár CY - Budapest PY - 2015 SN - 9789632484565 UR - https://m2.mtmt.hu/api/publication/3191829 ID - 3191829 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Giachetta, Roberto AU - Fekete, István TI - A Case Study of Advancing Remote Sensing Image Analysis JF - ACTA CYBERNETICA J2 - ACTA CYBERN-SZEGED VL - 22 PY - 2015 IS - 1 SP - 57 EP - 79 PG - 23 SN - 0324-721X DO - 10.14232/actacyb.22.1.2015.5 UR - https://m2.mtmt.hu/api/publication/2921814 ID - 2921814 AB - Big data and cloud computing are two phenomena, which have gained significant reputation over the last few years. In computer science the approach shifted towards distributed architectures and high performance computing. In case of geographical information systems (GIS) and remote sensing image analysis, the new paradigms have already been successfully applied to several problems, and systems have been developed to support processing of geographical and remote sensing data in the cloud. However, due to different circumstances many previous workflows have to be reconsidered and redesigned. Our goal is to show a way how the existing approaches to remote sensing image analysis can be advanced to take advantages of these new paradigms. The task aiming in shifting the algorithms shall require a moderate effort and must avoid the complete redesign and reimplementation of the existing approaches. We present the whole journey as a case study using an existing industrial workflow for demonstration. Nevertheless, we define the rules of thumb, which can come in hand when shifting any existing GIS workflows. Our case study is the workflow of waterlogging and flood detection, which is an operative task at the Institute of Geodesy, Cartography and Remote Sensing (FÖMI). This task in currently operational using a semi-automatic single machine approach involving multiple software. The workflow is neither efficient nor scalable, thus it is not applicable in emergency situations where quick response is required. We present an approach utilizing distributed computing, which enables the automated execution of this task on large input data with much better response time. The approach is based on the well-known MapReduce paradigm, its open-source implementation, the Apache Hadoop framework and the AEGIS geospatial toolkit. This enables the replacement of multiple software to a single, generic framework. Results show that significant performance benefits can be achieved at the expense of minor accuracy loss. LA - English DB - MTMT ER - TY - BOOK AU - Fekete, István AU - László, István TI - Távérzékelt felvételek elemzése ET - 0 PB - ELTE Informatikai Kar CY - Budapest PY - 2014 SP - 130 SN - 9789632844701 UR - https://m2.mtmt.hu/api/publication/3163236 ID - 3163236 LA - Hungarian DB - MTMT ER - TY - CHAP AU - Molnár, Bálint AU - Zsigmond, Máriás AU - Zoltán, Suhajda AU - Fekete, István ED - Orosz, Gábor Tamás TI - AMNIS - DESIGN AND IMPLEMENTATION OF AN ADAPTIVE WORKFLOW MANAGEMENT SYSTEM T2 - 9th International Symposium on Applied Informatics and Related Areas - AIS2014 PB - Óbudai Egyetem CY - Székesfehérvár SN - 9786155460210 PY - 2014 PG - 8 DO - 10.13140/2.1.2922.6565 UR - https://m2.mtmt.hu/api/publication/2791192 ID - 2791192 AB - The experiences of introduction and operation of ERP systems have revealed that update of these software due to the constantly changing business processes demand huge resources. That is why the demand was formulated for a method that enables introducing new features in software system without any modification in program code according to the evolution of the organization. The objective of Amnis development project is to create a system with this adaptation capability using the basic idea of workflows that create documents during evaluation. In this article design and programming challenges are shown that had to be met during the development of Amnis, focusing on topics of effective data storage and queries, workflow control structures and workflow evaluation techniques. LA - English DB - MTMT ER - TY - JOUR AU - Dezső, Balázs AU - Giachetta, Roberto AU - László, István AU - Fekete, István TI - Experimental study on graph-based image segmentation methods in the classification of satellite images JF - EARSEL EPROCEEDINGS J2 - EARSEL EPROC VL - 11 PY - 2012 SP - 12 EP - 24 PG - 13 SN - 1729-3782 UR - https://m2.mtmt.hu/api/publication/2061844 ID - 2061844 LA - English DB - MTMT ER - TY - JOUR AU - Dezső, Balázs AU - Fekete, István AU - Dávid, Ákos Gera AU - Giachetta, Roberto AU - László, István TI - Object-based image analysis in remote sensing applications using various segmentation techniques JF - ANNALES UNIVERSITATIS SCIENTIARUM BUDAPESTINENSIS DE ROLANDO EOTVOS NOMINATAE SECTIO COMPUTATORICA J2 - ANN UNIV SCI BP R EÖTVÖS NOM SECT COMPUT VL - 37 PY - 2012 SP - 103 EP - 120 PG - 18 SN - 0138-9491 UR - https://m2.mtmt.hu/api/publication/1945182 ID - 1945182 AB - At Eötvös Loránd University (ELTE), Faculty of Informatics extensive education, research and development activity is carried out in geoinformatics, in cooperation with Institute of Geodesy, Cartography and Remote Sensing (FÖMI). It includes the teaching of subject "Remote Sensing Image Analysis", research of segment-based classification of remote sensing images and its applications in operational projects. Investigation of segmentation methods is embedded into the classification problem. Segments are homogeneous areas of images, consisting of neighboring pixels. Segment membership of pixels conveys valuable geometric information to classification step. This article gives a summary on several merge-based and cut-based segmentation methods. The application of segmentation is not only an option, but a necessity in the processing of very high resolution images, as their pixels usually cannot be interpreted individually. Segments are assigned with several attributes (e.g. texture) derived from geometrical properties. This leads to the advanced approach called Object-based Image Analysis (OBIA). As an application, the task of delimiting tree groups and scattered trees in pastures will be presented in detail. Three further applications will also be shortly introduced. LA - English DB - MTMT ER - TY - CHAP AU - László, István AU - Ócsai, Katalin AU - Gera, Dávid AU - Giachetta, Roberto AU - Fekete, István ED - Halounová, L TI - Object-based image analysis of pasture with trees and red mud spill T2 - Remote Sensing and Geoinformation not only for Scientific Cooperation PB - European Association of Remote Sensing Laboratories (EARSeL) CY - Prague SN - 9788001048689 T3 - Proceedings of the EARSeLSymposium ; 31. PY - 2011 SP - 423 EP - 431 PG - 9 UR - https://m2.mtmt.hu/api/publication/2113530 ID - 2113530 AB - Abstract. This article shows the possibilities of object-based analysis of very high resolution satel-lite and aerial images in three applications from the areas of agriculture and disaster monitoring: the detection of scattered trees and bushes on pasture (eligibility issues in Land Parcel Identifica-tion System), the delineation of industrial red sludge spill and ragweed monitoring (mapping of ragweed spots in agricultural areas). To achieve proper results, we need to create image objects fit-ting land cover objects and classify them to predefined classes. The key step of object-based ap-proach is segmentation, that is, the division of image to contiguous sets of spectrally similar pixels. Beside implementing and examining different segmentation algorithms, the authors have used the Definiens / eCognition software in operational applications. The results achieved justify that the accuracy of object-based classification is comparable to pixel-based one, and the analysis of tex-tural and shape properties can further increase accuracy and appropriateness of procedures. Keywords. Segmentation, object-based image analysis, Land Parcel Identification System, toxic spill, ragweed monitoring LA - English DB - MTMT ER -