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Multi-Level Bayesian Models for Environment Perception

Multi-Level Bayesian Models for Environment Perception


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About the Book

Introduction.- Fundamentals. - Bayesian models for Dynamic Scene Analysis.- Multi-layer label fusion models.- Multitemporal data analysis with Marked Point Processes. - Multi-level object population analysis with an EMPP model.- Concluding Remarks.- References.- Index.


About the Author: Dr. Csaba Benedek is a scientific advisor with the Machine Perception Research Laboratory at the Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH) in Budapest, Hungary, and a professor with the Faculty of Information Technology and Bionics of the Péter Pázmány Catholic University (PPCU). He obtained his PhD from PPCU in 2008, and his DSc from the Hungarian of Academy of Sciences (HAS) in 2020. Dr. Benedek has been the president of the Hungarian Image Processing and Pattern Recognition Society (Képaf), and the Hungarian Governing Board Member of the International Association for Pattern Recognition (IAPR). He has been a Senior Member of the IEEE, an Associate Editor of the journal Digital Signal Processing (Elsevier) and a Guest Editor of Remote Sensing (MDPI). His awards include the Bolyai plaquette from HAS (2019), a Researcher Acknowledgement from the HAS Secretary-General (2018), the Imreh Csanád plaquette (2019), and the Michelberger Master Award from the Hungarian Academy of Engineering (2020). In recent years, he has managed various national and international research projects. His research interests include Bayesian image and point cloud segmentation, object extraction, change detection, machine learning applications and GIS data analysis.


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Product Details
  • ISBN-13: 9783030836535
  • Publisher: Springer International Publishing
  • Publisher Imprint: Springer
  • Height: 234 mm
  • No of Pages: 218
  • Spine Width: 14 mm
  • Width: 156 mm
  • ISBN-10: 3030836533
  • Publisher Date: 13 Dec 2021
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Weight: 535 gr


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