LEADER 04145nam  22007215  450 
001 9910564689303321
005 20250504232140.0
010   $a9783030836542$b(electronic bk.)
010   $z9783030836535
024 7 $a10.1007/978-3-030-83654-2
035   $a(MiAaPQ)EBC6954931
035   $a(Au-PeEL)EBL6954931
035   $a(CKB)21536419600041
035   $a(PPN)262171856
035   $a(DE-He213)978-3-030-83654-2
035   $a(EXLCZ)9921536419600041
100   $a20220418d2022      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMulti-Level Bayesian Models for Environment Perception /$fby Csaba Benedek
205   $a1st ed. 2022.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022.
215   $a1 online resource (208 pages)
311 08$aPrint version: Benedek, Csaba Multi-Level Bayesian Models for Environment Perception Cham : Springer International Publishing AG,c2022 9783030836535 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- 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.
330   $aThis book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed static or dynamic environment. To ensure a sound theoretical basis of the new models, the surveys and algorithmic developments are performed in well-established Bayesian frameworks. Low level scene understanding functions are formulated as various image segmentation problems, where the advantages of probabilistic inference techniques such as Markov Random Fields (MRF) or Mixed Markov Models are considered. For the object level scene analysis, the book mainly relies on the literature of Marked Point Process (MPP) approaches, which consider strong geometric and prior interaction constraints in object population modeling. In particular, key developments are introduced in the spatial hierarchical decomposition of the observed scenarios, and in the temporal extension of complex MRF and MPP models. Apart from utilizing conventional optical sensors, case studies are provided on passive radar (ISAR) and Lidar-based Bayesian environment perception tasks. It is shown, via several experiments, that the proposed contributions embedded into a strict mathematical toolkit can significantly improve the results in real world 2D/3D test images and videos, for applications in video surveillance, smart city monitoring, autonomous driving, remote sensing, and optical industrial inspection.
606   $aStatistics
606   $aComputer vision
606   $aStochastic processes
606   $aMarkov processes
606   $aStatistics
606   $aGeographic information systems
606   $aBayesian Inference
606   $aComputer Vision
606   $aStochastic Processes
606   $aMarkov Process
606   $aStatistical Theory and Methods
606   $aGeographical Information System
615  0$aStatistics.
615  0$aComputer vision.
615  0$aStochastic processes.
615  0$aMarkov processes.
615  0$aStatistics.
615  0$aGeographic information systems.
615 14$aBayesian Inference.
615 24$aComputer Vision.
615 24$aStochastic Processes.
615 24$aMarkov Process.
615 24$aStatistical Theory and Methods.
615 24$aGeographical Information System.
676   $a006.4
676   $a006.37
700   $aBenedek$b Csaba$01222945
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910564689303321
996   $aMulti-Level Bayesian Models for Environment Perception$92836951
997   $aUNINA