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001446048 001__ 1446048
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001446048 019__ $$a1311953219$$a1312172433
001446048 020__ $$a9783030836542$$q(electronic bk.)
001446048 020__ $$a3030836541$$q(electronic bk.)
001446048 020__ $$z3030836533
001446048 020__ $$z9783030836535
001446048 0247_ $$a10.1007/978-3-030-83654-2$$2doi
001446048 035__ $$aSP(OCoLC)1311570301
001446048 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dOCLCF$$dSFB$$dUKAHL$$dOCLCQ
001446048 049__ $$aISEA
001446048 050_4 $$aTK7882.P3
001446048 08204 $$a006.4$$223/eng/20220503
001446048 1001_ $$aBenedek, Csaba,$$eauthor.
001446048 24510 $$aMulti-level Bayesian models for environment perception /$$cCsaba Benedek.
001446048 260__ $$aCham, Switzerland :$$bSpringer,$$c2022.
001446048 300__ $$a1 online resource
001446048 504__ $$aIncludes bibliographical references and index.
001446048 5050_ $$aIntro -- Acknowledgements -- Contents -- Acronyms and Notations -- Abbreviations and Concepts -- General Notations Used in the Book -- Specific Notations Used in MRF/CXM Models -- Specific Notations Used in MPP Models -- 1 Introduction -- 2 Fundamentals -- 2.1 Measurement Representation and Problem Formulations -- 2.2 Markovian Classification Models -- 2.2.1 Markov Random Fields, Gibbs Potentials, and Observation Processes -- 2.2.2 Bayesian Labeling Approach and the Potts Model -- 2.2.3 MRF-Based Image Segmentation -- 2.2.4 MRF Optimization -- 2.2.5 Mixed Markov Models
001446048 5058_ $$a2.3 Object Population Extraction with Marked Point Processes -- 2.3.1 Definition of Marked Point Processes -- 2.3.2 MPP Energy Functions -- 2.3.3 MPP Optimization -- 2.4 Methodological Contributions of the Book -- 3 Bayesian Models for Dynamic Scene Analysis -- 3.1 Dynamic Scene Perception -- 3.2 Foreground Extraction in Video Sequences -- 3.2.1 Related Work in Video-Based Foreground Detection -- 3.2.2 MRF Model for Foreground Extraction -- 3.2.3 Probabilistic Model of the Background and Shadow Processes -- 3.2.4 Microstructural Features -- 3.2.5 Foreground Probabilities
001446048 5058_ $$a3.2.6 Parameter Settings -- 3.2.7 MRF Optimization -- 3.2.8 Results -- 3.2.9 Summary and Applications of Foreground Segmentation -- 3.3 People Localization in Multi-camera Systems -- 3.3.1 A New Approach on Multi-view People Localization -- 3.3.2 Silhouette-Based Feature Extraction -- 3.3.3 3D Marked Point Process Model -- 3.3.4 Evaluation of Multi-camera People Localization -- 3.3.5 Applications and Alternative Ways of 3D Person Localization -- 3.4 Foreground Extraction in Lidar Point Cloud Sequences -- 3.4.1 Problem Formulation and Data Mapping -- 3.4.2 Background Model
001446048 5058_ $$a3.4.3 DMRF Approach on Foreground Segmentation -- 3.4.4 Evaluation of DMRF-Based Foreground-Background Separation -- 3.4.5 Application of the DMFR Method for Person and Activity Recognition -- 3.5 Conclusions -- 4 Multi-layer Label Fusion Models -- 4.1 Markovian Fusion Models in Computer Vision -- 4.2 A Label Fusion Model for Object Motion Detection -- 4.2.1 2D Image Registration -- 4.2.2 Change Detection with 3D Approach -- 4.2.3 Feature Selection -- 4.2.4 Multi-layer Segmentation Model -- 4.2.5 L3Mrf Optimization -- 4.2.6 Experiments on Object Motion Detection
001446048 5058_ $$a4.3 Long-Term Change Detection in Aerial Photos -- 4.3.1 Image Model and Feature Extraction -- 4.3.2 A Conditional Mixed Markov Image Segmentation Model -- 4.3.3 Experiments on Long-Term Change Detection -- 4.4 Parameter Settings in Multi-layer Segmentation Models -- 4.5 Conclusions -- 5 Multitemporal Data Analysis with Marked Point Processes -- 5.1 Introducing the Time Dimension in MPP Models -- 5.2 Object-Level Change Detection -- 5.2.1 Building Development Monitoring-Problem Definition -- 5.2.2 Feature Selection -- 5.2.3 Multitemporal MPP Configuration Model and Optimization
001446048 506__ $$aAccess limited to authorized users.
001446048 520__ $$aThis book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed static or dynamic environment. To ensure a sound theoretical basis of the new models, the surveys and algorithmic developments are performed in well-established Bayesian frameworks. Low level scene understanding functions are formulated as various image segmentation problems, where the advantages of probabilistic inference techniques such as Markov Random Fields (MRF) or Mixed Markov Models are considered. For the object level scene analysis, the book mainly relies on the literature of Marked Point Process (MPP) approaches, which consider strong geometric and prior interaction constraints in object population modeling. In particular, key developments are introduced in the spatial hierarchical decomposition of the observed scenarios, and in the temporal extension of complex MRF and MPP models. Apart from utilizing conventional optical sensors, case studies are provided on passive radar (ISAR) and Lidar-based Bayesian environment perception tasks. It is shown, via several experiments, that the proposed contributions embedded into a strict mathematical toolkit can significantly improve the results in real world 2D/3D test images and videos, for applications in video surveillance, smart city monitoring, autonomous driving, remote sensing, and optical industrial inspection.
001446048 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 3, 2022).
001446048 650_0 $$aPattern recognition systems$$xMathematical models.
001446048 650_0 $$aComputer vision$$xMathematical models.
001446048 650_0 $$aMarkov processes.
001446048 650_0 $$aBayesian statistical decision theory.
001446048 650_6 $$aReconnaissance des formes (Informatique)$$xModèles mathématiques.
001446048 650_6 $$aVision par ordinateur$$xModèles mathématiques.
001446048 650_6 $$aProcessus de Markov.
001446048 650_6 $$aThéorie de la décision bayésienne.
001446048 655_7 $$aLlibres electrònics.$$2thub
001446048 655_0 $$aElectronic books.
001446048 77608 $$iPrint version: $$z3030836533$$z9783030836535$$w(OCoLC)1259585903
001446048 852__ $$bebk
001446048 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-83654-2$$zOnline Access$$91397441.1
001446048 909CO $$ooai:library.usi.edu:1446048$$pGLOBAL_SET
001446048 980__ $$aBIB
001446048 980__ $$aEBOOK
001446048 982__ $$aEbook
001446048 983__ $$aOnline
001446048 994__ $$a92$$bISE