Multi-level Bayesian models for environment perception / / Csaba Benedek

• Autore: Benedek Csaba
• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (208 pages)
• Disciplina: 006.4
• Soggetto topico: Markov processes; Bayesian statistical decision theory; Reconeixement de formes (Informàtica); Visió per ordinador; Models matemàtics; Processos de Markov; Estadística bayesiana
• Soggetto genere / forma: Llibres electrònics
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- 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 -- 2.3 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 -- 3.2.6 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 -- 3.4.3 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 -- 4.3 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 -- 5.2.4 Experimental Study of the mMPP Model -- 5.3 A Point Process Model for Target Sequence Analysis -- 5.3.1 Application on Moving Target Analysis in ISAR Image Sequences -- 5.3.2 Problem Definition and Notations -- 5.3.3 Data Preprocessing in a Bottom-Up Approach -- 5.3.4 Multiframe Marked Point Process Model -- 5.3.5 Multiframe MPP Optimization -- 5.3.6 Experimental Results on Target Sequence Analysis -- 5.4 Parameter Settings in Dynamic MPP Models -- 5.5 Conclusions -- 6 Multi-level Object Population Analysis with an Embedded MPP Model -- 6.1 A Hierarchical MPP Approach -- 6.2 Problem Formulation and Notations -- 6.3 EMPP Energy Model -- 6.4 Multi-level MPP Optimization -- 6.5 Applications of the EMPP Model -- 6.5.1 Built-in Area Analysis in Aerial and Satellite Images -- 6.5.2 Traffic Monitoring-Based on Lidar Data. ; 6.5.3 Automatic Optical Inspection of Printed Circuit Boards -- 6.6 Implementation Details -- 6.7 Quantitative Evaluation Framework -- 6.7.1 EMPP Benchmark Database -- 6.7.2 Quantitative Evaluation Methodology -- 6.8 Experimental Results -- 6.8.1 EMPP Versus an Ensemble of Single Layer MPPs -- 6.8.2 Application Level Comparison to Non-MPP-Based Techniques -- 6.8.3 Effects on Data Term Parameter Settings -- 6.8.4 Computational Time -- 6.8.5 Experiment Repeatability -- 6.9 Conclusion -- 7 Concluding Remarks -- Appendix References -- -- Index.
• Titolo autorizzato: Multi-Level Bayesian Models for Environment Perception
• ISBN: 9783030836542; 9783030836535
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996472038103316
• Lo trovi qui: Univ. di Salerno