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[null,"1.","Record Nr.",null,"UNINA9910254116803321"]
[null,null,"Autore",null,"Vieux Baxter E"]
[null,null,"Titolo",null,"Distributed Hydrologic Modeling Using GIS / / by Baxter E. Vieux"]
[null,null,"Pubbl/distr/stampa",null,"Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2016"]
[null,null,"ISBN",null,"94-024-0930-0"]
[null,null,"Edizione",null,"[3rd ed. 2016.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVI, 262 p. 124 illus., 60 illus. in color.)"]
[null,null,"Collana",null,"Water Science and Technology Library, , 0921-092X ; ; 74"]
[null,null,"Disciplina",null,"551.480285"]
[null,null,"Soggetti",null,"Hydrogeology"]
[{"$nulls":4},"Hydrology"]
[{"$nulls":4},"Natural disasters"]
[{"$nulls":4},"Physical geography"]
[{"$nulls":4},"Hydrology/Water Resources"]
[{"$nulls":4},"Natural Hazards"]
[{"$nulls":4},"Physical Geography"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"1. Introduction to Physics-based Distributed Hydrology -- 2. Geospatial Data for Hydrology 3 Surface Generation -- 4. Spatial Variability -- 5. Infiltration -- 6. Hydrolic Roughness -- 7. Watersheds and Drainage Networks -- 8. Distributed Precipitation Estimation -- 9. Surface Runoff Model Formulation -- 10. Distributed Model Calibration -- 11. Case Studies in Distributed Hydrology -- 12. Vflo® – Software for Distributed Hydrology -- Index."]
[null,null,null,"Sommario/riassunto","This book presents a unified approach for modeling hydrologic processes distributed in space and time using geographic information systems (GIS). This Third Edition focuses on the principles of implementing a distributed model using geospatial data to simulate hydrologic processes in urban, rural and peri-urban watersheds. The author describe fully distributed representations of hydrologic processes, where physics is the basis for modeling, and geospatial data forms the cornerstone of parameter and process representation. A physics-based approach involves conservation laws that govern the movement of water, ranging from precipitation over a river basin to flow in a river. Global geospatial data have become readily available in GIS format, and a modeling approach that can utilize this data for hydrology offers numerous possibilities. GIS data formats, spatial "]
[{"$nulls":4},"interpolation and resolution have important effects on the hydrologic simulation of the major hydrologic components of a watershed, and the book provides examples illustrating how to represent a watershed with spatially distributed data along with the many pitfalls inherent in such an undertaking. Since the First and Second Editions, software development and applications have created a richer set of examples, and a deeper understanding of how to perform distributed hydrologic analysis and prediction. This Third Edition describes the development of geospatial data for use in Vflo® physics-based distributed modeling. ."]
[null,"2.","Record Nr.",null,"UNINA9910674347403321"]
[null,null,"Titolo",null,"Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging : Mathematical Imaging and Vision / / edited by Ke Chen, Carola-Bibiane Schönlieb, Xue-Cheng Tai, Laurent Younes"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"9783030986612"]
[{"$nulls":4},"3030986616"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (1981 pages)"]
[null,null,"Disciplina",null,"006.37"]
[null,null,"Soggetti",null,"Mathematics - Data processing"]
[{"$nulls":4},"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Mathematical optimization"]
[{"$nulls":4},"Mathematical analysis"]
[{"$nulls":4},"Neural networks (Computer science)"]
[{"$nulls":4},"Computational Mathematics and Numerical Analysis"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Optimization"]
[{"$nulls":4},"Analysis"]
[{"$nulls":4},"Mathematical Models of Cognitive Processes and Neural Networks"]
[{"$nulls":4},"Models matemàtics"]
[{"$nulls":4},"Visió per ordinador"]
[{"$nulls":4},"Diagnòstic per la imatge"]
[{"$nulls":4},"Optimització matemàtica"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"1. An Overview of SaT Segmentation Methodology and Its Applications in Image Processing -- 2. Analysis of different losses for deep learning image colorization -- 3. Blind phase retrieval with fast algorithms -- 4. Bregman Methods for Large-Scale Optimisation with Applications in Imaging -- 5. Connecting Hamilton-Jacobi Partial Differential Equations with Maximum a Posteriori and Posterior Mean Estimators for Some "]
[{"$nulls":4},"Non-convex Priors -- 6. Convex non-Convex Variational Models -- 7. Data-Informed Regularization for Inverse and Imaging Problems -- 8. Diffraction Tomography, Fourier Reconstruction, and Full Waveform Inversion -- 9. Domain Decomposition for Non-smooth (in Particular TV) Minimization -- 10. Fast numerical methods for image segmentation models."]
[null,null,null,"Sommario/riassunto","This handbook gathers together the state of the art on mathematical models and algorithms for imaging and vision. Its emphasis lies on rigorous mathematical methods, which represent the optimal solutions to a class of imaging and vision problems, and on effective algorithms, which are necessary for the methods to be translated to practical use in various applications. Viewing discrete images as data sampled from functional surfaces enables the use of advanced tools from calculus, functions and calculus of variations, and nonlinear optimization, and provides the basis of high-resolution imaging through geometry and variational models. Besides, optimization naturally connects traditional model-driven approaches to the emerging data-driven approaches of machine and deep learning. No other framework can provide comparable accuracy and precision to imaging and vision. Written by leading researchers in imaging and vision, the chapters in this handbook all start with gentle introductions, which make this work accessible to graduate students. For newcomers to the field, the book provides a comprehensive and fast-track introduction to the content, to save time and get on with tackling new and emerging challenges. For researchers, exposure to the state of the art of research works leads to an overall view of the entire field so as to guide new research directions and avoid pitfalls in moving the field forward and looking into the next decades of imaging and information services. This work can greatly benefit graduate students, researchers, and practitioners in imaging and vision; applied mathematicians; medical imagers; engineers; and computer scientists."]