LEADER 05435nam 22008055 450 001 9910674347403321 005 20250610121115.0 010 $a9783030986612 010 $a3030986616 024 7 $a10.1007/978-3-030-98661-2 035 $a(MiAaPQ)EBC7209271 035 $a(Au-PeEL)EBL7209271 035 $a(CKB)26240815900041 035 $a(DE-He213)978-3-030-98661-2 035 $a(PPN)268208271 035 $a(EXLCZ)9926240815900041 100 $a20230224d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHandbook of Mathematical Models and Algorithms in Computer Vision and Imaging $eMathematical Imaging and Vision /$fedited by Ke Chen, Carola-Bibiane Schönlieb, Xue-Cheng Tai, Laurent Younes 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (1981 pages) 311 08$aPrint version: Chen, Ke Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging Cham : Springer International Publishing AG,c2023 9783030986605 320 $aIncludes bibliographical references and index. 327 $a1. 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 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. 330 $aThis 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. 606 $aMathematics$xData processing 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aMathematical optimization 606 $aMathematical analysis 606 $aNeural networks (Computer science) 606 $aComputational Mathematics and Numerical Analysis 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aOptimization 606 $aAnalysis 606 $aMathematical Models of Cognitive Processes and Neural Networks 606 $aModels matemàtics$2thub 606 $aVisió per ordinador$2thub 606 $aDiagnòstic per la imatge$2thub 606 $aOptimització matemàtica$2thub 608 $aLlibres electrònics$2thub 615 0$aMathematics$xData processing. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aMathematical optimization. 615 0$aMathematical analysis. 615 0$aNeural networks (Computer science) 615 14$aComputational Mathematics and Numerical Analysis. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aOptimization. 615 24$aAnalysis. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 7$aModels matemàtics 615 7$aVisió per ordinador 615 7$aDiagnòstic per la imatge 615 7$aOptimització matemàtica 676 $a006.37 702 $aChen$b Ke 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910674347403321 996 $aHandbook of Mathematical Models and Algorithms in Computer Vision and Imaging$92830638 997 $aUNINA