001454909 000__ 04351nam\a2200517\i\4500
001454909 001__ 1454909
001454909 003__ OCoLC
001454909 005__ 20230314003234.0
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001454909 007__ cr\un\nnnunnun
001454909 008__ 230228s2023\\\\sz\a\\\\of\\\\001\0\eng\d
001454909 020__ $$a9783030986612$$q(electronic bk.)
001454909 020__ $$a3030986616$$q(electronic bk.)
001454909 020__ $$z9783030986605
001454909 0247_ $$a10.1007/978-3-030-98661-2$$2doi
001454909 035__ $$aSP(OCoLC)1371328866
001454909 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE
001454909 049__ $$aISEA
001454909 050_4 $$aQA401
001454909 08204 $$a511/.8$$223/eng/20230228
001454909 24500 $$aHandbook of mathematical models and algorithms in computer vision and imaging :$$bmathematical imaging and vision /$$cKe Chen, Carola-Bibiane Schönlieb, Xue-Cheng Tai, Laurent Younes, editors.
001454909 264_1 $$aCham :$$bSpringer,$$c2023.
001454909 300__ $$a1 online resource :$$billustrations (some color)
001454909 336__ $$atext$$btxt$$2rdacontent
001454909 337__ $$acomputer$$bc$$2rdamedia
001454909 338__ $$aonline resource$$bcr$$2rdacarrier
001454909 500__ $$aIncludes index.
001454909 5050_ $$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.
001454909 506__ $$aAccess limited to authorized users.
001454909 520__ $$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.
001454909 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 28, 2023).
001454909 650_0 $$aMathematical models.
001454909 650_0 $$aComputer vision$$xMathematical models.
001454909 650_0 $$aDiagnostic imaging$$xMathematical models.
001454909 650_0 $$aMathematical optimization.
001454909 655_0 $$aElectronic books.
001454909 7001_ $$aChen, Ke,$$d1962-$$eeditor.
001454909 7001_ $$aSchönlieb, Carola-Bibiane,$$d1979-$$eeditor.$$1https://orcid.org/0000-0003-0099-6306
001454909 7001_ $$aTai, Xue-Cheng,$$eeditor.
001454909 7001_ $$aYounes, Laurent,$$d1963-$$eeditor.
001454909 852__ $$bebk
001454909 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-98661-2$$zOnline Access$$91397441.1
001454909 909CO $$ooai:library.usi.edu:1454909$$pGLOBAL_SET
001454909 980__ $$aBIB
001454909 980__ $$aEBOOK
001454909 982__ $$aEbook
001454909 983__ $$aOnline
001454909 994__ $$a92$$bISE