000856631 000__ 06179cam\a2200541Ii\4500
000856631 001__ 856631
000856631 005__ 20230306145146.0
000856631 006__ m\\\\\o\\d\\\\\\\\
000856631 007__ cr\un\nnnunnun
000856631 008__ 181128s2018\\\\sz\a\\\\ob\\\\101\0\eng\d
000856631 020__ $$a9783319912745$$q(electronic book)
000856631 020__ $$a3319912747$$q(electronic book)
000856631 020__ $$z9783319912738
000856631 035__ $$aSP(OCoLC)on1076485223
000856631 035__ $$aSP(OCoLC)1076485223
000856631 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dN$T$$dEBLCP$$dGW5XE$$dFIE
000856631 049__ $$aISEA
000856631 050_4 $$aTA1637.5
000856631 08204 $$a621.367028553$$223
000856631 24500 $$aImaging, vision and learning based on optimization and PDEs :$$bIVLOPDE, Bergen, Norway, August 29 -- September 2, 2016 /$$ceditors Xue-Cheng Tai, Egil Bae and Marius Lysaker.
000856631 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2018]
000856631 300__ $$a1 online resource :$$billustrations
000856631 336__ $$atext$$btxt$$2rdacontent
000856631 337__ $$acomputer$$bc$$2rdamedia
000856631 338__ $$aonline resource$$bcr$$2rdacarrier
000856631 4901_ $$aMathematics and visualization
000856631 504__ $$aIncludes bibliographical references and index.
000856631 5050_ $$aIntro; Preface; Contents; Part I Image Reconstruction from Incomplete Data; 1 Adaptive Regularization for Image Reconstruction from Subsampled Data; Introduction; Problem Settings and Notations; Adaptive Regularization Approach; ROF-Model and Surrogate Iteration; Hierarchical Spatially Adaptive Algorithm; Numerical Experiments; Reconstruction of Partial Fourier Data; Wavelet Inpainting; Qualitative Relation to Other Spatially Distributed Parameter Methods; Conclusion; References; 2 A Convergent Fixed-Point Proximity Algorithm Accelerated by FISTA for the 0 Sparse Recovery Problem
000856631 5058_ $$aIntroductionMinimizers of the Proposed Model; Convergence Analysis of the Proposed Algorithms; Sparse Support Pursuit; Recovery on the Sparse Support; Numerical Experiments; Conclusion; References; 3 Sparse-Data Based 3D Surface Reconstructionfor Cartoon and Map; Introduction; Proposed Model; Augmented Lagrangian Method; Solving the Q-Subproblem (3.7); Solving the P-Subproblem (3.8); Solving the C-Subproblem (3.9); Solving the S-Subproblem (3.10); Solving the I-Subproblem (3.11); Solving the E-Subproblem (3.12); Numerical Results; Conclusion; References
000856631 5058_ $$aPart II Image Enhancement, Restoration and Registration4 Variational Methods for Gamut Mappingin Cinema and Television; Introduction; Related Work; Gamut Reduction Algorithms (GRAs); Gamut Extension Algorithms (GEAs); Reproduction Intent and Evaluation; Subjective Evaluation; Objective Evaluation; Gamut Mapping in RGB Based on Perceptually-Based Color and Contrast Enhancement; GRA-RGB Zamir2014: Gamut Reduction Algorithm on RGB; GEA-RGB Zamir2014: Gamut Extension Algorithm on RGB; Gamut Extension in CIELAB Color Space; GEA-LAB1 Zamir2015: Gamut Extension Algorithm
000856631 5058_ $$aGEA-LAB2 Zamir2017: Gamut Extension Algorithm Driven by Hue, Saturation and Chroma ConstraintsQualitative Experiments and Results; Methodology; Results; Temporal Consistency Test; Gamut Mapping Using Kernel Based Retinex (KBR) in HSV Color Space; GEA-KBR Zamir2016: Gamut Extension Algorithm; Results of GEA-KBR; Making the GEA-KBR Faster; GRA-KBR: Gamut Reduction Algorithm; Results of GRA-KBR; Conclusion and Future Work; References; 5 Functional Lifting for Variational Problems with Higher-Order Regularization; Introduction and Related Work; Contributions
000856631 5058_ $$aLifting for Absolute Laplacian RegularizationNotation and Mathematical Preliminaries; Approximate Relaxation of the Absolute Laplacian; Experimental Results; Non-convex Denoising with Second-Order Regularity; Image Registration Using the Absolute Laplacian; Translation-Only Synthetic Image; Real-World Image Registration; Conclusion and Outlook; References; 6 On the Convex Model of Speckle Reduction; Introduction; A Convex Model for Despeckling; Speckle Noise; Convex Variational Model for Despeckling; Numerical Scheme Using Bermudez-Moreno Algorithm; Generalized Form
000856631 506__ $$aAccess limited to authorized users.
000856631 520__ $$aThis volume presents the peer-reviewed proceedings of the international conference Imaging, Vision and Learning Based on Optimization and PDEs (IVLOPDE), held in Bergen, Norway, in August/September 2016. The contributions cover state-of-the-art research on mathematical techniques for image processing, computer vision and machine learning based on optimization and partial differential equations (PDEs). It has become an established paradigm to formulate problems within image processing and computer vision as PDEs, variational problems or finite dimensional optimization problems. This compact yet expressive framework makes it possible to incorporate a range of desired properties of the solutions and to design algorithms based on well-founded mathematical theory. A growing body of research has also approached more general problems within data analysis and machine learning from the same perspective, and demonstrated the advantages over earlier, more established algorithms. This volume will appeal to all mathematicians and computer scientists interested in novel techniques and analytical results for optimization, variational models and PDEs, together with experimental results on applications ranging from early image formation to high-level image and data analysis.--$$cProvided by publisher.
000856631 588__ $$aOnline resource; title from PDF title page (viewed November 30, 2018).
000856631 650_0 $$aImage processing$$xMathematics$$vCongresses.
000856631 650_0 $$aImage processing$$xDigital techniques$$vCongresses.
000856631 650_0 $$aDifferential equations$$vCongresses.
000856631 650_0 $$aVisualization$$xMathematics$$vCongresses.
000856631 7001_ $$aTai, Xue-Cheng,$$eeditor.
000856631 7001_ $$aBae, Egil,$$eeditor.
000856631 7001_ $$aLysaker, Marius,$$eeditor.
000856631 7112_ $$aInternational Conference "Imaging, vision and learning based on optimization and PDEs"$$d(2016 :$$cBergen, Norway)
000856631 830_0 $$aMathematics and visualization.
000856631 852__ $$bebk
000856631 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-91274-5$$zOnline Access$$91397441.1
000856631 909CO $$ooai:library.usi.edu:856631$$pGLOBAL_SET
000856631 980__ $$aEBOOK
000856631 980__ $$aBIB
000856631 982__ $$aEbook
000856631 983__ $$aOnline
000856631 994__ $$a92$$bISE