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    1. Naturvetenskap och teknik
    2. Matematik och naturvetenskap
    3. Matematik
    4. Beräkning och matematisk analys

    Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2

    AvRon Kimmel,Xue-Cheng Tai

    Inbunden, Engelska, 2019

    Del 20 i serien Handbook of Numerical Analysis

    2 216 kr

    Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

    Beskrivning

    Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, Volume 20, surveys the contemporary developments relating to the analysis and learning of images, shapes and forms, covering mathematical models and quick computational techniques. Chapter cover Alternating Diffusion: A Geometric Approach for Sensor Fusion, Generating Structured TV-based Priors and Associated Primal-dual Methods, Graph-based Optimization Approaches for Machine Learning, Uncertainty Quantification and Networks, Extrinsic Shape Analysis from Boundary Representations, Efficient Numerical Methods for Gradient Flows and Phase-field Models, Recent Advances in Denoising of Manifold-Valued Images, Optimal Registration of Images, Surfaces and Shapes, and much more.



    • Covers contemporary developments relating to the analysis and learning of images, shapes and forms
    • Presents mathematical models and quick computational techniques relating to the topic
    • Provides broad coverage, with sample chapters presenting content on Alternating Diffusion and Generating Structured TV-based Priors and Associated Primal-dual Methods

    Produktinformation

    • Utgivningsdatum:2019-10-15
    • Mått:152 x 229 x 38 mm
    • Vikt:1 220 g
    • Format:Inbunden
    • Språk:Engelska
    • Serie:Handbook of Numerical Analysis
    • Antal sidor:706
    • Förlag:Elsevier Science
    • ISBN:9780444641403

    Utforska kategorier

    • Beräkning och matematisk analys inom Naturvetenskap och teknik

    Mer om författaren

    Ron Kimmel is a Professor of Computer Science at the Technion where he holds the Montreal Chair in Sciences. He held a post-doctoral position at UC Berkeley and a visiting professorship at Stanford University. He has worked in various areas of image and shape analysis in computer vision, image processing, and computer graphics. Kimmel's interest in recent years has been non-rigid shape processing and analysis, medical imaging and computational biometry, numerical optimization of problems with a geometric flavor, and applications of metric geometry, deep learning, and differential geometry. Kimmel is an IEEE Fellow for his contributions to image processing and non-rigid shape analysis. He is an author of two books, an editor of one, and an author of numerous articles. He is the founder of the Geometric Image Processing Lab. and a founder and advisor of several successful image processing and analysis companies. Professor Tai Xue-Cheng is a member of the Department of Mathematics at the Hong Kong Baptist University, Hong Kong and also the University of Bergen of Norway. His research interests include Numerical partial differential equations, optimization techniques, inverse problems, and image processing. He is the winner for several prizes for his contributions to scientific computing and innovative researches for image processing. He served as organizing and program committee members for many international conferences and has been often invited for international conferences. He has served as referee and reviewers for many premier conferences and journals.

    Innehållsförteckning

    • 1. Diffusion operators for multimodal data analysisTal Shnitzer, Roy R. Lederman, Gi-Ren Liu, Ronen Talmon and Hau-tieng Wu2. Intrinsic and extrinsic operators for shape analysisYu Wang and Justin Solomon3. Operator-based representations of discrete tangent vector fieldsMirela Ben-Chen and Omri Azencot4. Active contour methods on arbitrary graphs based on partial differential equationsChristos Sakaridis, Nikos Kolotouros, Kimon Drakopoulos and Petros Maragos5. Fast operator-splitting algorithms for variational imaging models: Some recent developmentsRoland Glowinski, Shousheng Luo and Xue-Cheng Tai6. From active contours to minimal geodesic paths: New solutions to active contours problems by Eikonal equationsDa Chen and Laurent D. Cohen7. Computable invariants for curves and surfacesOshri Halimi, Dan Raviv, Yonathan Aflalo and Ron Kimmel8. Solving PDEs on manifolds represented as point clouds and applicationsRongjie Lai and Hongkai Zhao9. Tightening continuous relaxations for MAP inference in discrete MRFs: A surveyHariprasad Kannan, Nikos Komodakis and Nikos Paragios10. Lagrangian methods for composite optimizationShoham Sabach and Marc Teboulle11. Generating structured nonsmooth priors and associated primal-dual methodsMichael Hintermuller and Kostas Papafitsoros12. Graph-based optimization approaches for machine learning, uncertainty quantification and networksAndrea L. Bertozzi and Ekaterina Merkurjev13. Survey of fast algorithms for Euler’s elastica-based image segmentationSung Ha Kang, Xuecheng Tai and Wei Zhu14. Recent advances in denoising of manifold-valued imagesR. Bergmann, F. Laus, J. Persch and G. Steidl15. Image and surface registrationKe Chen, Lok Ming Lui and Jan Modersitzki16. Metric registration of curves and surfaces using optimal controlMartin Bauer, Nicolas Charon and Laurent Younes17. Efficient and accurate structure preserving schemes for complex nonlinear systemsJie Shen