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Conference SSVM (Conference) (7th : 2019 : Hofgeismar, Germany)
Title Scale space and variational methods in computer vision : 7th International Conference, SSVM 2019, Hofgeismar, Germany, June 30-July 4, 2019, Proceedings / Jan Lellmann, Martin Burger, Jan Modersitzki (eds.).
Imprint Cham, Switzerland : Springer, 2019.

LOCATION CALL # STATUS MESSAGE
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Conference SSVM (Conference) (7th : 2019 : Hofgeismar, Germany)
Series Lecture notes in computer science ; 11603.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
Lecture notes in computer science ; 11603.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
Subject Computer vision -- Congresses.
Alt Name Lellmann, Jan,
Burger, Martin (Mathematician),
Modersitzki, Jan,
Add Title SSVM 2019
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Conference SSVM (Conference) (7th : 2019 : Hofgeismar, Germany)
Series Lecture notes in computer science ; 11603.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
Lecture notes in computer science ; 11603.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
Subject Computer vision -- Congresses.
Alt Name Lellmann, Jan,
Burger, Martin (Mathematician),
Modersitzki, Jan,
Add Title SSVM 2019
Description 1 online resource (xvii, 574 pages) : illustrations (some color).
Summary This book constitutes the proceedings of the 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019, held in Hofgeismar, Germany, in June/July 2019. The 44 papers included in this volume were carefully reviewed and selected for inclusion in this book. They were organized in topical sections named: 3D vision and feature analysis; inpainting, interpolation and compression; inverse problems in imaging; optimization methods in imaging; PDEs and level-set methods; registration and reconstruction; scale-space methods; segmentation and labeling; and variational methods.
Note Includes author index.
Contents Intro; Preface; Tribute to Mila Nikolova (1962-2018); Organization; Contents; 3D Vision and Feature Analysis; The Fractional Harris-Laplace Feature Detector; 1 Introduction; 2 Fractional Differentiation; 2.1 The CRONE Operator; 3 The Harris-Laplace Detector; 4 The Fractional Harris-Laplace Detector; 5 Results; 5.1 Boats1 Sequence; 5.2 Boats2 Sequence; 6 Discussion; 7 Future Directions; References; Macrocanonical Models for Texture Synthesis; 1 Introduction; 2 Maximum Entropy Models; 2.1 Microcanonical Models; 2.2 Macrocanonical Models; 2.3 Some Feature Examples
3 Minimization and Sampling Algorithm3.1 Maximizing the Entropy; 3.2 Sampling from Gibbs Measures; 3.3 Combining Dynamics; 4 Experiments; 4.1 Empirical Convergence of the Sampling Algorithm; 4.2 Neural Network Features; 4.3 Comparison with State-of-the Art Methods; 5 Perspectives; References; Finding Structure in Point Cloud Data with the Robust Isoperimetric Loss; 1 Introduction; 1.1 Previous Work and Challenges; 2 Preliminaries and Main Assumptions; 3 Proposed Approach; 3.1 Outlier Estimation; 3.2 Step 2: Adaptive Regularization Using the Isoperimetric Loss (IPL); 4 Experimental Results
4.1 Manifold Denoising4.2 The RIPL Approach; 4.3 Experiments with Real Data: Application to Motion Segmentation; 4.4 Experiments with Real Data: Application with 3D Laser Scan Data; 5 Discussion; References; Deep Eikonal Solvers; 1 Introduction; 2 Background; 2.1 The Eikonal Equation; 2.2 Numerical Approximation; 2.3 Fast Eikonal Solvers; 3 Deep Eikonal Solver; 3.1 Training the Local Solver; 3.2 Deep Eikonal Solver for Cartesian Grids; 4 Deep Eikonal Solver for Triangulated Meshes; 4.1 Experimental Results; 5 Conclusion; References
A Splitting-Based Algorithm for Multi-view Stereopsis of Textureless Objects1 Introduction; 2 Preliminaries; 3 A Generic Splitting Strategy for Multi-view Stereo; 4 Regularizers for Textureless Multi-view Stereopsis; 5 Experimental Results; 6 Conclusion and Perspectives; References; Inpainting, Interpolation and Compression; Pseudodifferential Inpainting: The Missing Link Between PDE- and RBF-Based Interpolation; 1 Introduction; 2 From Harmonic to Pseudodifferential Inpainting; 3 Interpolation with Radial Basis Functions; 4 Connecting both Worlds; 5 One Numerical Algorithm for All Approaches
6 Experiments7 Conclusions and Outlook; References; Towards PDE-Based Video Compression with Optimal Masks and Optic Flow; 1 Introduction; 2 Discussion of Considered Models and Methods; 2.1 Image Inpainting with PDEs; 2.2 Extension from Images to Videos; 2.3 Optical Flow; 3 Combining Optimal Masks with Flow Data; 4 Experimental Evaluation; 4.1 Methods Considered; 4.2 Evaluation; 4.3 Influence of the Optical Flow; 4.4 Evaluation of the Reconstruction Error; 5 Summary and Conclusion; References; Compressing Audio Signals with Inpainting-Based Sparsification; 1 Introduction
ISBN 9783030223687 electronic book
303022368X electronic book
ISBN/ISSN 10.1007/978-3-030-22
OCLC # 1106169114


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