Scale Space and Variational Methods in Computer Vision [electronic resource] : 7th International Conference, SSVM 2019, Hofgeismar, Germany, June 30 - July 4, 2019, Proceedings / edited by Jan Lellmann, Martin Burger, Jan Modersitzki.
Contributor(s): Lellmann, Jan [editor.]
| Burger, Martin [editor.] | Modersitzki, Jan [editor.] | SpringerLink (Online service).
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BookSeries: Image Processing, Computer Vision, Pattern Recognition, and Graphics: 11603Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XVII, 574 p. 302 illus., 153 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030223687.Subject(s): Computer vision | Numerical analysis | Computer science -- Mathematics | Artificial intelligence | Computer Vision | Numerical Analysis | Mathematical Applications in Computer Science | Artificial IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.37 Online resources: Click here to access online
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Springer Nature eBookSummary: 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. .
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. .
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