This book provides an introduction to the use of geometric partial differential equations in image processing and computer vision. This research area brings a number of new concepts into the field, providing a very fundamental and formal approach to image processing. State-of-the-art practical results in a large number of real problems are achieved with the techniques described in this book. Applications covered include image segmentation, shape analysis, image enhancement, and tracking. This book will be a useful resource for researchers and practitioners. It is intended to provide information for people investigating new solutions to image processing problems as well as for people searching for existent advanced solutions.
Rezensionen / Stimmen
' ... enjoyable to read ... an excellent introduction for someone interested in pursuing research in this area, with ample references to current work sprinkled throughout.' SIAM Review ' ... every person interested in image analysis by partial differential equations or related fields, such as differential geometry and curve evolution, should read this book.' Mathematics of Computation '... this book will certainly be accepted as a standard text ... A student who had worked through everything here would be very well equipped to develop powerful image processing software.' Alex M. Andrew, Robotica '... a useful introduction to the subject.' John Urbas, Zentralblatt fuer Mathematik 'For those who are prepared to also take the mathematics, a marvellous world will open. This is applied mathematics at its best. Recommended for researchers and practitioners.' Bulletin of the Belgian Mathematical Society
Sprache
Verlagsort
Zielgruppe
Produkt-Hinweis
Illustrationen
Worked examples or Exercises
Maße
Höhe: 229 mm
Breite: 152 mm
Dicke: 24 mm
Gewicht
ISBN-13
978-0-521-68507-8 (9780521685078)
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Schweitzer Klassifikation
Guillermo Sapiro is a Professor of Electrical and Computer Engineering at the University of Minnesota, where he works on differential geometry and geometric partial differential equations, both in theory and applications in computer vision and image analysis.
Autor*in
University of Minnesota
1. Basic mathematical background; 2. Geometric curve and surface evolution; 3. Geodesic curves and minimal surfaces; 4. Geometric diffusion of scalar images; 5. Geometric diffusion of vector valued images; 6. Diffusion on non-flat manifolds; 7. Contrast enhancement; 8. Additional theories and applications.
