Medial Representations : Mathematics, Algorithms and Applications /
With contributions by numerous experts
edited by Kaleem Siddiqi, Stephen M. Pizer.
- xviii, 440 páginas 204 ilustraciones, 84 ilustraciones en color. recurso en línea.
- Computational Imaging and Vision, 37 1381-6446 ; .
Springer eBooks
Mathematics -- Local Forms and Transitions of the Medial Axis -- Geometry and Medial Structure -- Algorithms -- Skeletons via Shocks of Boundary Evolution -- Discrete Skeletons from Distance Transforms in 2D and 3D -- Voronoi Skeletons -- Voronoi Methods for 3D Medial Axis Approximation -- Synthesis, Deformation, and Statistics of 3D Objects via M-Reps -- Applications -- Statistical Applications with Deformable M-Reps -- 3D Model Retrieval Using Medial Surfaces -- From the Infinitely Large to the Infinitely Small.
The last half century has seen the development of many biological or physical theories that have explicitly or implicitly involved medial descriptions of objects and other spatial entities in our world. Simultaneously mathematicians have studied the properties of these skeletal descriptions of shape, and, stimulated by the many areas where medial models are useful, computer scientists and engineers have developed numerous algorithms for computing and using these models. We bring this knowledge and experience together into this book in order to make medial technology more widely understood and used. Edited by Prof. K. Siddiqi and Prof. S. Pizer, renowned experts in the field and authors of five of the chapters, this book consists of an introductory chapter, two chapters on the major mathematical results on medial representations, five chapters on algorithms for extracting medial models from boundary or binary image descriptions of objects, and three chapters on applications in image analysis and other areas of study and design. These chapters have been integrated and combined with a mathematics notation appendix and a detailed glossary, bibliography and index. This book will serve the science and engineering communities using medial models and will provide learning material for students entering this field.