Handbook of Mathematical Models in Computer Vision
(Sprache: Englisch)
This comprehensive volume is an essential reference tool for professional and academic researchers in the filed of computer vision, image processing, and applied mathematics. Continuing rapid advances in image processing have been enhanced by the...
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This comprehensive volume is an essential reference tool for professional and academic researchers in the filed of computer vision, image processing, and applied mathematics. Continuing rapid advances in image processing have been enhanced by the theoretical efforts of mathematicians and engineers. This marriage of mathematics and computer vision - computational vision - has resulted in a discrete approach to image processing that is more reliable when leveraging in practical tasks. This comprehensive volume provides a detailed discourse on the mathematical models used in computational vision from leading educators and active research experts in this field. Topical areas include: image reconstruction, segmentation and object extraction, shape modeling and registration, motion analysis and tracking, and 3D from images, geometry and reconstruction. The book also includes a study of applications in medical image analysis.
Handbook of Mathematical Models in Computer Vision provides a graduate-level treatment of this subject as well as serving as a complete reference work for professionals.
Handbook of Mathematical Models in Computer Vision provides a graduate-level treatment of this subject as well as serving as a complete reference work for professionals.
Klappentext zu „Handbook of Mathematical Models in Computer Vision “
Abstract Biological vision is a rather fascinating domain of research. Scientists of various origins like biology, medicine, neurophysiology, engineering, math ematics, etc. aim to understand the processes leading to visual perception process and at reproducing such systems. Understanding the environment is most of the time done through visual perception which appears to be one of the most fundamental sensory abilities in humans and therefore a significant amount of research effort has been dedicated towards modelling and repro ducing human visual abilities. Mathematical methods play a central role in this endeavour. Introduction David Marr's theory v^as a pioneering step tov^ards understanding visual percep tion. In his view human vision was based on a complete surface reconstruction of the environment that was then used to address visual subtasks. This approach was proven to be insufficient by neuro-biologists and complementary ideas from statistical pattern recognition and artificial intelligence were introduced to bet ter address the visual perception problem. In this framework visual perception is represented by a set of actions and rules connecting these actions. The emerg ing concept of active vision consists of a selective visual perception paradigm that is basically equivalent to recovering from the environment the minimal piece information required to address a particular task of interest.
Inhaltsverzeichnis zu „Handbook of Mathematical Models in Computer Vision “
Image Reconstruction.- Diffusion Filters and Wavelets: What Can They Learn from Each Other?.- Total Variation Image Restoration: Overview and Recent Developments.- PDE-Based Image and Surface Inpainting.- Boundary Exraction, Segmentation and Grouping.- Levelings: Theory and Practice.- Graph Cuts in Vision and Graphics: Theories and Applications.- Minimal Paths and Fast Marching Methods for Image Analysis.- Integrating Shape and Texture in DeformabIe Models: from Hybrid Methods to Metamorphs.- Variational Segmentation with Shape Priors.- Curve Propagation, Level Set Methods and Grouping.- On a Stochastic Model of Geometric Snakes.- Shape Modeling & Registration.- Invariant Processing and Occlusion Resistant Recognition of Planar Shapes.- Planar Shape Analysis and Its Applications in Image-Based Inferences.- Diffeomorphic Point Matching.- Uncertainty-Driven, Point-Based Image Registration.- Motion Analysis, Optical Flow & Tracking.- Optical Flow Estimation.- From Bayes to PDEs in Image Warping.- Image Alignment and Stitching.- Visual Tracking: A Short Research Roadmap.- Shape Gradient for Image and Video Segmentation.- Model-Based Human Motion Capture.- Modeling Dynamic Scenes: An Overview of Dynamic Textures.- 3D from Images, Projective Geometry & Stereo Reconstruction.- Differential Geometry from the Frenet Point of View: Boundary Detection, Stereo, Texture and Color.- Shape From Shading.- 3D from Image Sequences: Calibration, Motion and Shape Recovery.- Multi-view Reconstruction of Static and Dynamic Scenes.- Graph Cut Algorithms for Binocular Stereo with Occlusions.- Modelling Non-Rigid Dynamic Scenes from Multi-View Image Sequences.- Applications: Medical Image Analysis.- Interactive Graph-Based Segmentation Methods in Cardiovascular Imaging.- 3D Active Shape and Appearance Models in Cardiac Image Analysis.- Characterization of Diffusion Anisotropy in DWI.- Segmentation of Diffusion Tensor Images.- Variational Approaches to the Estimation,
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Regularizatinn and Segmentation of Diffusion Tensor Images.- An Introduction to Statistical Methods of Medical Image Registration.
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Bibliographische Angaben
- 2010, Softcover reprint of hardcover 1st ed. 2006, XXXIV, 606 Seiten, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Nikos Paragios, Yunmei Chen, Olivier D. Faugeras
- Verlag: Springer, Berlin
- ISBN-10: 1441938850
- ISBN-13: 9781441938855
Sprache:
Englisch
Pressezitat
From the reviews: "The focus of the book is on mathematical methods that both model and reproduce human visual abilities. ... This book is a must-have for those interested in the full breadth of research done in the biological & computer vision community. As a bonus, the chapters can also be used in a seminar-based, advanced undergraduate course in mathematical based computer vision. " (Arjan Kuijper, IAPR Newsletter, October, 2006)
"Computational visual perception can be defined as the discipline of enabling computers to identify features in image data. ... I found this book to be detailed and comprehensive enough to be well worth the time spent on it. Citations linking the text to the relevant literature are profusely sprinkled throughout the text, and a very extensive bibliography is included ... . the production qualities are excellent. ... it should be a useful reference text for researchers or practitioners in this field." (R. M. Malyankar, Computing Reviews, January, 2006)
"The editors of this important compendium view their task as a contribution to modeling and simulating human vision by machine. ... The editors should be congratulated for bringing together high-level researchers to contribute chapters on cutting-edge technologies based on mathematical modeling. This compendium is a solid contribution to the recent literature combining the theories and applications of mathematical modeling to the domain of computer vision." (R. Goldberg, Computing Reviews, June, 2006)
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