Bærentzen, J.A., Gravesen, J., Anton, F., Aanæs, H.
2012, XVIII, 326 p.
Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.
You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.
After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.
Provides a practical focus on important techniques not covered by traditional courses on computer vision and computer graphics
Presents an introduction to the theoretical and mathematical underpinnings of each technique
Includes self-study exercises throughout the text, and supplies additional material at a supplementary website
Optical scanning is rapidly becoming ubiquitous. From industrial laser scanners to medical CT, MR and 3D ultrasound scanners, numerous organizations now have easy access to optical acquisition devices that provide huge volumes of image data. However, the raw geometry data acquired must first be processed before it is useful.
This Guide to Computational Geometry Processing reviews the algorithms for processing geometric data, with a practical focus on important techniques not covered by traditional courses on computer vision and computer graphics. This is balanced with an introduction to the theoretical and mathematical underpinnings of each technique, enabling the reader to not only implement a given method, but also to understand the ideas behind it, its limitations and its advantages.
Topics and features:
Presents an overview of the underlying mathematical theory, covering vector spaces, metric space, affine spaces, differential geometry, and finite difference methods for derivatives and differential equations
Reviews geometry representations, including polygonal meshes, splines, and subdivision surfaces
Examines techniques for computing curvature from polygonal meshes
Describes algorithms for mesh smoothing, mesh parametrization, and mesh optimization and simplification
Discusses point location databases and convex hulls of point sets
Investigates the reconstruction of triangle meshes from point clouds, including methods for registration of point clouds and surface reconstruction
Provides additional material at a supplementary website
Includes self-study exercises throughout the text
Graduate students will find this text a valuable, hands-on guide to developing key skills in geometry processing. The book will also serve as a useful reference for professionals wishing to improve their competency in this area.