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From Global to Local Statistical Shape Priors

Novel Methods to Obtain Accurate Reconstruction Results with a Limited Amount of Training Shapes

  • Book
  • © 2017

Overview

Authors:
  1. Carsten Last

    1. Institut für Robotik und Prozessinformat, Technische Universität Braunschweig, Braunschweig, Germany

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  • Is understandable, readable, and well-structured with numerous illustrations
  • Presents interesting, new, and powerful concepts
  • Serves as a “gateway drug” to the field thanks to its unique presentation style
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 98)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xxi
    Download chapter PDF

  2. Basics

    • Carsten Last
    Pages 1-19

  3. Statistical Shape Models (SSMs)

    • Carsten Last
    Pages 21-51

  4. A Locally Deformable Statistical Shape Model (LDSSM)

    • Carsten Last
    Pages 53-73

  5. Evaluation of the Locally Deformable Statistical Shape Model

    • Carsten Last
    Pages 75-123

  6. Global-To-Local Shape Priors for Variational Level Set Methods

    • Carsten Last
    Pages 125-172

  7. Evaluation of the Global-To-Local Variational Formulation

    • Carsten Last
    Pages 173-205

  8. Conclusion and Outlook

    • Carsten Last
    Pages 207-209

  9. Back Matter

    Pages 211-259
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Keywords

About this book

This book proposes a new approach to handle the problem of limited training data. Common approaches to cope with this problem are to model the shape variability independently across predefined segments or to allow artificial shape variations that cannot be explained through the training data, both of which have their drawbacks. The approach presented uses a local shape prior in each element of the underlying data domain and couples all local shape priors via smoothness constraints. The book provides a sound mathematical foundation in order to embed this new shape prior formulation into the well-known variational image segmentation framework. The new segmentation approach so obtained allows accurate reconstruction of even complex object classes with only a few training shapes at hand.

Authors and Affiliations

  • Institut für Robotik und Prozessinformat, Technische Universität Braunschweig, Braunschweig, Germany

    Carsten Last

About the author

Carsten Last received his diploma degree in computer and communications systems engineering (with distinction) from TU Braunschweig, Germany, in 2009. During his studies he worked as a student assistant in the area of speech enhancement at the Institute for Communications Technology at TU Braunschweig. From 2009 to 2015 he was a research assistant and PhD student at the Institute for Robotics and Process Control at TU Braunschweig, from which he received his doctorate degree in computer science in 2016 (summa cum laude). His research focused mainly on the areas of medical image processing and computer vision. Since 2015, he is working as a research engineer at Volkswagen AG in the area of autonomous driving.

Bibliographic Information

  • Book Title: From Global to Local Statistical Shape Priors

  • Book Subtitle: Novel Methods to Obtain Accurate Reconstruction Results with a Limited Amount of Training Shapes

  • Authors: Carsten Last

  • Series Title: Studies in Systems, Decision and Control

  • DOI: https://doi.org/10.1007/978-3-319-53508-1

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing AG 2017

  • Hardcover ISBN: 978-3-319-53507-4Published: 21 March 2017

  • Softcover ISBN: 978-3-319-85169-3Published: 21 July 2018

  • eBook ISBN: 978-3-319-53508-1Published: 14 March 2017

  • Series ISSN: 2198-4182

  • Series E-ISSN: 2198-4190

  • Edition Number: 1

  • Number of Pages: XXI, 259

  • Number of Illustrations: 20 b/w illustrations, 64 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence, Computer Imaging, Vision, Pattern Recognition and Graphics

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