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Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS)

Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces

Authors: Laube, Pascal

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  • Demonstrates that machine learning can be a viable part of the CAD reverse engineering pipeline
  • Scientific-technical study
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eBook 50,28 €
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  • ISBN 978-3-658-29017-7
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Softcover 62,39 €
price for Spain (gross)
  • ISBN 978-3-658-29016-0
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About this book

Pascal Laube presents machine learning approaches for three key problems of reverse engineering of defective structured surfaces: parametrization of curves and surfaces, geometric primitive classification and inpainting of high-resolution textures. The proposed methods aim to improve the reconstruction quality while further automating the process. The contributions demonstrate that machine learning can be a viable part of the CAD reverse engineering pipeline.

About the authors

Pascal Laube’s main research interest is the development of machine learning methods for CAD reverse engineering. He is currently developing self-driving cars for an international operating German enterprise in the field of mobility, automotive and industrial technology.

Table of contents (6 chapters)

Table of contents (6 chapters)

Buy this book

eBook 50,28 €
price for Spain (gross)
  • ISBN 978-3-658-29017-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 62,39 €
price for Spain (gross)
  • ISBN 978-3-658-29016-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces
Authors
Series Title
Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS)
Copyright
2020
Publisher
Springer Vieweg
Copyright Holder
Springer Fachmedien Wiesbaden GmbH, part of Springer Nature
eBook ISBN
978-3-658-29017-7
DOI
10.1007/978-3-658-29017-7
Softcover ISBN
978-3-658-29016-0
Series ISSN
2661-8087
Edition Number
1
Number of Pages
XV, 161
Number of Illustrations
56 b/w illustrations
Topics