Ellipse Fitting for Computer Vision

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Details

Because circular objects are projected to ellipses in images, ellipse fitting is a first step for 3-D analysis of circular objects in computer vision applications. For this reason, the study of ellipse fitting began as soon as computers came into use for image analysis in the 1970s, but it is only recently that optimal computation techniques based on the statistical properties of noise were established. These include renormalization (1993), which was then improved as FNS (2000) and HEIV (2000). Later, further improvements, called hyperaccurate correction (2006), HyperLS (2009), and hyper-renormalization (2012), were presented. Today, these are regarded as the most accurate fitting methods among all known techniques. This book describes these algorithms as well implementation details and applications to 3-D scene analysis. We also present general mathematical theories of statistical optimization underlying all ellipse fitting algorithms, including rigorous covariance and bias analyses and the theoretical accuracy limit. The results can be directly applied to other computer vision tasks including computing fundamental matrices and homographies between images. This book can serve not simply as a reference of ellipse fitting algorithms for researchers, but also as learning material for beginners who want to start computer vision research. The sample program codes are downloadable from the website: https://sites.google.com/a/morganclaypool.com/ellipse-fitting-for-computer-vision-implementation-and-applications.

Autorentext

Dr. Kenichi Kanatani is a Professor Emeritus at Okayama University, Japan. Drs. Yasuyuki Sugaya and Yasushi Kanazawa are Associate Professors in the Department of Computer Science and Engineering at Toyohashi University of Technology, Japan.


Inhalt
Preface.- Introduction.- Algebraic Fitting.- Geometric Fitting.- Robust Fitting.- Ellipse-based 3-D Computation.- Experiments and Examples.- Extension and Generalization.- Accuracy of Algebraic Fitting.- Maximum Likelihood and Geometric Fitting.- Theoretical Accuracy Limit.- Answers.- Bibliography.- Authors' Biographies.- Index .

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783031006876
    • Genre Information Technology
    • Lesemotiv Verstehen
    • Anzahl Seiten 128
    • Größe H8mm x B191mm x T235mm
    • Jahr 2016
    • EAN 9783031006876
    • Format Kartonierter Einband
    • ISBN 978-3-031-00687-6
    • Titel Ellipse Fitting for Computer Vision
    • Autor Kenichi Kanatani , Yasuyuki Sugaya , Yasushi Kanazawa
    • Untertitel Implementation and Applications
    • Herausgeber Springer
    • Sprache Englisch

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