Bézier and Splines in Image Processing and Machine Vision

Digital image processing and machine vision have grown considerably during the last few decades. Of the various techniques, developed so far splines play a positive and significant role in many of them. Strong mathematical theory and ease of implementations is one of the keys of their success in man...

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Détails bibliographiques
Cote:Libro Electrónico
Auteurs principaux: Biswas, Sambhunath (Auteur), Lovell, Brian C. (Auteur)
Collectivité auteur: SpringerLink (Online service)
Format: Électronique eBook
Langue:Inglés
Publié: London : Springer London : Imprint: Springer, 2008.
Édition:1st ed. 2008.
Sujets:
Computer vision.
Image processing-Digital techniques.
Computer Vision.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Accès en ligne:Texto Completo
Table des matières:
  • Part I Early Background
  • 1 Bernstein Polynomial and Bézier-Bernstein Spline
  • Significance of Bernstein Polynomial in Splines
  • 2 Image Segmentation
  • Two Different Concepts of Segmentation
  • Contour Based Segmentation
  • Region Based Segmentation
  • 3 1-d B-B Spline Polynomial and Hilbert Scan for Graylevel Image Coding
  • Hilbert Scanned Image
  • Shortcomings of Bernstein Polynomial and Error of Approximation
  • 4 Image Compression
  • SLIC: Sub-image Based Lossy Image Compression
  • Part II Intermediate Steps
  • 5 B-Splines and its Applications
  • B-Spline Function
  • 6 Beta-Splines: A Flexible Model
  • Beta-Spline Curve
  • 7 Discrete Spline and Vision
  • Smoothing Discrete Spline and Vision
  • Cardinal B-spline Basis and Riesz Basis
  • 8 Spline Wavelets: Construction, Implication and Uses
  • Cardinal B-spline Basis and Riesz Basis
  • 9 Snakes and Active Contours
  • Splines and Energy Minimisation Techniques
  • Part III Advanced Methodologies
  • 10 Globally Optimal Energy Minimisation Techinques
  • Globally Minimal Surfaces (GMS).

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