Skip to main content
SpringerLink
Log in

Handbook of Mathematical Models in Computer Vision

  • Book
  • © 2006

Overview

Editors:
  1. Nikos Paragios

    1. Ecole Nationale des Ponts et Chaussees, Champs sur Marne, France

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Yunmei Chen

    1. University of Florida, Gainesville, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Olivier Faugeras

    1. INRIA, Sophia Antipolis, France

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • The most comprehensive, up-to-date treatment of computational vision topics
  • Six thematic areas provide answers to the most dominant questions of computational vision

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (33 chapters)

  1. Front Matter

    Pages i-xxxiii
    Download chapter PDF

  2. Image Reconstruction

    1. Diffusion Filters and Wavelets: What Can They Learn from Each Other?

      • J. Weickert, G. Steidl, P. Mräzek, M. Welk, T. Brox
      Pages 1-16

    2. Total Variation Image Restoration: Overview and Recent Developments

      • T. Chan, S. Esedoglu, F. Park, A. Yip
      Pages 17-31

    3. PDE-Based Image and Surface Inpainting

      • M. Bertalmío, V. Caselles, G. Haro, G. Sapiro
      Pages 33-61

  3. Boundary Exraction, Segmentation and Grouping

    1. Levelings: Theory and Practice

      • F. Meyer
      Pages 63-78

    2. Graph Cuts in Vision and Graphics: Theories and Applications

      • Y. Boykov, O. Veksler
      Pages 79-96

    3. Minimal Paths and Fast Marching Methods for Image Analysis

      • L. Cohen
      Pages 97-111

    4. Integrating Shape and Texture in DeformabIe Models: from Hybrid Methods to Metamorphs

      • D. Metaxas, X. Huang, T. Chen
      Pages 113-129

    5. Variational Segmentation with Shape Priors

      • M. Bergtholdh, D. Cremers, C. Schurr
      Pages 131-143

    6. Curve Propagation, Level Set Methods and Grouping

      • N. Paragios
      Pages 145-159

    7. On a Stochastic Model of Geometric Snakes

      • A. Yezzi, D. Nain, G. Unal, O. Zeitouni, A. Tannenbaum
      Pages 161-174

  4. Shape Modeling & Registration

    1. Invariant Processing and Occlusion Resistant Recognition of Planar Shapes

      • A. Bruckstein
      Pages 175-188

    2. Planar Shape Analysis and Its Applications in Image-Based Inferences

      • A. Srivastava, S. Joshi, D. Kaziska, D. Wilson
      Pages 189-203

    3. Diffeomorphic Point Matching

      • H. Guo, A. Rangarajan, S. Joshi
      Pages 205-219

    4. Uncertainty-Driven, Point-Based Image Registration

      • C. Stewart
      Pages 221-235

  5. Motion Analysis, Optical Flow & Tracking

    1. Optical Flow Estimation

      • D. Fleet, Y. Weiss
      Pages 237-257

    2. From Bayes to PDEs in Image Warping

      • M. Nielsen, B. Markussen
      Pages 259-272

    3. Image Alignment and Stitching

      • R. Szeliski
      Pages 273-292

    4. Visual Tracking: A Short Research Roadmap

      • A. Blake
      Pages 293-307

    5. Shape Gradient for Image and Video Segmentation

      • S. Jehan-Besson, A. Herbulot, M. Bariaud, G. Aubert
      Pages 309-323
Back to top

Keywords

About this book

Abstract Biological vision is a rather fascinating domain of research. Scientists of various origins like biology, medicine, neurophysiology, engineering, math­ ematics, etc. aim to understand the processes leading to visual perception process and at reproducing such systems. Understanding the environment is most of the time done through visual perception which appears to be one of the most fundamental sensory abilities in humans and therefore a significant amount of research effort has been dedicated towards modelling and repro­ ducing human visual abilities. Mathematical methods play a central role in this endeavour. Introduction David Marr's theory v^as a pioneering step tov^ards understanding visual percep­ tion. In his view human vision was based on a complete surface reconstruction of the environment that was then used to address visual subtasks. This approach was proven to be insufficient by neuro-biologists and complementary ideas from statistical pattern recognition and artificial intelligence were introduced to bet­ ter address the visual perception problem. In this framework visual perception is represented by a set of actions and rules connecting these actions. The emerg­ ing concept of active vision consists of a selective visual perception paradigm that is basically equivalent to recovering from the environment the minimal piece information required to address a particular task of interest.

Reviews

From the reviews:

"The focus of the book is on mathematical methods that both model and reproduce human visual abilities. ... This book is a must-have for those interested in the full breadth of research done in the biological & computer vision community. As a bonus, the chapters can also be used in a seminar-based, advanced undergraduate course in mathematical based computer vision. " (Arjan Kuijper, IAPR Newsletter, October, 2006)

"Computational visual perception can be defined as the discipline of enabling computers to identify features in image data. … I found this book to be detailed and comprehensive enough to be well worth the time spent on it. Citations linking the text to the relevant literature are profusely sprinkled throughout the text, and a very extensive bibliography is included … . the production qualities are excellent. … it should be a useful reference text for researchers or practitioners in this field." (R. M. Malyankar, Computing Reviews, January, 2006)

"The editors of this important compendium view their task as a contribution to modeling and simulating human vision by machine. … The editors should be congratulated for bringing together high-level researchers to contribute chapters on cutting-edge technologies based on mathematical modeling. This compendium is a solid contribution to the recent literature combining the theories and applications of mathematical modeling to the domain of computer vision." (R. Goldberg, Computing Reviews, June, 2006)

Editors and Affiliations

  • Ecole Nationale des Ponts et Chaussees, Champs sur Marne, France

    Nikos Paragios

  • University of Florida, Gainesville, USA

    Yunmei Chen

  • INRIA, Sophia Antipolis, France

    Olivier Faugeras

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation