Skip to main content
SpringerLink
Log in

Shape, Contour and Grouping in Computer Vision

  • Book
  • © 1999

Overview

Authors:
  1. David A. Forsyth

    1. Computer Science Division, University of California at Berkeley, Berkeley, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Joseph L. Mundy

    1. G.E. Corporate Research and Development, Niskayuna, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  3. Vito Gesú

    1. Palermo University, C.I.T.C., Sicily, Italy

    View author publications

    You can also search for this author in PubMed   Google Scholar

  4. Roberto Cipolla

    1. Department of Engineering, University of Cambridge, Cambridge, UK

    View author publications

    You can also search for this author in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1681)

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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 (19 chapters)

  1. Front Matter

    Pages I-VIII
    Download chapter PDF

  2. Introduction

    1. Introduction

      • David Forsyth, Joe Mundy
      Pages 3-8

    2. An Empirical-Statistical Agenda for Recognition

      • David Forsyth
      Pages 9-21

    3. A Formal-Physical Agenda for Recognition

      • Joe Mundy
      Pages 22-27

  3. Shape

    1. Shape Models and Object Recognition

      • Jean Ponce, Martha Cepeda, Sung-il Pae, Steve Sullivan
      Pages 31-57

    2. Order Structure, Correspondence, and Shape Based Categories

      • Stefan Carlsson
      Pages 58-71

    3. Quasi-Invariant Parameterisations and Their Applications in Computer Vision

      • Jun Sato, Roberto Cipolla
      Pages 72-92

  4. Shading

    1. Representations for Recognition Under Variable Illumination

      • David Juris Kriegman, Peter N. Belhumeur, Athinodoros S. Georghiades
      Pages 95-131

    2. Shadows, Shading, and Projective Ambiguity

      • Peter N. Belhumeur, David Juris Kriegman, Alan L. Yuille
      Pages 132-151

  5. Grouping

    1. Grouping in the Normalized Cut Framework

      • Jitendra Malik, Jianbo Shi, Serge Belongie, Thomas Leung
      Pages 155-164

    2. Geometric Grouping of Repeated Elements within Images

      • Frederik Schaffalitzky, Andrew Zisserman
      Pages 165-181

    3. Constrained Symmetry for Change Detection

      • Rupert W. Curwen, Joe L. Mundy
      Pages 182-195

    4. Grouping Based on Coupled Diffusion Maps

      • Marc Proesmans, Luc Van Gool
      Pages 196-213

  6. Representation and Recognition

    1. Integrating Geometric and Photometric Information for Image Retrieval

      • Cordelia Schmid, Roger Mohr, Andrew Zisserman
      Pages 217-233

    2. Towards the Integration of Geometric and Appearance-Based Object Recognition

      • Joe Mundy, Tushar Saxena
      Pages 234-245

    3. Recognizing Objects Using Color-Annotated Adjacency Graphs

      • Peter Tu, Richard Hartley, Tushar Saxena
      Pages 246-263

    4. A Cooperating Strategy for Objects Recognition

      • Antonio Chella, Vito Di Gesù, Ignazio Infantino, Daniela Intravaia, Cesare Valenti
      Pages 264-274

  7. Statistics, Learning and Recognition

    1. Model Selection for Two View Geometry:A Review

      • Philip H. S. Torr
      Pages 277-301

    2. Finding Objects by Grouping Primitives

      • David Forsyth, John Haddon, Sergey Ioffe
      Pages 302-318

    3. Object Recognition with Gradient-Based Learning

      • Yann LeCun, Patrick Haffner, Léon Bottou, Yoshua Bengio
      Pages 319-345
Back to top

Keywords

About this book

Computer vision has been successful in several important applications recently. Vision techniques can now be used to build very good models of buildings from pictures quickly and easily, to overlay operation planning data on a neuros- geon’s view of a patient, and to recognise some of the gestures a user makes to a computer. Object recognition remains a very di cult problem, however. The key questions to understand in recognition seem to be: (1) how objects should be represented and (2) how to manage the line of reasoning that stretches from image data to object identity. An important part of the process of recognition { perhaps, almost all of it { involves assembling bits of image information into helpful groups. There is a wide variety of possible criteria by which these groups could be established { a set of edge points that has a symmetry could be one useful group; others might be a collection of pixels shaded in a particular way, or a set of pixels with coherent colour or texture. Discussing this process of grouping requires a detailed understanding of the relationship between what is seen in the image and what is actually out there in the world.

Authors and Affiliations

  • Computer Science Division, University of California at Berkeley, Berkeley, USA

    David A. Forsyth

  • G.E. Corporate Research and Development, Niskayuna, USA

    Joseph L. Mundy

  • Palermo University, C.I.T.C., Sicily, Italy

    Vito Gesú

  • Department of Engineering, University of Cambridge, Cambridge, UK

    Roberto Cipolla

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation