Advertisement

Shape, Contour and Grouping in Computer Vision

  • David A. Forsyth
  • Joseph L. Mundy
  • Vito di Gesú
  • Roberto Cipolla

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

Table of contents

  1. Front Matter
    Pages I-VIII
  2. Introduction

    1. David Forsyth, Joe Mundy
      Pages 3-8
  3. Shape

    1. Jean Ponce, Martha Cepeda, Sung-il Pae, Steve Sullivan
      Pages 31-57
  4. Shading

    1. David Juris Kriegman, Peter N. Belhumeur, Athinodoros S. Georghiades
      Pages 95-131
    2. Peter N. Belhumeur, David Juris Kriegman, Alan L. Yuille
      Pages 132-151
  5. Grouping

    1. Jitendra Malik, Jianbo Shi, Serge Belongie, Thomas Leung
      Pages 155-164
    2. Frederik Schaffalitzky, Andrew Zisserman
      Pages 165-181
    3. Rupert W. Curwen, Joe L. Mundy
      Pages 182-195
    4. Marc Proesmans, Luc Van Gool
      Pages 196-213
  6. Representation and Recognition

    1. Cordelia Schmid, Roger Mohr, Andrew Zisserman
      Pages 217-233
    2. Peter Tu, Richard Hartley, Tushar Saxena
      Pages 246-263
    3. Antonio Chella, Vito Di Gesù, Ignazio Infantino, Daniela Intravaia, Cesare Valenti
      Pages 264-274
  7. Statistics, Learning and Recognition

    1. Philip H. S. Torr
      Pages 277-301
    2. David Forsyth, John Haddon, Sergey Ioffe
      Pages 302-318
    3. Yann LeCun, Patrick Haffner, Léon Bottou, Yoshua Bengio
      Pages 319-345
  8. Back Matter
    Pages 347-347

About this book

Introduction

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.

Keywords

Computer Vision Image Processing Machine Learning Object Representation Pattern Recognition Shading cognition learning object recognition

Authors and affiliations

  • David A. Forsyth
    • 1
  • Joseph L. Mundy
    • 2
  • Vito di Gesú
    • 3
  • Roberto Cipolla
    • 4
  1. 1.Computer Science DivisionUniversity of California at BerkeleyBerkeleyUSA
  2. 2.G.E. Corporate Research and DevelopmentNiskayunaUSA
  3. 3.Palermo University, C.I.T.C.SicilyItaly
  4. 4.Department of EngineeringUniversity of CambridgeCambridgeUK

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-46805-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 1999
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-66722-3
  • Online ISBN 978-3-540-46805-9
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
Industry Sectors
Pharma
Materials & Steel
Automotive
Biotechnology
Finance, Business & Banking
Electronics
IT & Software
Telecommunications
Consumer Packaged Goods
Energy, Utilities & Environment
Aerospace
Engineering