Toward Category-Level Object Recognition

  • Jean Ponce
  • Martial Hebert
  • Cordelia Schmid
  • Andrew Zisserman

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4170)

Table of contents

  1. Front Matter
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. J. Ponce, T. L. Berg, M. Everingham, D. A. Forsyth, M. Hebert, S. Lazebnik et al.
      Pages 29-48
    3. Yutaka Hirano, Christophe Garcia, Rahul Sukthankar, Anthony Hoogs
      Pages 49-64
  3. Recognition of Specific Objects

    1. Front Matter
      Pages 65-65
    2. Iryna Gordon, David G. Lowe
      Pages 67-82
    3. Fred Rothganger, Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
      Pages 105-126
    4. Josef Sivic, Andrew Zisserman
      Pages 127-144
    5. Vittorio Ferrari, Tinne Tuytelaars, Luc Van Gool
      Pages 145-169
  4. Recognition of Object Categories

    1. Front Matter
      Pages 171-171
    2. Margarita Osadchy, Yann Le Cun, Matthew L. Miller
      Pages 196-206
    3. Gabriela Csurka, Christopher R. Dance, Florent Perronnin, Jutta Willamowski
      Pages 207-224
    4. Bernd Heisele, Ivaylo Riskov, Christian Morgenstern
      Pages 225-237
    5. Kobus Barnard, Keiji Yanai, Matthew Johnson, Prasad Gabbur
      Pages 238-257
    6. Pınar Duygulu, Muhammet Baştan, David Forsyth
      Pages 258-276
    7. Peter Carbonetto, Gyuri Dorkó, Cordelia Schmid, Hendrik Kück, Nando de Freitas
      Pages 277-300
    8. S. Charles Brubaker, Jianxin Wu, Jie Sun, Matthew D. Mullin, James M. Rehg
      Pages 301-320
    9. Shimon Ullman, Boris Epshtein
      Pages 321-344
    10. Antonio Torralba, Kevin P. Murphy, William T. Freeman
      Pages 345-361
    11. Kevin Murphy, Antonio Torralba, Daniel Eaton, William Freeman
      Pages 382-400
    12. Sachin Gangaputra, Donald Geman
      Pages 401-420
  5. Recognition of Object Categories with Geometric Relations

    1. Front Matter
      Pages 421-421
    2. Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
      Pages 423-442
    3. Rob Fergus, Pietro Perona, Andrew Zisserman
      Pages 443-461
    4. David Crandall, Pedro Felzenszwalb, Daniel Huttenlocher
      Pages 462-482
    5. Alexander C. Berg, Jitendra Malik
      Pages 483-507
    6. Bastian Leibe, Ales Leonardis, Bernt Schiele
      Pages 508-524
    7. Timothy F. Cootes, David Cristinacce, Vladimir Petrović
      Pages 525-542
  6. Joint Recognition and Segmentation

    1. Front Matter
      Pages 543-543
    2. Zhuowen Tu, Xiangrong Chen, Alan Yuille, Song Chun Zhu
      Pages 545-576
    3. Michalis K. Titsias, Christopher K. I. Williams
      Pages 577-595
    4. M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
      Pages 596-616
  7. Back Matter

About this book


Although research in computer vision for recognizing 3D objects in photographs dates back to the 1960s, progress was relatively slow until the turn of the millennium, and only now do we see the emergence of effective techniques for recognizing object categories with different appearances under large variations in the observation conditions. Tremendous progress has been achieved in the past five years, thanks largely to the integration of new data representations, such as invariant semi-local features, developed in the computer vision community with the effective models of data distribution and classification procedures developed in the statistical machine-learning community.

This volume is a post-event proceedings volume and contains selected papers based on presentations given, and vivid discussions held, during two workshops held in Taormina in 2003 and 2004. The main goals of these two workshops were to promote the creation of an international object recognition community, with common datasets and evaluation procedures, to map the state of the art and identify the main open problems and opportunities for synergistic research, and to articulate the industrial and societal needs and opportunities for object recognition research worldwide.

The 30 thoroughly revised papers presented are organized in the following topical sections: recognition of specific objects, recognition of object categories, recognition of object categories with geometric relations, and joint recognition and segmentation.


3D 3D objects Textur classification cognition cognitive computer vision cognitive systems computational attention computer vision computer vision systems face detection feature extraction learning modeling object recognition

Editors and affiliations

  • Jean Ponce
    • 1
  • Martial Hebert
    • 2
  • Cordelia Schmid
    • 3
  • Andrew Zisserman
    • 4
  1. 1.Département d’InformatiqueEcole Normale SupérieureParisFrance
  2. 2.Carnegie Mellon UniversityPittsburghUSA
  3. 3.GRAVIR-INRIAMontbonnotFrance
  4. 4.Department of Engineering ScienceUniversity of OxfordOxfordUK

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