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  • © 2006

Toward Category-Level Object Recognition

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

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

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Table of contents (30 chapters)

  1. Front Matter

  2. Introduction

    1. Front Matter

      Pages 1-1
    2. Dataset Issues in Object Recognition

      • J. Ponce, T. L. Berg, M. Everingham, D. A. Forsyth, M. Hebert, S. Lazebnik et al.
      Pages 29-48
    3. Industry and Object Recognition: Applications, Applied Research and Challenges

      • Yutaka Hirano, Christophe Garcia, Rahul Sukthankar, Anthony Hoogs
      Pages 49-64
  3. Recognition of Specific Objects

    1. Front Matter

      Pages 65-65
    2. What and Where: 3D Object Recognition with Accurate Pose

      • Iryna Gordon, David G. Lowe
      Pages 67-82
    3. 3D Object Modeling and Recognition from Photographs and Image Sequences

      • Fred Rothganger, Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
      Pages 105-126
    4. Video Google: Efficient Visual Search of Videos

      • Josef Sivic, Andrew Zisserman
      Pages 127-144
    5. Simultaneous Object Recognition and Segmentation by Image Exploration

      • Vittorio Ferrari, Tinne Tuytelaars, Luc Van Gool
      Pages 145-169
  4. Recognition of Object Categories

    1. Front Matter

      Pages 171-171
    2. Synergistic Face Detection and Pose Estimation with Energy-Based Models

      • Margarita Osadchy, Yann Le Cun, Matthew L. Miller
      Pages 196-206
    3. Generic Visual Categorization Using Weak Geometry

      • Gabriela Csurka, Christopher R. Dance, Florent Perronnin, Jutta Willamowski
      Pages 207-224
    4. Components for Object Detection and Identification

      • Bernd Heisele, Ivaylo Riskov, Christian Morgenstern
      Pages 225-237
    5. Cross Modal Disambiguation

      • Kobus Barnard, Keiji Yanai, Matthew Johnson, Prasad Gabbur
      Pages 238-257
    6. Translating Images to Words for Recognizing Objects in Large Image and Video Collections

      • Pınar Duygulu, Muhammet Baştan, David Forsyth
      Pages 258-276
    7. A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues

      • Peter Carbonetto, Gyuri Dorkó, Cordelia Schmid, Hendrik Kück, Nando de Freitas
      Pages 277-300
    8. Towards the Optimal Training of Cascades of Boosted Ensembles

      • S. Charles Brubaker, Jianxin Wu, Jie Sun, Matthew D. Mullin, James M. Rehg
      Pages 301-320

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.

Keywords

  • 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
  • algorithm analysis and problem complexity

Editors and Affiliations

  • Département d’Informatique, Ecole Normale Supérieure, Paris, France

    Jean Ponce

  • Carnegie Mellon University, Pittsburgh, USA

    Martial Hebert

  • GRAVIR-INRIA, Montbonnot, France

    Cordelia Schmid

  • Department of Engineering Science, University of Oxford, Oxford, UK

    Andrew Zisserman

Bibliographic Information

Buying options

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions