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  • Conference proceedings
  • © 2004

Pattern Recognition

26th DAGM Symposium, August 30 - September 1, 2004, Proceedings

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3175)

Conference series link(s): DAGM: DAGM (German Association for Pattern Recognition) Symposium

Conference proceedings info: DAGM 2004.

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Table of contents (70 papers)

  1. Front Matter

  2. Learning

    1. Predictive Discretization During Model Selection

      • Harald Steck, Tommi S. Jaakkola
      Pages 1-8
    2. Adaptive Feature Selection in Image Segmentation

      • Volker Roth, Tilman Lange
      Pages 9-17
    3. Semi-supervised Kernel Regression Using Whitened Function Classes

      • Matthias O. Franz, Younghee Kwon, Carl Edward Rasmussen, Bernhard Schölkopf
      Pages 18-26
  3. Vision and Faces

    1. Efficient Approximations for Support Vector Machines in Object Detection

      • Wolf Kienzle, Gökhan Bakır, Matthias Franz, Bernhard Schölkopf
      Pages 54-61
    2. Efficient Face Detection by a Cascaded Support Vector Machine Using Haar-Like Features

      • Matthias Rätsch, Sami Romdhani, Thomas Vetter
      Pages 62-70
  4. Vision / Motion

    1. Differential Analysis of Two Model-Based Vehicle Tracking Approaches

      • Hendrik Dahlkamp, Arthur E. C. Pece, Artur Ottlik, Hans-Hellmut Nagel
      Pages 71-78
    2. Hybrid Model-Based Estimation of Multiple Non-dominant Motions

      • Arne Jacobs, Thorsten Hermes, Otthein Herzog
      Pages 87-94
  5. Biologically Motivated Approaches

    1. A Model of Motion, Stereo, and Monocular Depth Perception

      • Pierre Bayerl, Heiko Neumann
      Pages 95-102
    2. POI Detection Using Channel Clustering and the 2D Energy Tensor

      • Michael Felsberg, Gösta Granlund
      Pages 103-110
  6. Segmentation

    1. Hierarchical Image Segmentation Based on Semidefinite Programming

      • Jens Keuchel, Matthias Heiler, Christoph Schnörr
      Pages 120-128
    2. Fast Random Sample Matching of 3d Fragments

      • Simon Winkelbach, Markus Rilk, Christoph Schönfelder, Friedrich M. Wahl
      Pages 129-136

Other Volumes

  1. Pattern Recognition

Keywords

  • Resolution
  • Support Vector Machine
  • algorithms
  • classification
  • computer vision
  • face recognition
  • feature extraction
  • image analysis
  • image sequences
  • multi-camera systems
  • optimization
  • pattern recognition
  • robot
  • robotics
  • speech recognition
  • algorithm analysis and problem complexity

Editors and Affiliations

  • Max Planck Institute for Biological Cybernetics,, Tübingen, Germany

    Carl Edward Rasmussen

  • Max-Planck-Institute for Biological Cybernetics,, Tübingen, Germany

    Heinrich H. Bülthoff

  • MPI for Biological Cybernetics, Tübingen, Germany

    Bernhard Schölkopf

  • School of Psychology, University of Wales, Bangor, UK

    Martin A. Giese

Bibliographic Information

  • Book Title: Pattern Recognition

  • Book Subtitle: 26th DAGM Symposium, August 30 - September 1, 2004, Proceedings

  • Editors: Carl Edward Rasmussen, Heinrich H. Bülthoff, Bernhard Schölkopf, Martin A. Giese

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/b99676

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2004

  • Softcover ISBN: 978-3-540-22945-2Published: 23 August 2004

  • eBook ISBN: 978-3-540-28649-3Published: 10 August 2004

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVIII, 586

  • Topics: Automated Pattern Recognition, Computer Vision, Artificial Intelligence, Computer Graphics, Algorithms

Buying options

eBook USD 39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
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