Computer Vision - ECCV 2000

1. Front Matter

   Pages I-XVIII

   PDF

2. Recognition & Modelling I

   1. Non-linear Bayesian Image Modelling

      • Christopher M. Bishop, John M. Winn

      Pages 3-17

   2. Unsupervised Learning of Models for Recognition

      • M. Weber, M. Welling, P. Perona

      Pages 18-32

   3. Learning over Multiple Temporal Scales in Image Databases

      • Nuno Vasconcelos, Andrew Lippman

      Pages 33-47

   4. Colour Image Retrieval and Object Recognition Using the Multimodal Neighbourhood Signature

      • Jiri George Matas, Dimitri Koubaroulis, Josef Kittler

      Pages 48-64

3. Stereoscopic Vision

   1. Approximate N-View Stereo

      • Kiriakos N. Kutulakos

      Pages 67-83

   2. A Minimal Set of Constraints for the Trifocal Tensor

      • Nikos Canterakis

      Pages 84-99

   3. Intrinsic Images for Dense Stereo Matching with Occlusions

      • César Silva, José Santos-Victor

      Pages 100-114

4. Recognition & Modelling / Stereoscopic Vision / Texture, Shading, & Colour

   1. Local Scale Selection for Gaussian Based Description Techniques

      • Olivier Chomat, Vincent Colin de Verdiere, Daniela Hall, James L. Crowley

      Pages 117-134

   2. Anti-Faces for Detection

      • Daniel Keren, Margarita Osadchy, Craig Gotsman

      Pages 134-148

   3. Combining Elastic and Statistical Models of Appearance Variation

      • T. F. Cootes, C. J. Taylor

      Pages 149-163

   4. Object Recognition Using Coloured Receptive Fields

      • Daniela Hall, Vincent Colin de Verdiere, James L. Crowley

      Pages 164-177

   5. Learning Similarity for Texture Image Retrieval

      • Guodong Guo, Stan Z. Li, Kap Luk Chan

      Pages 178-190

   6. Parametric View-Synthesis

      • Miles E. Hansard, Bernard F. Buxton

      Pages 191-202

   7. Least Committment Graph Matching by Evolutionary Optimisation

      • Richard Myers, Edwin R. Hancock

      Pages 203-219

   8. Predicting Disparity Windows for Real-Time Stereo

      • Jane Mulligan, Kostas Daniilidis

      Pages 220-235

   9. On the Estimation of the Fundamental Matrix: A Convex Approach to Constrained Least-Squares

      • G. Chesi, A. Garulli, A. Vicino, R. Cipolla

      Pages 236-250

   10. Velocity-Guided Tracking of Deformable Contours in Three Dimensional Space

       • Reuven Zaritsky, Natan Peterfreund, Nahum Shimkin

       Pages 251-266

   11. Model Based Pose Estimator Using Linear-Programming

       • Moshe Ben-Ezra, Shmuel Peleg, Michael Werman

       Pages 267-281

   12. Measuring the Self-Consistency of Stereo Algorithms

       • Yvan G. Leclerc, Q.-Tuan Luong, P. Fua

       Pages 282-298

Ten years ago, the inaugural European Conference on Computer Vision was held in Antibes, France. Since then, ECCV has been held biennially under the auspices of the European Vision Society at venues around Europe. This year, the privilege of organizing ECCV 2000 falls to Ireland and it is a signal honour for us to host what has become one of the most important events in the calendar of the computer vision community. ECCV is a single-track conference comprising the highest quality, previously unpublished, contributed papers on new and original research in computer vision. This year, 266 papers were submitted and, following a rigorous double-blind review process, with each paper being reviewed by three referees, 116 papers were selected by the Programme Committee for presentation at the conference. The venue for ECCV 2000 is the University of Dublin, Trinity College. - unded in 1592, it is Ireland’s oldest university and has a proud tradition of scholarship in the Arts, Humanities, and Sciences, alike. The Trinity campus, set in the heart of Dublin, is an oasis of tranquility and its beautiful squares, elegant buildings, and tree-lined playing- elds provide the perfect setting for any conference.

• 3-D vision
• Stereo
• computer vision
• image processing
• object recognition
• pattern recognition
• robot
• robot systems
• robot vision
• shading
• vision systems
• visual motion
• visual perception

• Department of Robotics, Brain, and Cognitive Sciences, Italian Institute of Technology, Genoa, Italy

  David Vernon

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