Complex Motion

1. Front Matter

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2. Optical Flow Estimation from Monogenic Phase

   • Michael Felsberg

   Pages 1-13

3. Optimal Filters for Extended Optical Flow

   • Hanno Scharr

   Pages 14-29

4. Wiener-Optimized Discrete Filters for Differential Motion Estimation

   • Kai Krajsek, Rudolf Mester

   Pages 30-41

5. Boundary Characterization Within the Wedge-Channel Representation

   • Ullrich Köthe

   Pages 42-53

6. Multiple Motion Estimation Using Channel Matrices

   • Per-Erik Forssén, Hagen Spies

   Pages 54-65

7. Divide-and-Conquer Strategies for Estimating Multiple Transparent Motions

   • Cicero Mota, Ingo Stuke, Til Aach, Erhardt Barth

   Pages 66-77

8. Towards a Multi-camera Generalization of Brightness Constancy

   • Hanno Scharr

   Pages 78-90

9. Complex Motion in Environmental Physics and Live Sciences

   • Bernd Jähne

   Pages 91-103

10. Bayesian Approaches to Motion-Based Image and Video Segmentation

    • Daniel Cremers

    Pages 104-123

11. On Variational Methods for Fluid Flow Estimation

    • Paul Ruhnau, Jing Yuan, Christoph Schnörr

    Pages 124-145

12. Motion Based Estimation and Representation of 3D Surfaces and Boundaries

    • Klas Nordberg, Fredrik Vikstén

    Pages 146-164

13. A Probabilistic Formulation of Image Registration

    • Christoph Strecha, Rik Fransens, Luc Van Gool

    Pages 165-176

14. Myocardial Motion and Strain Rate Analysis from Ultrasound Sequences

    • Michael Sühling, Muthuvel Arigovindan, Christian Jansen, Patrick Hunziker, Michael Unser

    Pages 177-189

15. Determining the Translational Speed of a Camera from Time-Varying Optical Flow

    • Anton van den Hengel, Wojciech Chojnacki, Michael J. Brooks

    Pages 190-197

16. A Robust Approach for Ego-Motion Estimation Using a Mobile Stereo Platform

    • Hernán Badino

    Pages 198-208

17. Robust Monocular Detection of Independent Motion by a Moving Observer

    • Felix Woelk, Reinhard Koch

    Pages 209-222

18. Tracking Complex Objects Using Graphical Object Models

    • Leonid Sigal, Ying Zhu, Dorin Comaniciu, Michael Black

    Pages 223-234

19. Back Matter

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The world we live in is a dynamic one: we explore it by moving through it, and many of the objects which we are interested in are also moving. Tra?c, for instance, is an example of a domain where detecting and processing visual motion is of vital interest, both in a metaphoric as well as in a purely literal sense. Visual communication is another important example of an area of science which is dominated by the need to measure, understand, and represent visual motion in an e?cient way. Visual motion is a subject of research which forces the investigator to deal withcomplexity;complexityinthesenseoffacinge?ectsofmotioninaverylarge diversity of forms, starting from analyzing simple motion in a changing envir- ment (illumination, shadows, . . . ), under adverse observation conditions, such as bad signal-to-noiseratio (low illumination, small-scaleprocesses, low-dosex-ray, etc. ), covering also multiple motions of independent objects, occlusions, and - ing as far as dealing with objects which are complex in themselves (articulated objects such as bodies of living beings). The spectrum of problems includes, but does not end at, objects which are not ‘bodies’ at all, e. g. , when anal- ing ?uid motion, cloud motion, and so on. Analyzing the motion of a crowd in a shopping mall or in an airport is a further example that implies the need to struggleagainsttheproblemsinducedbycomplexity.

• 3D
• 3D motion
• Stereo
• camera grid
• classification
• computer vision
• feature extraction
• image analysis
• image reconstruction
• image segmentation
• image sequences
• motion estimation
• motion tracking
• object recognition
• pattern recogni
• algorithm analysis and problem complexity

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