Motion analysis is central to both human and machine vision. It involves the interpretation of image data over time and is crucial for a range of motion tasks suchasobstacledetection,depthestimation,videoanalysis,sceneinterpretation, videocompressionandotherapplications. Motionanalysisis unsolvedbecauseit requires modeling of the complicated relationships between the observed image data and the motion of objects and motion patterns (e. g. , falling rain) in the visual scene. The Dagstuhl Seminar 08291 on Statistical and Geometrical Approaches to Visual Motion Analysis was held during July 13¿18, 2008 at the International Conference and Research Center (IBFI), Schloss Dagstuhl, near Wadern in G- many. The workshop focused on critical aspects of motion analysis, including motion segmentation, the modeling of motion patterns and the di?erent te- niques used. These techniques include variationalapproaches,level set methods, probabilistic models, graph cut approaches, factorization techniques, and neural networks. All these techniques can be subsumed within statistical and geomet- cal frameworks. We further involved experts in the study of human and primate vision. Primatevisualsystemsareextremely sophisticatedat processingmotion, thus there is much to be learnt from studying them. In particular, we discussed how to relate the computational models of primate visual systems to those - veloped for machine vision. In total, 15 papers were accepted for these proceedings after the workshop. We werecarefulto ensurea high standardof qualityfor the accepted papers. All submissions were double-blind reviewed by at least two experts.

Optical Flow and Extensions.- Discrete-Continuous Optimization for Optical Flow Estimation.- An Improved Algorithm for TV-L 1 Optical Flow.- An Evaluation Approach for Scene Flow with Decoupled Motion and Position.- An Affine Optical Flow Model for Dynamic Surface Reconstruction.- Deinterlacing with Motion-Compensated Anisotropic Diffusion.- Human Motion Modeling.- Real-Time Synthesis of Body Movements Based on Learned Primitives.- 2D Human Pose Estimation in TV Shows.- Recognition and Synthesis of Human Movements by Parametric HMMs.- Recognizing Human Actions by Their Pose.- Biological and Statistical Approaches.- View-Based Approaches to Spatial Representation in Human Vision.- Combination of Geometrical and Statistical Methods for Visual Navigation of Autonomous Robots.- Motion Integration Using Competitive Priors.- Alternative Approaches to Motion Analysis.- Derivation of Motion Characteristics Using Affine Shape Adaptation for Moving Blobs.- Comparison of Point and Line Features and Their Combination for Rigid Body Motion Estimation.- The Conformal Monogenic Signal of Image Sequences.