Frank R. Schmidt – författare

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.

Overthelastdecades,energyminimizationmethods havebecomeanestablished paradigm to resolve a variety of challenges in the ?elds of computer vision and pattern recognition. While traditional approaches to computer vision were often based on a heuristic sequence of processing steps and merely allowed very l- ited theoretical understanding of the respective methods, most state-of-the-art methods are nowadays based on the concept of computing solutions to a given problem by minimizing respective energies. This volume contains the papers presented at the 7th International Conf- ence on Energy Minimization Methods in Computer Vision and Pattern Rec- nition (EMMCVPR 2009), held at the University of Bonn, Germany, August 24-28, 2009. These papers demonstrate that energy minimization methods have become a mature ?eld of research spanning a broad range of areas from discrete graph theoretic approaches and Markov random ?elds to variational methods and partial di?erential equations. Application areas include image segmentation and tracking, shape optimization and registration, inpainting and image deno- ing, color and texture modeling, statistics and learning.Overall, we received 75 high-quality double-blind submissions. Based on the reviewer recommendations, 36paperswereselectedforpublication,18asoraland18asposterpresentations. Both oral and poster papers were attributed the same number of pages in the conference proceedings. Furthermore, we were delighted that three leading experts from the ?elds of computer vision and energy minimization, namely, Richard Hartley (C- berra, Australia), Joachim Weickert (Saarbruc .. ken, Germany) and Guillermo Sapiro(Minneapolis,USA)agreedtofurtherenrichtheconferencewithinspiring keynote lectures.

This book constitutes the refereed proceedings of the 8th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2011, held in St. Petersburg, Russia in July , 2011. The book presents 30 revised full papers selected from a total of 52 submissions. The book is divided in sections on discrete and continuous optimization, segmentation, motion and video, learning and shape analysis.