Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses.

The observation described above lead to the initiation of the DIRAC EC project in 2006. In 2010 a workshop was held, aimed to bring together researchers and students from different disciplines in order to present and discuss new approaches for identifying and reacting to unexpected events in information-rich environments. This book includes a summary of the achievements of the DIRAC project in chapter 1, and a collection of the papers presented in this workshop in the remaining parts."

Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses.

The observation described above lead to the initiation of the DIRAC EC project in 2006. In 2010 a workshop was held, aimed to bring together researchers and students from different disciplines in order to present and discuss new approaches for identifying and reacting to unexpected events in information-rich environments. This book includes a summary of the achievements of the DIRAC project in chapter 1, and a collection of the papers presented in this workshop in the remaining parts."

This book constitutes the refereed proceedings of the 24th Symposium of the German Association for Pattern Recognition, DAGM 2002, held in Zurich, Switzerland, in September 2002.The 74 revised papers presented were carefully reviewed and selected from more than 140 submissions. The papers address all current issues in pattern recognition. The book offers topical sections on tracking, segmentation, 3D shape, optical flow, recognition, and spherical images.

This volume contains the ?nal version of the papers originally presented at the second SMILE workshop 3D Structure from Multiple Images of Large-scale Environments, which was held on 1-2 July 2000 in conjunction with the Sixth European Conference in Computer Vision at Trinity College Dublin. The subject of the workshop was the visual acquisition of models of the 3D world from images and their application to virtual and augmented reality. Over the last few years tremendous progress has been made in this area. On the one hand important new insightshavebeenobtainedresultinginmore exibilityandnewrepresentations.Onthe other hand a number of techniques have come to maturity, yielding robust algorithms delivering good results on real image data. Moreover supporting technologies - such as digital cameras, computers, disk storage, and visualization devices - have made things possible that were infeasible just a few years ago. Opening the workshop was Paul Debevec s invited presentation on image-based modeling,rendering,andlighting.Hepresentedanumberoftechniquesforusingdigital images of real scenes to create 3D models, virtual camera moves, and realistic computer animations.Theremainderoftheworkshopwasdividedintothreesessions:Computation and Algorithms, Visual Scene Representations, and Extended Environments. After each session there was a panel discussion that included all speakers. These panel discussions were organized by Bill Triggs, Marc Pollefeys, and Tomas Pajdla respectively, who introduced the topics and moderated the discussion. Asubstantialpartoftheseproceedingsarethetranscriptsofthediscussionsfollowing each paper and the full panel sessions. These discussions were of very high quality and were an integral part of the workshop.

The contributions in this volume give an overview of state-of-the-art results presented at the Workshop on 3D Structure from Multiple Images of Lar- scale Environments (SMILE). This workshop was held in conjunction with the Fifth European Conference on Computer Vision 1998 in Freiburg, Germany. SMILE was a joint e ort of the European ACTS projects VANGUARD and PANORAMA and the Esprit projectCUMULI, all of which are involved in the analysis and reconstruction of 3D scenes from image sequences. The potential for 3D reconstructions of scenes and objects is tremendous. Much of the work reported here is to be seen especially against the background of a convergence between computer vision and computer graphics, and of a shift from signal-based to content-based image analysis in telecommunications. - cordingly, the requirementsfor 3D modelsand acquisitionsystemsarealso shi- ing. Visualization rather than mensuration is the primary issue. The perceptual qualityofthe models, the?exibility ofthe acquisition, andthe costofthe system are three driving forces in the search for new methods. The last few years have seen important steps toward genuine ?exibility. A case in point is the use of multiple images to generate 3D models, without an explicit knowledgeof the relativeposition of the camerasor the camera settings. The same developments also hold good promise to make 3D acquisition cheaper and more widely availab

This book discusses methods to extract 3-dimensional (3D) models from plain images. In particular, the 3D information is obtained from images for which the camera parameters are unknown. The principles underlying such uncalibrated structure-from-motion methods are outlined. First, a short review of 3D acquisition technologies puts such methods in a wider context, and highlights their important advantages. Then, the actual theory behind this line of research is given. The authors have tried to keep the text maximally self-contained, therefore also avoiding to rely on an extensive knowledge of the projective concepts that usually appear in texts about self-calibration 3D methods. Rather, mathematical explanations that are more amenable to intuition are given. The explanation of the theory includes the stratification of reconstructions obtained from image pairs as well as metric reconstruction on the basis of more than 2 images combined with some additional knowledge about the cameras used. Readers who want to obtain more practical information about how to implement such uncalibrated structure-from-motion pipelines may be interested in two more Foundations and Trends issues written by the same authors. Together with this issue they can be read as a single tutorial on the subject.

In the arts and sciences, as well as in our daily lives, symmetry has made a profound and lasting impact. Likewise, a computational treatment of symmetry and group theory (the ultimate mathematical formalization of symmetry) has the potential to play an important role in computational sciences. Though the term Computational Symmetry was formally defined a decade ago, referring to algorithmic treatment of symmetries, seeking symmetry from digital data has been attempted for over four decades. Computational symmetry on real world data turns out to be challenging enough that, after decades of effort, a fully automated symmetry-savvy system remains elusive for real world applications. Computational Symmetry in Computer Vision and Computer Graphics provides a succinct summary of the relevant mathematical theory, a historic perspective of some important symmetry-related ideas, a partial yet timely report on the state of the arts symmetry detection algorithms along with its first quantitative benchmark, a diverse set of real world applications, suggestions for future directions and a comprehensive reference list.The recent resurgence in interest in computational symmetry for computer vision and computer graphics applications has shown promising results. Computational Symmetry in Computer Vision and Computer Graphics illustrates how far we have come and where the future challenges and opportunities may lie.