The image taken by a moving camera changes with time. These image motions contain information about the motion of the camera and about the shapes of the objects in the field of view. There are two main types of image motion, finite displacements and image velocities. Finite displacements are described by the point correspondences between two images of the same scene taken from different positions. Image velocities are the velocities of the points in the image as they move over the projection surface. Reconstruction is the task of obtaining from the image-motions information about the camera motion or about the shapes of objects in the field of view. In this book the theory underlying reconstruction is described. Reconstruction from image motion is the subject matter of two different sci entific disciplines, photogrammetry and computer vision. In photogrammetry the accuracy of reconstruction is emphasised; in computer vision the emphasis is on methods for obtaining information from images in real time in order to guide a mechanical device such as a robot arm or an automatic vehicle. This book arises from recent work carried out in computer vision. Computer vision is a young field but it is developing rapidly. The earliest papers on reconstruction in the computer vision literature date back only to the mid 1970s. As computer vision develops, the mathematical techniques applied to the analysis of recon struction become more appropriate and more powerful.

Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. Explains the underlying principles of MRI reconstruction, along with the latest research“/li> Gives example codes for some of the methods presented Includes updates on the latest developments, including compressed sensing, tensor-based reconstruction and machine learning based reconstruction

The issue 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.

Motion and Structure from Image Sequences is invaluable reading for researchers, graduate students, and practicing engineers dealing with computer vision. It presents a balanced treatment of the theoretical and practical issues, including very recent results - some of which are published here for the first time. The topics covered in detail are: - image matching and optical flow computation - structure from stereo - structure from motion - motion estimation - integration of multiple views - motion modeling and prediction Aspects such as uniqueness of the solution, degeneracy conditions, error analysis, stability, optimality, and robustness are also investigated. These details together with the fact that the algorithms are accessible without necessarily studying the rest of the material, make this book particularly attractive to practitioners.

Image processing and machine vision are fields of renewed interest in the commercial market. People in industry, managers, and technical engineers are looking for new technologies to move into the market. Many of the most promising developments are taking place in the field of image processing and its applications. The book offers a broad coverage of advances in a range of topics in image processing and machine vision.

Computer Vision is a rapidly growing field of research investigating computational and algorithmic issues associated with image acquisition, processing, and understanding. It serves tasks like manipulation, recognition, mobility, and communication in diverse application areas such as manufacturing, robotics, medicine, security and virtual reality. This volume contains a selection of papers devoted to theoretical foundations of computer vision covering a broad range of fields, e.g. motion analysis, discrete geometry, computational aspects of vision processes, models, morphology, invariance, image compression, 3D reconstruction of shape. Several issues have been identified to be of essential interest to the community: non-linear operators; the transition between continuous to discrete representations; a new calculus of non-orthogonal partially dependent systems.

This two-volume set constitutes the refereed proceedings of the 5th European Conference on Computer Vision, ECCV'98, held in Freiburg, Germany, in June 1998. The 42 revised full papers and 70 revised posters presented were carefully selected from a total of 223 papers submitted. The papers are organized in sections on multiple-view geometry, stereo vision and calibration, geometry and invariances, structure from motion, colour and indexing, grouping and segmentation, tracking, condensation, matching and registration, image sequences and video, shape and shading, motion and flow, medical imaging, appearance and recognition, robotics and active vision, and motion segmentation.