Synopses & Reviews
Level set methods are emerging techniques for representing, deforming, and recovering structures in an arbitrary dimension across different fields (mathematics, fluid dynamics, graphics, imaging, vision, etc.). Advances in numerical analysis have led to computationally efficient tools for computing and analyzing interface motion within level set frameworks in a host of application settings. This authoritative edited survey provides readers with the state-of-the-art in applying level set techniques in the imaging, vision, and graphics domains, presenting thematically grouped chapters contributed by leading experts from both industry and academia. The work bridges the theoretical foundations of level set methods with the latest significant applications. It will assist readers with both the technical aspects of the field as well as its practical ramifications for areas like medical imaging, computer animation, film restoration, video surveillance, visual inspection, and a range of scientific and engineering disciplines. Topics and features: * Covers comprehensively the applications of imaging, vision, & graphics * Includes a helpful introductory survey chapter on level set methods * Provides a complete overview of concepts and advanced technologies in the field * Describes leading-edge research, providing insight into a variety of potential avenues for problem solving * Supplies numerous implementations, examples, and relevant and useful experimental results This essential resource carefully integrates the theoretical foundations of level set methods with their actual performance capabilities. Its clarity of organization and approach makes the book accessible for researchers and professionals working in the areas of vision, graphics, image processing, robotics, mathematics, and computational geometry.>
Synopsis
Includes bibliographical references (p. [481]-513) and index.
Synopsis
Introduction Imageprocessing, computervisionandcomputergraphicsarenowestablished - search areas. Pattern recognition and arti?cial intelligence were the origins of the explorationofthespace ofimages.Simplistic digitaltechniquesusedatthe beg- ning of 60 s for gray image processing operations have been now replaced with a complex mathematical framework that aims to exploit and understand images in two and three dimensions. Advances in computing power continue to make the use and processing of visual information an important part of our lives. The evolution of these techniques was a natural outcome of the need to p- cess an emerging informationspace, the space of natural images. Images in space and time are now a critical part of many human activities. First, pictures and now video streams were used to eternalize small and signi?cant moments of our life. Entertainment including movies, TV-programs and video games are part of our every-day life where capturing, editing, understanding and transmitting images are issues to be dealt with. The medical sector is also a major area for the use of images. The evolution of the acquisition devices led to new ways of capturing information, not visible by the human eye. Medical imaging is probably the most established market for processing visual information 405]. Visualization of c- plex structures and automated processing towards computer aided diagnosis is used more and more by the physicians in the diagnostic process. Safety and se- rity are also important areas where images and video play a signi?cant role 432]."
Synopsis
The topic of level sets is currently very timely and useful for creating realistic 3-D images and animations. They are powerful numerical techniques for analyzing and computing interface motion in a host of application settings. In computer vision, it has been applied to stereo and segmentation, whereas in graphics it has been applied to the postproduction process of in-painting and 3-D model construction. Osher is co-inventor of the Level Set Methods, a pioneering framework introduced jointly with James Sethian from the University of Berkeley in 1998. This methodology has been used up to now to provide solutions to a wide application range not limited to image processing, computer vision, robotics, fluid mechanics, crystallography, lithography, and computer graphics. The topic is of great interest to advanced students, professors, and R&D professionals working in the areas of graphics (post-production), video-based surveillance, visual inspection, augmented reality, document image processing, and medical image processing. These techniques are already employed to provide solutions and products in the industry (Cognitech, Siemens, Philips, Focus Imaging). An essential compilation of survey chapters from the leading researchers in the field, emphasizing the applications of the methods. This book can be suitable for a short professional course related with the processing of visual information.
Synopsis
Here is, for the first time, a book that clearly explains and applies new level set methods to problems and applications in computer vision, graphics, and imaging. It is an essential compilation of survey chapters from the leading researchers in the field. The applications of the methods are emphasized.
Table of Contents
* Level set methods * Deformable models * Fast methods for implicit active contour models * Fast edge integration * Variational snake theory * Multiplicative denoising and deblurring * Total varation minimization for scalar/vector regularization * Morphological global reconstruction and levelings * Fast marching techniques for visual grouping and segmentation * Multiphase object detection and image segmentation * Adaptive segmentation of vector-valued images * Mumford-Shah for segmentation and stereo * Shape analysis toward model-based segmentation * Joint image registration and segmentation * Image alignment * Variational principles in optical flow estimation and tracking * Region matching and tracking under deformations or occlusions * Computational stereo * Visualization, analysis and shape reconstruction of sparse data * Variational problems and partial differential equations on implicit surfaces * Knowledge-based segmentation of medical images * Topology preserving geometric deformable models for brain reconstruction * Editing geometric models * Simulating natural phenomena