Biomedical image analysis has become a major aspect of engineering sciences, and radiology in particular has become a dominant player in the field. Recent developments have made it possible to use biomedical imaging to view the human body from an anatomical or physiological perspective in a non-invasive fashion. Computer-aided diagnosis consists of developing algorithms and intelligent software components that can automatically process images and spot potential irregularities in the health chain.

The aim of this book is to explain the process of biomedical imaging, from image acquisition to automated diagnosis. This process consists of three thematic areas. The first is dedicated to the acquisition process and the underlying properties of images from a physics-oriented perspective. The second part addresses the dominant state-of-the-art methodologies behind content extraction and interpretation of medical images. The third section presents an application-based example, which develops solutions to address the particular needs of various diagnoses.

This complete volume is an exceptional tool for radiologists, research scientists, senior undergraduate and graduate students in health sciences and engineering, and university professors. This book offers a unique guide to the entire chain of biomedical imaging, explaining how image formation is done, and how the most appropriate algorithms are used to address demands and diagnoses.

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.

The latest generation of visual surveillance systems have adopted recent technological developments in acquisition and communications. These advances have not so much changed the nature of surveillance as extended its reach and reliability. Fundamentally, systems remain relatively unintelligent with human operators remaining central to the threat assessment and response planning procedures found in CCTV installations. Nonetheless, the availability of high-performance computing platforms will ensure that cycle-hungry intellectual property gestating in academic and industrial research programs will have a major impact on the next generation of products. Video-Based Surveillance Systems: Computer Vision and Distributed Processing, surveys works in progress in laboratories from around the world. The first part of the book present the most recent trends in the industrial world including real-time systems for monitoring of indoor and outdoor environments, society infrastructures such as subways and motorways, retail stores and aerial surveillance. Part Two explores current best practices in a chain of algorithms required to perform robust and accurate real-time tracking for motion detection involving rapid and frequent lighting changes, the establishment of accurate temporally consistent object trajectories particularly in crowded scenes, and the classification of object types. Part Three contains contributions which attempt to analyze events unfolding in a monitored scheme. The last part reviews distributed intelligent architectures which are likely to exploit three key recent technological developments in light-weight distributed computing methodologies, and intelligent sensors. Sucharchitectures, in which signal analysis is moving towards sensing devices, can exploit the reduced bandwidth requirements of transmitting knowledge rather than pixels. Video-Based Surveillance Systems: Computer Vision and Distributed Processing provides timely information for professionals working in the areas of surveillance, image processing, computer vision, digital signal processing and telecommunications.

Abstract Biological vision is a rather fascinating domain of research. Scientists of various origins like biology, medicine, neurophysiology, engineering, math ematics, etc. aim to understand the processes leading to visual perception process and at reproducing such systems. Understanding the environment is most of the time done through visual perception which appears to be one of the most fundamental sensory abilities in humans and therefore a significant amount of research effort has been dedicated towards modelling and repro ducing human visual abilities. Mathematical methods play a central role in this endeavour. Introduction David Marr's theory v DEGREESas a pioneering step tov DEGREESards understanding visual percep tion. In his view human vision was based on a complete surface reconstruction of the environment that was then used to address visual subtasks. This approach was proven to be insufficient by neuro-biologists and complementary ideas from statistical pattern recognition and artificial intelligence were introduced to bet ter address the visual perception problem. In this framework visual perception is represented by a set of actions and rules connecting these actions. The emerg ing concept of active vision consists of a selective visual perception paradigm that is basically equivalent to recovering from the environment the minimal piece information required to address a particular task of interest."

Monitoring of public and private sites has increasingly become a very sensitive issue resulting in a patchwork of privacy laws varying from country to country -though all aimed at protecting the privacy of the citizen. It is important to remember, however, that monitoring and vi sual surveillance capabilities can also be employed to aid the citizen. The focus of current development is primarily aimed at public and cor porate safety applications including the monitoring of railway stations, airports, and inaccessible or dangerous environments. Future research effort, however, has already targeted citizen-oriented applications such as monitoring assistants for the aged and infirm, route-planning and congestion-avoidance tools, and a range of environment al monitoring applications. The latest generation of surveillance systems has eagerly adopted re cent technological developments to produce a fully digital pipeline of digital image acquisition, digital data transmission and digital record ing. The resultant surveillance products are highly-fiexihle, capahle of generating forensic-quality imagery, and ahle to exploit existing Internet and wide area network services to provide remote monitoring capability.

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.

Abstract Biological vision is a rather fascinating domain of research. Scientists of various origins like biology, medicine, neurophysiology, engineering, math ematics, etc. aim to understand the processes leading to visual perception process and at reproducing such systems. Understanding the environment is most of the time done through visual perception which appears to be one of the most fundamental sensory abilities in humans and therefore a significant amount of research effort has been dedicated towards modelling and repro ducing human visual abilities. Mathematical methods play a central role in this endeavour. Introduction David Marr's theory v DEGREESas a pioneering step tov DEGREESards understanding visual percep tion. In his view human vision was based on a complete surface reconstruction of the environment that was then used to address visual subtasks. This approach was proven to be insufficient by neuro-biologists and complementary ideas from statistical pattern recognition and artificial intelligence were introduced to bet ter address the visual perception problem. In this framework visual perception is represented by a set of actions and rules connecting these actions. The emerg ing concept of active vision consists of a selective visual perception paradigm that is basically equivalent to recovering from the environment the minimal piece information required to address a particular task of interest."

The two volume set LNCS 4841 and LNCS 4842 constitutes the refereed proceedings of the Third International Symposium on Visual Computing, ISVC 2007, held in Lake Tahoe, NV, USA in November 2007.

The 77 revised full papers and 42 poster papers presented together with 32 full and 5 poster papers of 6 special tracks were carefully reviewed and selected from more than 270 submissions. The papers cover the four main areas of visual computing: vision, graphics, visualization, and virtual reality. There 6 additional special tracks address issues such as intelligent algorithms for smart monitoring of complex environments, object recognition, image databases, algorithms for the understanding of dynamics in complex and cluttered scenes, medical data analysis, and soft computing in image processing. The papers of this volume are organized in topical sections on motion and tracking, segmentation, feature extraction, classification, intelligent algorithms for smart monitoring of shape/recognition, image databases, soft computing in image processing, and posters.

The two volume set LNCS 4841 and LNCS 4842 constitutes the refereed proceedings of the Third International Symposium on Visual Computing, ISVC 2007, held in Lake Tahoe, NV, USA in November 2007.

The 77 revised full papers and 42 poster papers presented together with 32 full and 5 poster papers of 6 special tracks were carefully reviewed and selected from more than 270 submissions. The papers cover the four main areas of visual computing: vision, graphics, visualization, and virtual reality. There 6 additional special tracks address issues such as intelligent algorithms for smart monitoring of complex environments, object recognition, image databases, algorithms for the understanding of dynamics in complex and cluttered scenes, medical data analysis, and soft computing in image processing. The papers of this volume are organized in topical sections on motion and tracking, computer graphics, virtual reality, medical data analysis, calibration/reconstruction, visualization, computer vision applications, algorithms for the understanding, face reconstruction and processing, object recognition, and shape/motion/tracking.

This book constitutes the refereed proceedings of the First International Conference on Scale Space Methods and Variational Methods in Computer Vision, SSVM 2007, emanated from the joint edition of the 4th International Workshop on Variational, Geometric and Level Set Methods in Computer Vision, VLSM 2007 and the 6th International Conference on Scale Space and PDE Methods in Computer Vision, Scale-Space 2007, held in Ischia Italy, May/June 2007.

Computational visual perception seeks to reproduce human vision through the combination of visual sensors, artificial intelligence, and computing. To this end, computer vision tasks are often reformulated as mathematical inference problems where the objective is to determine the set of parameters corresponding to the lowest potential of a task-specific objective function. Graphical models have been the most popular formulation in the field over the past two decades where the problem is viewed as a discrete assignment labeling one. Modularity, scalability, and portability are the main strengths of these methods which once combined with efficient inference algorithms they could lead to state of the art results. This monograph focuses on the inference component of the problem and in particular discusses in a systematic manner the most commonly used optimization principles in the context of graphical models. It looks at inference over low rank models (interactions between variables are constrained to pairs) as well as higher order ones (arbitrary set of variables determine hyper-cliques on which constraints are introduced) and seeks a concise, self-contained presentation of prior art as well as the presentation of the current state of the art methods in the field.