This book describes active illumination techniques in computer vision. We can classify computer vision techniques into two classes: passive and active techniques. Passive techniques observe the scene statically and analyse it as is. Active techniques give the scene some actions and try to facilitate the analysis. In particular, active illumination techniques project specific light, for which the characteristics are known beforehand, to a target scene to enable stable and accurate analysis of the scene. Traditional passive techniques have a fundamental limitation. The external world surrounding us is three-dimensional; the image projected on a retina or an imaging device is two-dimensional. That is, reduction of one dimension has occurred. Active illumination techniques compensate for the dimensional reduction by actively controlling the illumination. The demand for reliable vision sensors is rapidly increasing in many application areas, such as robotics and medical image analysis. This book explains this new endeavour to explore the augmentation of reduced dimensions in computer vision. This book consists of three parts: basic concepts, techniques, and applications. The first part explains the basic concepts for understanding active illumination techniques. In particular, the basic concepts of optics are explained so that researchers and engineers outside the field can understand the later chapters. The second part explains currently available active illumination techniques, covering many techniques developed by the authors. The final part shows how such active illumination techniques can be applied to various domains, describing the issue to be overcome by active illumination techniques and the advantages of using these techniques. This book is primarily aimed at 4th year undergraduate and 1st year graduate students, and will also help engineers from fields beyond computer vision to use active illumination techniques. Additionally, the book is suitable as course material for technical seminars.

This book describes active illumination techniques in computer vision. We can classify computer vision techniques into two classes: passive and active techniques. Passive techniques observe the scene statically and analyse it as is. Active techniques give the scene some actions and try to facilitate the analysis. In particular, active illumination techniques project specific light, for which the characteristics are known beforehand, to a target scene to enable stable and accurate analysis of the scene. Traditional passive techniques have a fundamental limitation. The external world surrounding us is three-dimensional; the image projected on a retina or an imaging device is two-dimensional. That is, reduction of one dimension has occurred. Active illumination techniques compensate for the dimensional reduction by actively controlling the illumination. The demand for reliable vision sensors is rapidly increasing in many application areas, such as robotics and medical image analysis. This book explains this new endeavour to explore the augmentation of reduced dimensions in computer vision. This book consists of three parts: basic concepts, techniques, and applications. The first part explains the basic concepts for understanding active illumination techniques. In particular, the basic concepts of optics are explained so that researchers and engineers outside the field can understand the later chapters. The second part explains currently available active illumination techniques, covering many techniques developed by the authors. The final part shows how such active illumination techniques can be applied to various domains, describing the issue to be overcome by active illumination techniques and the advantages of using these techniques. This book is primarily aimed at 4th year undergraduate and 1st year graduate students, and will also help engineers from fields beyond computer vision to use active illumination techniques. Additionally, the book is suitable as course material for technical seminars.

The material of this book encompasses many disciplines, including visible,

infrared, far infrared, millimeter wave, microwave, radar, synthetic aperture radar, and

electro-optical sensors as well as the very dynamic topics of image processing, computer

vision and pattern recognition.

This book is composed of six parts:

* Advanced background modeling for surveillance

* Advances in Tracking in Infrared imagery

* Methods for Pose estimation in Ultrasound and LWIR imagery

* Recognition in multi-spectral and synthetic aperture radar

* Fusion of disparate sensors

* Smart Sensors

The material of this book encompasses many disciplines, including visible,

infrared, far infrared, millimeter wave, microwave, radar, synthetic aperture radar, and

electro-optical sensors as well as the very dynamic topics of image processing, computer

vision and pattern recognition.

This book is composed of six parts:

* Advanced background modeling for surveillance

* Advances in Tracking in Infrared imagery

* Methods for Pose estimation in Ultrasound and LWIR imagery

* Recognition in multi-spectral and synthetic aperture radar

* Fusion of disparate sensors

* Smart Sensors

As our heritage deteriorates through erosion, human error or natural disasters, it has become more important than ever to preserve our past - even if it is in digital form only. This highly relevant work describes thorough research and methods for preserving cultural heritage objects through the use of 3D digital data. These methods were developed via computer vision and computer graphics technologies. They offer a way of passing our heritage down to future generations.

A large number of cultural heritage objects around the world are deteriorating or being destroyed due to the work of natural disasters, such as earthquakes and floods, and human-inflicted destruction, such as war and vandalism. In the wake of these threats, 3D data becomes a critical component to permanently recording the shapes of these important objects so that they might be passed down to future generations. Digitally Archiving Cultural Objects describes thorough research and methods for preserving cultural heritage objects through the use of 3D digital data. These methods were developed through using computer vision and computer graphics technologies. This data can also be used for simulation and restoration purposes as well as towards various multimedia applications. This comprehensive book is organized into four parts. Part 1 describes various sensors designed to obtain data. Part 2 contains a collection of papers that describe the geometric pipeline, converting obtained data into a consistent geometric model, through determining relative relations among digital data and connecting those data into a uniform representation. Part 3 concerns photometric issues, including how to map color pictures on a geometric model and how to remove the effect of sunlight in the pictures obtained. Part 4 reports on the effort to establish a digital museum to restore and display the original appearance of heritage objects as well as conduct analyses of obtained data for heritage research.

This book summarizes the results of our modeling-from-reality (MFR) project which took place over the last decade or so. The goal of this project is to develop techniques for modeling real objects and/or environments into geometric and photometric models through computer vision techniques. By developing such techniques, time consuming modeling process, currently un dertaken by human programmers, can be (semi-)automatically performed, and, as a result, we can drastically shorten the developing time of such virtual reality systems, reduce their developing cost, and widen their application areas. Originally, we began to develop geometric modeling techniques that acquire shape information of objects/environments for object recognition. Soon, this effort evolved into an independent modeling project, virtual-reality modeling, with the inclusion of photometric modeling aspects that acquire appearance information, such as color, texture, and smoothness. Over the course of this development, it became apparent that environmental modeling techniques were necessary when applying our techniques to mixed realities that seamlessly combine generated virtual models with other real/virtual images. The material in his book covers these aspects of development."

Some of the fundamental constraints of automated machine vision have been the inability to automatically adapt parameter settings or utilize previous adaptations in changing environments. Symbolic Visual Learning presents research which adds visual learning capabilities to computer vision systems. Using this state-of-the-art recognition technology, the outcome is different adaptive recognition systems that can measure their own performance, learn from their experience and outperform conventional static designs. Written as a companion volume to Early Visual Learning (edited by S. Nayar and T. Poggio), this book is intended for researchers and students in machine vision and machine learning.

This comprehensive reference provides easy access to relevant information on all aspects of Computer Vision. An A-Z format of over 240 entries offers a diverse range of topics for those seeking entry into any aspect within the broad field of Computer Vision. Over 200 Authors from both industry and academia contributed to this volume. Each entry includes synonyms, a definition and discussion of the topic, and a robust bibliography. Extensive cross-references to other entries support efficient, user-friendly searches for immediate access to relevant information. Entries were peer-reviewed by a distinguished international advisory board, both scientifically and geographically diverse, ensuring balanced coverage. Over 3700 bibliographic references for further reading enable deeper exploration into any of the topics covered. The content of Computer Vision: A Reference Guide is expository and tutorial, making the book a practical resource for students who are considering entering the field, as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest.