Katsushi Ikeuchi – författare

Some of the fundamental constraints of automated machine vision have been the inability automatically to 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.

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 work summarizes the results of the editors' modelling-from-reality (MFR) project, during the 1990s. The goal of this project was to develop techniques for modelling real objects and/or environments into geometric and photometric models through computer vision techniques. By developing such techniques, time- consuming modelling process, currently undertaken by human programmers, can be (semi-) automatically performed, and, as a result, one can drastically shorten the developing time of such virtual reality systems, reduce their developing cost, and widen their application areas. The project was conducted while the authors were at the Computer Science Department of Carnegie Mellon University (CMU) and the Institute of Industrial Science at the University of Tokyo. "Modeling from Reality" is suitable for a secondary text in a graduate-level course, and as a reference for researchers and practitioners in industry.

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.

This book describes active illumination techniques in computer vision. 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 describes active illumination techniques in computer vision. 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 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.

The material of this book encompasses many disciplines, including visible,infrared, far infrared, millimeter wave, microwave, radar, synthetic aperture radar, andelectro-optical sensors as well as the very dynamic topics of image processing, computervision 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