The Encyclopedia of Image Processing presents a vast collection of well-written articles covering image processing fundamentals (e.g. color theory, fuzzy sets, cryptography) and applications (e.g. geographic information systems, traffic analysis, forgery detection). Image processing advances have enabled many applications in healthcare, avionics, robotics, natural resource discovery, and defense, which makes this text a key asset for both academic and industrial libraries and applied scientists and engineers working in any field that utilizes image processing. Written by experts from both academia and industry, it is structured using the ACM Computing Classification System (CCS) first published in 1988, but most recently updated in 2012.

The Encyclopedia of Image Processing presents a vast collection of well-written articles covering image processing fundamentals (e.g. color theory, fuzzy sets, cryptography) and applications (e.g. geographic information systems, traffic analysis, forgery detection). Image processing advances have enabled many applications in healthcare, avionics, robotics, natural resource discovery, and defense, which makes this text a key asset for both academic and industrial libraries and applied scientists and engineers working in any field that utilizes image processing. Written by experts from both academia and industry, it is structured using the ACM Computing Classification System (CCS) first published in 1988, but most recently updated in 2012.

This introductory image processing text is ideal for college students studying computer science or software engineering. With an emphasis on software design, the text builds on an accessible mathematical foundation and on extensive sample Java code to teach students the fundamentals of image processing. The text is accompanied by rich illustrations that demonstrate the results of performing processing on famous art pieces. This approach gives readers real-world examples of ways they may use image processing. Each chapter includes problems for students, and an online supplement offers instructor resources.

Similar to the way in which computer vision and computer graphics act as the dual fields that connect image processing in modern computer science, the field of image processing can be considered a crucial middle road between the vision and graphics fields. Research Developments in Computer Vision and Image Processing: Methodologies and Applications brings together various research methodologies and trends in emerging areas of application of computer vision and image processing. This book is useful for students, researchers, scientists, and engineers interested in the research developments of this rapidly growing field.

This encyclopedia is the first to offer in-depth coverage of imaging science and technology from a diverse range of applications, techniques and fields of study. Today imaging is used by astronomers to map distant galaxies, oceanographers to map the sea floor, chemists to map the distribution of atoms on a surface, physicians to map the functionality of the brain and electrical engineers to map electromagnetic fields around power lines. With this encyclopedia, scientists, engineers and physicians can understand more about the science and technology behind the imaging techniques they are currently using and learn the latest technologies. Diverse coverage offers the ability to learn from applications in archeology, aviation, astronomy, chemistry, forensics, geography, mathematics, medicine, meteorology, microscopy, oceanography, surveillance and more ... and how to apply those imaging solutions to many different problems. Also available in a user-friendly, online edition The new electronic version of the Encyclopedia, accessible through Wiley InterScience, offers enhanced browsing, searching and cross-referencing capabilities. Visit www.interscience.wiley.com/eist

Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.