This book presents an overview of the guidelines and strategies for transitioning an image or video processing algorithm from a research environment into a real-time constrained environment. Such guidelines and strategies are scattered in the literature of various disciplines including image processing, computer engineering, and software engineering, and thus have not previously appeared in one place. By bringing these strategies into one place, the book is intended to serve the greater community of researchers, practicing engineers, industrial professionals, who are interested in taking an image or video processing algorithm from a research environment to an actual real-time implementation on a resource constrained hardware platform. These strategies consist of algorithm simplifications, hardware architectures, and software methods. Throughout the book, carefully selected representative examples from the literature are presented to illustrate the discussed concepts. After reading thebook, the readers are exposed to a wide variety of techniques and tools, which they can then employ to design a real-time image or video processing system.

About the Author

Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor in the Department of Electrical and Computer Engineering at the University of Texas at Dallas. His research areas include signal and image processing, real-time processing on embedded processors, deep learning, and machine learning. He has authored or co-authored more than 400 publications and 9 other books pertaining to signal and image processing, and regularly teaches the signals and systems laboratory course, for which this book is written. Dr. Kehtarnavaz is a Fellow of IEEE, a Fellow of SPIE, and a licensed Professional Engineer. Mark Gamadia is currently a Ph.D. Candidate and an Erik Jonsson Research Assistant Fellow in the Department of Electrical Engineering at the University of Texas at Dallas. He has written several papers involving the development and real-time implementation of image processing algorithms. Mr. Gamadia is a Student Member of IEEE, IEEE Signal Processing Society, and IEEE Consumer Electronics Society.

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