Fundamentals of Computer Vision

Computer vision has widespread and growing application including robotics, autonomous vehicles, medical imaging and diagnosis, surveillance, video analysis, and even tracking for sports analysis. This book equips the reader with crucial mathematical and algorithmic tools to develop a thorough understanding of the underlying components of any complete computer vision system and to design such systems. These components include identifying local features such as corners or edges in the presence of noise, edge preserving smoothing, connected component labeling, stereopsis, thresholding, clustering, segmentation, and describing and matching both shapes and scenes. The extensive examples include photographs of faces, cartoons, animal footprints, and angiograms, and each chapter concludes with homework exercises and suggested projects. Intended for advanced undergraduate and beginning graduate students, the text will also be of use to practitioners and researchers in a range of applications.

“Advance praise: 'This new textbook by Snyder and Qi covers fundamental topics in computer vision. The authors do an outstanding job of discussing the subject. I like the way that required mathematical background is presented in the context of computer vision, thus reducing prerequisites on the part of the reader to basic calculus and introductory computer programming. The book is ideally suited for a first course in computer vision at the senior or first-year graduate level in a technical discipline, such as computer science, engineering, physics, or mathematics. I commend the authors for their style of presentation and for keeping a sharp focus on fundamentals.' Rafael C. Gonzalez, Distinguished-Service Professor Emeritus, Electrical Engineering and Computer Science, University of Tennessee”

'This new textbook by Snyder and Qi covers fundamental topics in computer vision. The authors do an outstanding job of discussing the subject. I like the way that required mathematical background is presented in the context of computer vision, thus reducing prerequisites on the part of the reader to basic calculus and introductory computer programming. The book is ideally suited for a first course in computer vision at the senior or first-year graduate level in a technical discipline, such as computer science, engineering, physics, or mathematics. I commend the authors for their style of presentation and for keeping a sharp focus on fundamentals.' Rafael C. Gonzalez, Distinguished-Service Professor Emeritus, Electrical Engineering and Computer Science, University of Tennessee
'Written with depth and clarity, this book introduces a variety of fundamentals needed to participate in computer vision research. Not only does it focus on mathematical fundamentals, but it shows all with well-motivated, concrete examples and applications. I believe vision students and researchers will find this book valuable. I look forward to teaching from it.' Tianfu Wu, North Carolina State University

Wesley E. Snyder is Professor Emeritus of Electrical and Computer Engineering at North Carolina State University. He was previously a professor at the Bowman Gray School of Medicine and worked at the GE Corporate Research and Development Center, NASA Langley Research Center, and the West German Air and Space Agency (DLR). He has published 179 research papers and was chosen as an Outstanding Engineering Educator in North Carolina in 1993. He was made fellow of the Institute of Electrical and Electronics Engineers (IEEE), Gladden Fellow (University of Western Australia), and Fellow of the American Institute of Medical and Biomedical Engineering. Hairong Qi is the Gonzalez Family Professor of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. Her research interests include collaborative signal and image processing, hyperspectral imaging, and bioinformatics. She is the recipient of a National Science Foundation CAREER Award, numerous best paper awards at international conferences, and was awarded the highest impact paper from the Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society in 2012.

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