Roy Davies was Emeritus Professor of Machine Vision at Royal Holloway, University of London. He worked on many aspects of vision, from feature detection to robust, real-time implementations of practical vision tasks. His interests included automated visual inspection, surveillance, vehicle guidance, crime detection and neural networks. He has published more than 200 papers, and three books. Machine Vision: Theory, Algorithms, Practicalities (1990) has been widely used internationally for more than 25 years, and is now out in this much enhanced fifth edition. Roy held a DSc at the University of London and was awarded Distinguished Fellow of the British Machine Vision Association, and Fellow of the International Association of Pattern Recognition. Dr. Sam Siewert has a B.S. in Aerospace and Mechanical Engineering from University of Notre Dame and M.S. and Ph.D. in Computer Science from University of Colorado Boulder. Dr. Siewert is presently an associate professor of Computer Science at California State University, an associate adjunct professor in the Electrical, Computer and Software Engineering Department at Embry Riddle Aeronautical University and an Associate Professor Adjunct in Electrical and Computer Engineering at University of Colorado Boulder. He teaches several summer courses in the Electrical, Computer, and Energy Engineering department at University of Colorado and on Coursera. As a computer system design engineer, Dr. Siewert has worked in the aerospace, telecommunications, and storage industries for more than twenty-four years before starting an academic career in 2012. Half of his time was spent on NASA space exploration programs and the other half of that time on commercial product development for high performance networking and storage systems. On-going interests as a researcher and consultant include real-time theory, scalable systems, computer and machine vision, hybrid architecture and operating systems. Related research interests include machine learning, interactive systems, and software engineering.

• 1. Vision, the Challenge2. Images and Imaging Operations3. Image Filtering and Morphology4. The Role of Thresholding5. Edge Detection6. Corner, Interest Point and Invariant Feature Detection7. Texture Analysis8. Binary Shape Analysis9. Boundary Pattern Analysis10. Line, Circle and Ellipse Detection11. The Generalized Hough Transform12. Object Segmentation and Shape Models13. Basic Classification Concepts14. Machine Learning: Probabilistic Methods15A. Deep Networks Learning15B. Transformers, their origins, importance and nature15C. Transformers in Computer Vision16. The Three-Dimensional World17. Tackling the Perspective n-point Problem18. Invariants and perspective19. Image transformations and camera calibration20. Motion21. Face Detection and Recognition: the Impact of Deep Learning22. Surveillance23. In-Vehicle Vision Systems24. Epilogue—Perspectives in VisionAppendixA: Robust statisticsB: The Sampling TheoremC: The representation of colorD: Sampling from distributions