Computer Vision
Principles, Algorithms, Applications, Learning
- 6th Edition - March 1, 2027
- Latest edition
- Authors: Sam Siewert, E. R. Davies
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 4 4 2 6 9 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 4 4 2 7 0 - 4
Computer Vision: Principles, Algorithms, Applications, Learning clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the… Read more
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Computer Vision: Principles, Algorithms, Applications, Learning clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This new sixth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject.
• Practical examples and case studies give the ‘ins and outs’ of developing real-world vision systems, giving engineers the realities of implementing the principles in practice
• Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples
• The ‘recent developments’ section included in each chapter helps bring students and practitioners up to date with the subject
• A package of student-friendly ancillaries includes MATLAB applications and tutorials, and solutions to selected problems
• Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples
• The ‘recent developments’ section included in each chapter helps bring students and practitioners up to date with the subject
• A package of student-friendly ancillaries includes MATLAB applications and tutorials, and solutions to selected problems
Upper level undergraduate and graduate students studying computer vision, machine learning, pattern recognition and image processing
1. Vision, the Challenge
2. Images and Imaging Operations
3. Image Filtering and Morphology
4. The Role of Thresholding
5. Edge Detection
6. Corner, Interest Point and Invariant Feature Detection
7. Texture Analysis
8. Binary Shape Analysis
9. Boundary Pattern Analysis
10. Line, Circle and Ellipse Detection
11. The Generalized Hough Transform
12. Object Segmentation and Shape Models
13. Basic Classification Concepts
14. Machine Learning: Probabilistic Methods
15A. Deep Networks Learning
15B. Transformers, their origins, importance and nature
15C. Transformers in Computer Vision
16. The Three-Dimensional World
17. Tackling the Perspective n-point Problem
18. Invariants and perspective
19. Image transformations and camera calibration
20. Motion
21. Face Detection and Recognition: the Impact of Deep Learning
22. Surveillance
23. In-Vehicle Vision Systems
24. Epilogue—Perspectives in Vision
Appendix A: Robust statistics
Appendix B: The Sampling Theorem
Appendix C: The representation of color
Appendix D: Sampling from distributions
2. Images and Imaging Operations
3. Image Filtering and Morphology
4. The Role of Thresholding
5. Edge Detection
6. Corner, Interest Point and Invariant Feature Detection
7. Texture Analysis
8. Binary Shape Analysis
9. Boundary Pattern Analysis
10. Line, Circle and Ellipse Detection
11. The Generalized Hough Transform
12. Object Segmentation and Shape Models
13. Basic Classification Concepts
14. Machine Learning: Probabilistic Methods
15A. Deep Networks Learning
15B. Transformers, their origins, importance and nature
15C. Transformers in Computer Vision
16. The Three-Dimensional World
17. Tackling the Perspective n-point Problem
18. Invariants and perspective
19. Image transformations and camera calibration
20. Motion
21. Face Detection and Recognition: the Impact of Deep Learning
22. Surveillance
23. In-Vehicle Vision Systems
24. Epilogue—Perspectives in Vision
Appendix A: Robust statistics
Appendix B: The Sampling Theorem
Appendix C: The representation of color
Appendix D: Sampling from distributions
- Edition: 6
- Latest edition
- Published: March 1, 2027
- Language: English
SS
Sam Siewert
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.
Affiliations and expertise
Computer Science Department, California State University, ChicoED
E. R. Davies
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.
Affiliations and expertise
Emeritus Professor of Machine Vision, Royal Holloway, University of London, UK (deceased)