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Computer Vision: Principles, Algorithms, Applications, Learning de E. R. Davies

Computer Vision: Principles, Algorithms, Applications, Learning

Autor E. R. Davies, Sam Siewert
en Limba Engleză Paperback – mar 2027
Computer Vision: Principles, Algorithms, Applications, Learning, Sixth Edition 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
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Specificații

ISBN-13: 9780443442698
ISBN-10: 044344269X
Pagini: 950
Dimensiuni: 191 x 235 mm
Ediția:6
Editura: ELSEVIER SCIENCE

Cuprins

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
B: The Sampling Theorem
C: The representation of color
D: Sampling from distributions