Computer Vision
Principles, Algorithms, Applications, Learning
Description
Key Features
- Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition.
- A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application.
- In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics.
- Examples and applications—including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians—give the ‘ins and outs’ of developing real-world vision systems, showing the realities of practical implementation.
- Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples.
- The ‘recent developments’ sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject.
- Tailored programming examples—code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)
Readership
Computer vision researchers; undergraduates and post graduates in computer vision, machine learning, pattern recognition and Image processing
Table of Contents
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 Generalised Hough Transform
12. Object Segmentation and Shape Models
13. Basic Classification Concepts
14. Machine Learning: Probabilistic Methods
15. Deep Learning Networks
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 colour
Appendix D: Sampling from distributions
References
Index
Product details
- No. of pages: 900
- Language: English
- Copyright: © Academic Press 2017
- Published: November 14, 2017
- Imprint: Academic Press
- eBook ISBN: 9780128095751
- Hardcover ISBN: 9780128092842
About the Author
E. R. Davies
Affiliations and Expertise
Ratings and Reviews
Latest reviews
(Total rating for all reviews)
PeterLazorik Fri Jan 11 2019
Perfect book with examples.
Perfect book with examples.
Vikash K. Wed Jan 24 2018
Detailed coverage from basic to advanced with focus on Deep Learning
Apart from being very well organised, detailed and complete the 5th edition provides new chapters on Face Recognition, Machine Learning, Deep Learning. Various aspects of DL and ML from the perspective of Computer Vision are addressed. With detailed focus on EM algorithm, boosting, semantic segmentation and RNN. Also more focus is given on Object Segmentation and Shape Models. Overall I would say a very good book, useful for beginner to advanced level.