Book on Computer Vision
Professor Davies's book Computer Vision: Principles, Algorithms, Applications,
Learning is widely used throughout the world by researchers and
practitioners of the subject, and carries a wealth of detail on algorithms
of importance to industrial and commercial applications of machine vision,
as well as giving insight into the underlying theory. In November 2017 it was released in a much
updated fifth edition containing four new chapters in the areas Machine Learning, Deep Learning Networks
and Face Detection and Recognition, together with additional material on the following topics:
- Shape models
- Geometric transformations
- The EM algorithm
- Boosting
- Semantic segmentation
- Face frontalisation
- Recurrent neural networks
- Sampling from distributions
as well as location of:
- faces
- eyes and irises
- human hands
- road markings and road signs
- vehicles
- pedestrians
- laparoscopic tools
- cracks, defects and foreign bodies
- biscuits.
See full list of contents for further details.
For details of the new chapters on Machine Learning, Deep Learning Networks
and Face Detection and Recognition,
see contents of new chapters.
The publisher's Companion Site for this book is:
Book companion – Computer Vision.
It contains solutions to selected problems and Matlab tutorials and programming examples.
See a revealing interview with the author explaining his approach to learning and teaching computer vision:
Interview with the author.
A further interesting interview with the author appears on pp. 22–25 of the
February 2018 issue of Computer Vision News.
The interview focuses particularly on the impact of deep learning networks on computer vision, and the aspects of this and other topics
covered in the latest edition of Roy’s book on Computer Vision.