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Machine Vision: Theory, Algorithms, Practicalities Hardcover – Dec 22 2004


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“This book brings together the analytic aspects of image processing with the practicalities of applying the techniques in an industrial setting. It is excellent grounding for a machine vision researcher.”
- John Billingsley, University of Southern Queensland

“The book in its previous incarnations has established its place as a unique repository of detailed analysis of important image processing and computer vision algorithms.”
- Farzin Deravi, University of Kent

“This book is an essential reference for anyone developing techniques for machine vision analysis, including systems for industrial inspection, biomedical analysis, and much more.”
- Majid Mirmehdi, University of Bristol

“The book contains a large number of experimental design and evaluation procedures that are of keen interest to industrial application engineers of machine vision.”
- William Wee, University of Cincinnati

“Author E.R. Davies covers essential elements of the theory while addressing algorithmic and practical design constraints.”
- Mechanical Engineering, August 2006

Book Description

Thoroughly updated solid text reference for an increasingly important field

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Amazon.com: 4 reviews
19 of 19 people found the following review helpful
Good survey of specific machine vision techniques June 16 2006
By calvinnme - Published on Amazon.com
Format: Hardcover
To begin with, the latest edition of this book was published in 2004, so all reviews dated earlier than that are referring to a previous edition. This book is a good one on issues and algorithms as they pertain to machine vision versus general computer vision. If you want a good general textbook on computer vision try "Computer Vision" by Linda Shapiro. It has all of the background material and a firm foundation in all of the topics you would expect in a course on computer vision. This book also has a section on introductory computer vision topics, I just don't think it is as clear and as comprehensive as Shapiro's book, especially for students.

However, if you want an excellent treatment of the kinds of problems specific to machine vision - the detection of lines, holes, corners, circles, elipses, and polygons, for example, along with specific algorithm details, this book is very good. It also has good sections on pattern matching, motion estimation, and 3D machine vision. I would recommend it especially for those individuals who are already familiar with the basics of computer vision and would like a book on algorithms for solving specific problems in machine vision. I notice that Amazon only shows the table of contents for the previous edition, so I show the table of contents for the new edition next:

1. Vision, The Challenge

PART 1 - LOW-LEVEL VISION
2. Images and Imaging Operations
3. Basic Image Filtering Operations
4. Thresholding Techniques
5. Edge Detection
6. Binary Shape Analysis
7. Boundary Pattern Analysis
8. Mathematical Morphology

PART 2 - INTERMEDIATE-LEVEL VISION
9. Line Detection
10. Circle Detection
11. The Hough Transform and Its Nature
12. Ellipse Detection
13. Hole Detection
14. Polygon and Corner Detection
15. Abstract Pattern Matching Techniques

PART 3 - 3D VISION AND MOTION
16. The Three-Dimensional World
17. Tackling the Perspective n-Point Problem
18. Motion
19. Invariants and their Applications
20. Egomotion and Related Tasks
21. Image Transformations and Camera Calibration

Part 4 - TOWARDS REAL-TIME PATTERN RECOGNITION SYSTEMS
22. Automated Visual Inspection
23. Inspection of Cereal Grains
24. Statistical Pattern Recognition
25. Biologically Inspired Recognition Schemes
26. Texture
27. Image Acquisition
28. Real-Time Hardware and Systems Design Considerations

PART 5 - PERSPECTIVES ON VISION
29. Machine Vision, Art or Science?
5 of 5 people found the following review helpful
Book with basic techniques June 6 2011
By M.Davydov - Published on Amazon.com
Format: Hardcover Verified Purchase
It is a good book for beginners in image processing. Basic techniques are well described with mathematical formulas and algorithms. There is a lot of models considering computer vision geometry.
On the other side there is a lack of modern techniques in the book. You will find no info about Haar features, Bayesian fields, Gabor filters. Neural networks, Ada-boost, SVM, PCA are described superficially. You will need more info to implement them.
2 of 2 people found the following review helpful
Excelent discussion of Machine VIsion and goes in depth into areas often over looked. July 14 2011
By P. Abeles - Published on Amazon.com
Format: Hardcover Verified Purchase
This book has a bit more of a practical feel than other related books in machine vision/computer vision. I feel it does a good job of balancing mathematics versus practical implementation issues. What I feel makes this book a real gem is how it goes into detail on subjects either glossed over or omitted in other books. Examples of that are its discussion on template based edge detection, various algorithms in Chapter 6: Binary Shape Analysis, corner detection, and a large discussion of the Hough transform (personally I find the Hough transform to be of little value in the images I work with). Recently I have found myself turning to this book more because of the less common information it contains. I tend to get the feel that the author has personally used much of what has been discussed while in other books it often feels like material has been included just because its standard practice to include it. I should note that for the most part I have ignored chapters 16 and beyond that deal with "higher level" vision. There are other books which focus on that area which I use instead.
1 of 1 people found the following review helpful
use it to understand OpenCV Dec 17 2007
By W Boudville - Published on Amazon.com
Format: Hardcover
For the analyst wanting to get into image recognition, Davies offers a detailed look at the many methods used in the last 30-40 years. These include neural networks, support vector machines, and the Hough transform.

If you are tempted to use [or are using] the OpenCV code base for image research, then the book can be a vital theoretical framework. OpenCV is about the best open source image code out there on the net, but it is poorly documented. It does come with many methods for basic and vital operations like make a grayscale image from a colour image, and making a binary image from a grayscale image. But why the code does certain things (actually many things) is rarely explained. Try using this book for understanding. Plus, the text lets you get an idea of how to modify OpenCV for your purposes.

And if you are going to use this book with OpenCV, look closely at the section on using multiple classifiers for training and then testing against unknown images. It is the basic idea for the cascading classifiers used by OpenCV.

Along these lines, one improvement for a future edition of the book could be an analysis of code packages that are currently available for image processing. Just a thought. But it would greatly help people wanting an expert assessment on the efficacies of available packages. Or, on a more basic level, it would aid simply in delineating what is out there.


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