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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. This edition builds on these strengths and adds material to guide the reader's understanding of the latest developments in the field. The result is a comprehensive up-to-date reference text." - 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. It comes from a long-term practitioner and is packed with the fundamental techniques required to build and prototype methods to test their applicability to the problem at hand." - 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. In this updated edition, he divides the material into horizontal levels of a complete machine vision system. He includes coverage of 2-D and 3-D scene analysis, along with the Hough Transform, a key technique for inspection and surveillance." - Mechanical Engineering, August 2006
Product Description
In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directly addresses this need. As in earlier editions, E.R. Davies clearly and systematically presents the basic concepts of the field in highly accessible prose and images, covering essential elements of the theory while emphasizing algorithmic and practical design constraints. In this thoroughly updated edition, he divides the material into horizontal levels of a complete machine vision system. Application case studies demonstrate specific techniques and illustrate key constraints for designing real-world machine vision systems. This work: includes solid, accessible coverage of 2-D and 3-D scene analysis; offers thorough treatment of the Hough Transform - a key technique for inspection and surveillance; brings vital topics and techniques together in an integrated system design approach; and, takes full account of the requirement for real-time processing in real applications.
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Customer Reviews
Most Helpful Customer Reviews
1 of 1 people found the following review helpful
Format:Hardcover
This book is not a hands on "cook book" for those wishing to get some simple code for a specific application. It is instead a comprehensive overview of just about every topic within the world of image processing. Upon reading through this the reader should have a firm grasp of image processing theory as well as a good understanding of the major areas of ongoing research. If someone is starting a career / PhD / Masters and needs to get a complete overview of image processing then this is the one book they need to read.
1 of 4 people found the following review helpful
Format:Hardcover
Edition 3 is a major 6 cm thick hardback tome of almost 1000 pages.
It is readable but very academic, (bizarrely) written in American, not
English.
Instead of inventing strange notations for array operations, this book
needs code examples - with some Java in there this would be a 5-star
recommendation.
How about setting up a web site with accompanying code - this would be
quicker than publishing a new edition & would be a fantastic resource.
(I'm surprised the author does not have access to a pet nerd to do this
- contact me if you wish to do this on the next exition).
It is readable but very academic, (bizarrely) written in American, not
English.
Instead of inventing strange notations for array operations, this book
needs code examples - with some Java in there this would be a 5-star
recommendation.
How about setting up a web site with accompanying code - this would be
quicker than publishing a new edition & would be a fantastic resource.
(I'm surprised the author does not have access to a pet nerd to do this
- contact me if you wish to do this on the next exition).
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
4 reviews
18 of 18 people found the following review helpful
Good survey of specific machine vision techniques
16 Jun 2006
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?
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?
3 of 3 people found the following review helpful
Book with basic techniques
6 Jun 2011
Format:Hardcover|Amazon 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.
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.
4 of 5 people found the following review helpful
use it to understand OpenCV
18 Dec 2007
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.
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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