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 | | Machine Vision: Theory, Algorithms, Practicalities (Microelectronics and Signal Processing Series) by E. R. Davies ISBN-10: 9780122060908 ISBN-10: 0-12-206090-3 ISBN-13: 9780122060908 ISBN-13: 978-0-12-206090-8 Hardcover 1991-01 Academic Press Find Lowest Price |
Editorials | ||
Book Description The field of machine vision has expanded extensively since the First Edition of Machine Vision was published by Academic Press in 1990. As a result, this Second Edition contains significant amounts of new material on artificial neural networks, mathematical morphology, motion, invariance, texture analysis, x-ray inspection, and foreign object detection. Intermediate level vision is examined in depth (especially Hough transforms), and automated visual inspectionis discussed. The author takes care to consider theoretical aspects as well as practical applications, including perspective invariants and robust statistics. Written in a user-friendly style and full of up-to-date methods, Machine Vision, Second Edition will be an essential volume for students and professionals in the field. Key Features * Gives considerable emphasis to robust analysis of images to demonstrate how problems of occlusion, noise, distortion, and variability may be overcome * Introduces Hough transforms as an integral part of the text and shows how they may be applied in a variety of situations * Presents the topic of robust statistics non-mathematically in the context of vision algorithms * Considers the role of neural networks in machine vision * Shows how the concepts of perspective invariance provide basic strategies for 2-D and 3-D vision * Studies image transformations and the prespective n-point problem systematically to clarify how interpretation may proceed in various geometrical situations * Pays special attention to the detection of defects, foreign objects, and real-time implementation hardware in consideration of automated visual inspection | ||
Reviews | ||
use it to understand OpenCV 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. | ||
Good survey of specific machine vision techniques 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? | ||
Solid Foundation to computer Vision First of all I like this book very much. This book provides a solid and concrete foundation to computer vision from engineering point of view. The basic issues are treated very well in the conceptual and practical levels (e.g. edge detection). I came from a photogrammetry background, which means that the geometric aspects are very dominant in my thinking, and this book emphasize many geometric concepts in computer vision specially the treatment of Hough Transform as a main theme in the book. I recommend this book to the practitioners in spatial sciences (GIS, Remote sensing, Photogrammetry, etc) as well as the general community of computer vision. | ||
Excellent resource Covers many aspects of vision, from basic image processing through high level scene analysis. It doesn't always go down to the nitty-gritty source code level for every topic, but it does provide the direction to handle most every common machine vision problem. Of the ten or so general machine vision books on my easy-access shelf, this is the one I seem to pull down the most. | ||
Good structured reference, very useful A very clearly structured book which is useful as a reference. Covers a lot of subjects (filtering, detection of shapes [lines, circles, holes and more], pattern matching/recognition, motion, invariants, ...), including the implementation aspects (hard/software). The chapters sometimes do not go much into deep but provide further references. Recommended book! | ||