In order to provide our readers with as many resources onimaging and machine vision as possible, Andy Wilson, Vision Systems Design Editor in Chief, has compiled a list of educational and informative books on various imaging topics that he personally recommends. Check out this week’s recommendations here:
- Machine Vision by Ramesh Jain, Rangachar Kasturi, Brian G. Schunck: This introduction to the field of computer vision focuses on basic concepts and techniques. The thrust is to give practitioners what they need to know to develop a practical machine vision system. Binary vision, segmentation, constraint propagation techniques are presented as are camera calibration, color and texture, detection of motion, and object recognition.
- Mechatronics and Machine Vision in Practice by John Billingsley, Robin Bradbeer: From grading and preparing harvested vegetables to the tactile probing of a patient’s innermost recesses, mechatronics has become part of our way of life. This cutting-edge volume features the 30 best papers of the 13th International Conference on Mechatronics and Machine Vision in Practice.
- Image Processing, Analysis & and Machine Vision - A MATLAB Companion by Tomas Svoboda, Jan Kybic, Vaclav Hlavac: This book is a companion book to the comprehensive text entitled Image Processing, Analysis, and Machine Vision by M. Sonka, V. Hlavac, and R. Boyle. This workbook provides additional material for readers of Sonka and is similarly structured. Written for students, teachers and practitioners to acquire practical understanding in a hands on fashion, this book provides the reader with short-answer questions, problems and selected algorithms from the main text using MATLAB in levels of varying difficulty.
- Pattern Classification (Pt.1)by Richard O. Duda, Peter E. Hart, David G. Stork: The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
- MATLAB: An Introduction with Applications by Amos Gilat: Designed for the newest version of the popular MATLAB software program, MATLAB: An Introduction with Applications, 3/e requires no previous knowledge of computer programming. The first chapter describes basic features of the program and shows how to use it in simple arithmetic operations with scalars. The next two chapters focus on the topic of arrays (the basis of MATLAB), while the remaining text covers a wide range of other applications.
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