| View Larger Image | Machine Vision Algorithms and Applications | Paperbackby Carsten Steger (Author), Markus Ulrich (Author), Christian Wiedemann (Author)
| List Price: | $60.00 | | Price: | $54.00 | | You Save: | $6.00 (10%) | | | Available: | Usually ships in 24 hours |
| | Binding: | Paperback | | Publisher: | Wiley-VCH | | Edition: | 1st Edition | | Page Count: | 370 Pages | | Publication Date: | January 18, 2008 | | Sales Rank: | 272,503nd |
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EDITORIAL REVIEWS |
Product Description
This first up-to-date textbook for machine vision software provides all the details on the theory and practical use of the relevant algorithms. The first part covers image acquisition, including illumination, lenses, cameras, frame grabbers, and bus systems, while the second deals with the algorithms themselves. This includes data structures, image enhancement and transformations, segmentation, feature extraction, morphology, template matching, stereo reconstruction, and camera calibration. The final part concentrates on applications, and features real-world examples, example code with HALCON, and further exercises. Uniting the latest research results with an industrial approach, this textbook is ideal for students of electrical engineering, physics and informatics, electrical and mechanical engineers, as well as those working in the sensor, automation and optical industries. Free software available with registration code |
CUSTOMER REVIEWS (Average Customer Rating: 4.0 based on 1 review)
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a follow on to Davies by W Boudville (Terra, Sol 3)  4 Stars
February 19, 2008
This very recent book can be used as a follow on the Davies tome, Machine Vision : Theory, Algorithms, Practicalities. The latter was a good general treatment of the many ideas used in image recognition. Whereas the current book does not start from scratch in the field, and takes the reader to much of the current research issues.
Image segmentation, morphology and edge extraction are several of these issues. The level of mathematical detail goes beyond the introductory style of Davies. Which can be a great help if you are relying on Davies. The problem with it is that a serious researcher often has to recreate or rederive much of the needed low level methods. These are often presented here by Steger et al.
For users of OpenCV, note that it is nowhere referenced in the book. But some ideas that form its basis, like template matching, can be found here.
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