* Essential reading for engineers and students working in this cutting edge field
* Ideal module text and background reference for courses in image processing and computer vision
* Companion website includes worksheets, links to free software, Matlab files and new demonstrations
Image processing and computer vision are currently hot topics with undergraduates and professionals alike. Feature Extraction and Image Processing provides an essential guide to the implementation of image processing and computer vision techniques, explaining techniques and fundamentals in a clear and concise manner. Readers can develop working techniques, with usable code provided throughout and working Matlab and Mathcad files on the web.
Focusing on feature extraction while also covering issues and techniques such as image acquisition, sampling theory, point operations and low-level feature extraction, the authors have a clear and coherent approach that will appeal to a wide range of students and professionals.
The new edition includes:
* New coverage of curvature in low-level feature extraction (SIFT and saliency) and features (phase congruency); geometric active contours; morphology; camera models
* Updated coverage of image smoothing (anistropic diffusion); skeletonization; edge detection; curvature; shape descriptions (moments)
* Essential reading for engineers and students working in this cutting edge field
* Ideal module text and background reference for courses in image processing and computer vision
* Companion website includes worksheets, links to free software, Matlab files and solutions
Customer Reviews & Comments
How to define and find features in an image is the core of this book. Unlike broader texts on image recognition, like Davies' Machine Vision : Theory, Algorithms, Practicalities, Nixon and Aguado specialise to what researchers might regard as a crucial issue.
There is some amount of background, of course. Like explaining how the Hough transform works in a dual image space. But in this example, as others, the book gives a more indepth mathematical treatment than Davies. So the Hough transform is described for lines, which Davies does, but also for polar representations of lines, and for circles and ellipses. One effect is to give you enough case studies of how Hough transforms are found, that you can make your own, for some geometrical object to search for in your images.
