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An Introduction to Digital Image Processing, by Wayne Niblack
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1986 125 pages with 88 figures, some in color. Hardbound, previous owner name, text in very good condition
- Sales Rank: #3342813 in Books
- Published on: 1986-07
- Original language: English
- Number of items: 1
- Binding: Hardcover
- 215 pages
Most helpful customer reviews
4 of 5 people found the following review helpful.
Excellent Introduction to All Facets of Image Processing
By AW
Despite its small size, this book contains a wealth of information on the field of digital image processing. Topics include color space descriptions, filtering in the spatial and Fourier domains, image registration, and classification. The topics are introduced with minimal but clear mathematics - just right for the beginner. The text is similarly concise and clear. The examples and figures in this volume are well chosen and very helpful. The questions at the end of each chapter are helpful as well for understanding the topics.
This book is a real gem for the beginning image processing student or practitioner.
0 of 0 people found the following review helpful.
One the best books of 2D Algorithms in plain english & with the maths
By X42
I bought this book back in 1993, at a cost of $237 which was exorbitant for a book back then and perhaps even now. Its a small, hardback book, written in plain English with a focus on practical algorithms with lots of examples using colour images to show the effects of an algorithm on test images.
I have an interest in the application of the Bayesian Algorithm (BA). This had a nice explanation of the concepts with an anchoring to a practical application. In this explanation the author bothers to spell out that each of the two overlapping distributions have to be independently characterized (obvious, when you know).
He introduces the fundamentals of a concept then extends the concepts (to take into account practical considerations for algorithm processing). The extension of theory for BA was to introduce parametric methods of expressing the BA relationships for data. In other words he shows how to express the probability distributions that exemplify data from the system under study, and shows how to fit distribution curves to the data (Gaussian curve for example). That way you can express a lot of data with just a few parameters needed to draw the parametric curves of each spread of data.
Overall, the production quality of the book is excellent, the algorithms are short and the explanations are very concise. The book is at the same time dense with both pictorial explanations and maths, but written very plainly and simply. It is evident from the explanations that the author has experience in practical use of the algorithms. He often points out what is practical to use. The difference is practical knowledge instead of academic knowledge.
These 2D algorithms are also useful for 1D signal processing (acoustics for example).
Lots of useful algorithm and equations. Useful as a source book of ideas and references. the book is a little outdated. For example it doesn't cover independent component analysis, Blind source separation, stochastic resonance, Elman-Jordan adaptive noise reduction techniques and compressed sensing (although to be fair it actually describes the technique but without the name which was invented around 2006 (?)).
Recommended.
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