Analysis of Variance in Statistical Image Processing
Analysis of Variance in Statistical Image Processing
M. Hafed Benteftifa , Polytechnic University, New York
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A key problem in practical image processing is that of detecting certain features in a noisy image. Analysis of variance (ANOVA) techniques can be very effective in such situations, and this book gives a detailed account of the use of ANOVA in statistical image processing. The book begins by describing the statistical representation of images in the various ANOVA models. A number of computationally efficient algorithms and techniques are then presented, to deal with such problems as line, edge and object detection, as well as image restoration and enhancement. By describing the basic principles of these techniques, and showing their use in specific situations, the book will facilitate the design of new algorithms for particular applications. It will be of great interest to graduate students and engineers in the field of image processing and pattern recognition.
- Shows how to deal with real, noisy images
- Provides efficient algorithms which can be used as the starting point for tackling new problems
- Can be used as a textbook for a graduate course
Product details
March 2011Adobe eBook Reader
9780511823251
0 pages
0kg
81 b/w illus. 5 tables
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- Preface
- 1. Introduction
- 2. Statistical linear models
- 3. Line detection
- 4. Edge detection
- 5. Object detection
- 6. Image segmentation
- 7. Radial masks in line and edge detection
- 8. Performance analysis
- 9. Some approaches to image restoration
- References
- Index.
