Image Processing and Jump Regression Analysis (Wiley Series in Probability and Statistics) [Hardcover]

Peihua Qiu (Author)

Book Description

Publication Date: January 28, 2005 | ISBN-10: 0471420999 | ISBN-13: 978-0471420996 | Edition: 1

The first text to bridge the gap between image processing and jump regression analysis

Recent statistical tools developed to estimate jump curves and surfaces have broad applications, specifically in the area of image processing. Often, significant differences in technical terminologies make communication between the disciplines of image processing and jump regression analysis difficult. In easy-to-understand language, Image Processing and Jump Regression Analysis builds a bridge between the worlds of computer graphics and statistics by addressing both the connections and the differences between these two disciplines. The author provides a systematic analysis of the methodology behind nonparametric jump regression analysis by outlining procedures that are easy to use, simple to compute, and have proven statistical theory behind them.

Key topics include:

• Conventional smoothing procedures
• Estimation of jump regression curves
• Estimation of jump location curves of regression surfaces
• Jump-preserving surface reconstruction based on local smoothing
• Edge detection in image processing
• Edge-preserving image restoration

With mathematical proofs kept to a minimum, this book is uniquely accessible to a broad readership. It may be used as a primary text in nonparametric regression analysis and image processing as well as a reference guide for academicians and industry professionals focused on image processing or curve/surface estimation.

Editorial Reviews

Review

"It has much to offer that is hard to find elsewhere." (Journal of the American Statistical Association, December 2006)

"…a well-written book offering comprehensive discussions...an excellent reference and source book for statisticians, computer scientists, engineers, and other researchers…" (IIE Transactions- Quality and Reliability Engineering, June 2006)

"…an impressive resource for research statisticians…researchers in computer graphics and image processing…" (Technometrics, May 2006)

From the Back Cover

The first text to bridge the gap between image processing and jump regression analysis

Recent statistical tools developed to estimate jump curves and surfaces have broad applications, specifically in the area of image processing. Often, significant differences in technical terminologies make communication between the disciplines of image processing and jump regression analysis difficult. In easy-to-understand language, Image Processing and Jump Regression Analysis builds a bridge between the worlds of computer graphics and statistics by addressing both the connections and the differences between these two disciplines. The author provides a systematic analysis of the methodology behind nonparametric jump regression analysis by outlining procedures that are easy to use, simple to compute, and have proven statistical theory behind them.

Key topics include:

• Conventional smoothing procedures
• Estimation of jump regression curves
• Estimation of jump location curves of regression surfaces
• Jump-preserving surface reconstruction based on local smoothing
• Edge detection in image processing
• Edge-preserving image restoration

With mathematical proofs kept to a minimum, this book is uniquely accessible to a broad readership. It may be used as a primary text in nonparametric regression analysis and image processing as well as a reference guide for academicians and industry professionals focused on image processing or curve/surface estimation.

See all Editorial Reviews

Product Details

• Hardcover: 344 pages
• Publisher: Wiley-Interscience; 1 edition (January 28, 2005)
• Language: English
• ISBN-10: 0471420999
• ISBN-13: 978-0471420996
• Product Dimensions: 9.5 x 6.2 x 0.8 inches
• Shipping Weight: 1.4 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #3,647,629 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

3 of 3 people found the following review helpful:

4.0 out of 5 stars An Interesting Statistical Approach to Image Analysis, December 21, 2007

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Random Thoughts (Maryland) - See all my reviews

This review is from: Image Processing and Jump Regression Analysis (Wiley Series in Probability and Statistics) (Hardcover)

Statistical approaches to image analysis often take the route of Markov random field which presents a reasonable model for many spatial processes including images. This is the case with Besag or Geman and Geman, or recent developments in penalized likelihood or Markov chain Monte Carlo, for example Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability). However, image analysis is a much broader field, not the least of which are the many important issues in preprocessing such as background correction, normalization, and segmentation. Many filtering and smoothing algorithms have much in common with time series analysis, and the statistical smoothing or nonparametric regression techniques. The book has given in depth some recent developments of local polynomial regression, including piecewise polynomials for discontinuous surfaces. The jump regression approach is among several recent developments of nonparametric regression for image analysis, and better comparison can be made to alternative approaches such as adaptive weights smoothing, nonlinear adaptive filtering, or PDE-based anisotropic diffusion. All these methods are for image restoration and image denoising. Image segmentation and boundary estimation are apparrently different problems, for example, both Canny edge detection (based on some ad hoc thresholding of partial derivative estimation) and Mumford and Shah (variational approach leading to complicated PDE numerics) have been used. The important issue of computation in the jump regression approach remains to be developed in order to make the new techniques in the book accessible and useful to the wider image analysis community.