Summary:

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 breakdown 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, edge detection in image processing, and edge-preserving image restoration, to name a few. With mathematical proofs kept to a minimum, this book is uniquely accessible as a primary text in nonparametric jump regression analysis and image processing as well as a reference on image processing or curve/surface estimation.

Table of Contents:
Preface. 1. Introduction. 2. Basic Statistical Concepts and Conventional Smoothing Techniques. 3. Estimation of Jump Regression Curves. 4. Estimation of Jump Location Curves of Regression Surfaces. 5. Jump Preserving Surface Estimation By Local Smoothing. 6. Edge Detection In Image Processing. 7. Edge-Preserving Image Restoration. References. Index.