Image Processing and Jump Regression Analysis

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.