Statistical Image Processing and Multidimensional Modeling

  • Paul Fieguth

Part of the Information Science and Statistics book series (ISS)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Paul Fieguth
    Pages 1-10
  3. Inverse Problems and Estimation

    1. Front Matter
      Pages 11-11
    2. Paul Fieguth
      Pages 13-55
    3. Paul Fieguth
      Pages 57-84
    4. Paul Fieguth
      Pages 85-129
  4. Modelling of Random Fields

    1. Front Matter
      Pages 131-131
    2. Paul Fieguth
      Pages 133-177
    3. Paul Fieguth
      Pages 179-214
    4. Paul Fieguth
      Pages 215-239
    5. Paul Fieguth
      Pages 241-290
  5. Methods and Algorithms

    1. Front Matter
      Pages 291-291
    2. Paul Fieguth
      Pages 293-324
    3. Paul Fieguth
      Pages 325-353
    4. Paul Fieguth
      Pages 355-380
  6. Appendices

    1. Front Matter
      Pages 381-381
    2. Paul Fieguth
      Pages 383-409
    3. Paul Fieguth
      Pages 411-421
    4. Paul Fieguth
      Pages 423-432
  7. Back Matter
    Pages 433-454

About this book

Introduction

Images are all around us! The proliferation of low-cost, high-quality imaging devices has led to an explosion in acquired images. When these images are acquired from a microscope, telescope, satellite, or medical imaging device, there is a statistical image processing task: the inference of something—an artery, a road, a DNA marker, an oil spill—from imagery, possibly noisy, blurry, or incomplete. A great many textbooks have been written on image processing. However this book does not so much focus on images, per se, but rather on spatial data sets, with one or more measurements taken over a two or higher dimensional space, and to which standard image-processing algorithms may not apply. There are many important data analysis methods developed in this text for such statistical image problems. Examples abound throughout remote sensing (satellite data mapping, data assimilation, climate-change studies, land use), medical imaging (organ segmentation, anomaly detection), computer vision (image classification, segmentation), and other 2D/3D problems (biological imaging, porous media). The goal, then, of this text is to address methods for solving multidimensional statistical problems. The text strikes a balance between mathematics and theory on the one hand, versus applications and algorithms on the other, by deliberately developing the basic theory (Part I), the mathematical modeling (Part II), and the algorithmic and numerical methods (Part III) of solving a given problem. The particular emphases of the book include inverse problems, multidimensional modeling, random fields, and hierarchical methods. Paul Fieguth is a professor in Systems Design Engineering at the University of Waterloo in Ontario, Canada. He has longstanding research interests in statistical signal and image processing, hierarchical algorithms, data fusion, and the interdisciplinary applications of such methods, particularly to problems in medical imaging, remote sensing, and scientific imaging.

Keywords

Image processing Matlab Multidimensional modeling Random fields Spatial statistics

Authors and affiliations

  • Paul Fieguth
    • 1
  1. 1.Faculty of Engineering, Department of Systems Design EngineeringUniversity of WaterlooWaterlooCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-7294-1
  • Copyright Information Springer Science+Business Media, LLC 2011
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4419-7293-4
  • Online ISBN 978-1-4419-7294-1
  • Series Print ISSN 1613-9011
  • About this book