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Statistical Image Processing Techniques for Noisy Images

An Application-Oriented Approach

  • François Goudail
  • Philippe Réfrégier

Table of contents

  1. Front Matter
    Pages i-xiii
  2. François Goudail, Philippe Réfrégier
    Pages 1-7
  3. François Goudail, Philippe Réfrégier
    Pages 9-48
  4. François Goudail, Philippe Réfrégier
    Pages 49-87
  5. François Goudail, Philippe Réfrégier
    Pages 89-128
  6. François Goudail, Philippe Réfrégier
    Pages 129-168
  7. François Goudail, Philippe Réfrégier
    Pages 169-195
  8. François Goudail, Philippe Réfrégier
    Pages 197-239
  9. Back Matter
    Pages 241-254

About this book

Introduction

Statistical Processing Techniques for Noisy Images presents a statistical framework to design algorithms for target detection, tracking, segmentation and classification (identification). Its main goal is to provide the reader with efficient tools for developing algorithms that solve his/her own image processing applications. In particular, such topics as hypothesis test-based detection, fast active contour segmentation and algorithm design for non-conventional imaging systems are comprehensively treated, from theoretical foundations to practical implementations. With a large number of illustrations and practical examples, this book serves as an excellent textbook or reference book for senior or graduate level courses on statistical signal/image processing, as well as a reference for researchers in related fields.

Keywords

Excel Statistica Tracking algorithm algorithms classification detection image processing imaging segmentation

Authors and affiliations

  • François Goudail
    • 1
  • Philippe Réfrégier
    • 1
  1. 1.Fresnel InstituteENSPMMarseilleFrance

Bibliographic information