Precision Landmark Location for Machine Vision and Photogrammetry

Finding and Achieving the Maximum Possible Accuracy

  • Brian S.R. Armstrong
  • José A. Gutierrez

Table of contents

About this book

Introduction

The applications of image-based measurement are many and various: image-guided surgery, mobile-robot navigation, component alignment, part inspection and photogrammetry, among others. In all these applications, landmarks are detected and located in images, and measurements made from those locations.

Precision Landmark Location for Machine Vision and Photogrammetry addresses the ubiquitous problem of measurement error associated with determining the location of landmarks in images. With a detailed model of the image formation process and landmark location estimation, the Cramér–Rao Lower Bound (CRLB) theory of statistics is applied to determine the least possible measurement uncertainty in a given situation.

This monograph provides the reader with:

• the most complete treatment to date of precision landmark location and the engineering aspects of image capture and processing;

• detailed theoretical treatment of the CRLB;

• a software tool for analyzing the potential performance-specific camera/lens/algorithm configurations;

• two novel algorithms which achieve precision very close to the CRLB;

• an experimental method for determining the accuracy of landmark location;

• downloadable MATLAB® package to assist the reader with applying theoretically-derived results to practical engineering configurations.

All of this adds up to a treatment that is at once theoretically sound and eminently practical.

Precision Landmark Location for Machine Vision and Photogrammetry will be of great interest to computer scientists and engineers working with and/or studying image processing and measurement. It includes cutting-edge theoretical developments and practical tools so it will appeal to research investigators and system designers.

Keywords

LED MATLAB Performance algorithms image processing navigation robot statistics

Authors and affiliations

  • Brian S.R. Armstrong
    • 1
  • José A. Gutierrez
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of Wisconsin – MilwaukeeMilwaukeeUSA
  2. 2.Emerson CorporationSt. LouisUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84628-913-2
  • Copyright Information Springer-Verlag 2008
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-84628-912-5
  • Online ISBN 978-1-84628-913-2
  • About this book