VISUAL AND SPATIAL ANALYSIS:
Advances in Data Mining,
Reasoning and Problem Solving

Conflation of images with algebraic structures
Boris Kovalerchuk, James Schwing, William Sumner, and Richard Chase

Sections

1.   Introduction

2.   Algebraic invariants

3.   Feature correlating algorithms

4.   Conflation measures

5.   Generalization: image structural similarity

6.   Conclusion

7.   Acknowledgements

8.   Exercises

9.   References

Abstract

Spatial decision making and analysis heavily depend on quality of image registration and conflation. An approach to conflation/registration of images that does not depend on identifying common points is being developed. It uses the method of algebraic invariants to provide a common set of coordinates to images using chains of line segments formally described as polylines. It is shown the invariant algebraic properties of the polylines provide sufficient information to automate conflation. When there are discrepancies between the image data sets, robust measures of the possibility and quality of match (measures of correctness) are necessary. Such measures are offered based on image structural characteristics. These measures may also be used to mitigate the effects of sensor and observational artifacts. This new approach grew from a careful review of conflating processes based on computational topology and geometry. This chapter describes the theory of algebraic invariants, a conflation/registration method with measures of correctness of feature matching.

Home

Overview

Table of Contents

Preface

Authors List

Links Page

Publisher Flyer

Springer (Buying Info)