Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-6bf8c574d5-zc66z Total loading time: 0 Render date: 2025-03-09T15:45:36.092Z Has data issue: false hasContentIssue false

7 - Registration of multiview images

from PART III - Feature Matching and Strategies for Image Registration

Published online by Cambridge University Press:  03 May 2011

By
A. Ardeshir Goshtasby
Edited by
Jacqueline Le Moigne ,
Nathan S. Netanyahu  and
Roger D. Eastman
A. Ardeshir Goshtasby
Affiliation:
Wright State University, Ohio
Jacqueline Le Moigne
Affiliation:
NASA-Goddard Space Flight Center
Nathan S. Netanyahu
Affiliation:
Bar-Ilan University, Israel and University of Maryland, College Park
Roger D. Eastman
Affiliation:
Loyola University Maryland
Get access

Summary

Abstract

Multiview images of a 3D scene contain sharp local geometric differences. To register such images, a transformation function is needed that can accurately model local geometric differences between the images. A weighted linear transformation function for the registration of multiview images is proposed. Properties of this transformation function are explored and its accuracy in image registration is compared with accuracies of other transformation functions.

Introduction

Due to the acquisition of satellite images at a high altitude and relatively low resolution, overlapping images have very little to no local geometric differences, although global geometric differences may exist between them. Surface spline (Goshtasby, 1988) and multiquadric (Zagorchev and Goshtasby, 2006) transformation functions have been found to effectively model global geometric differences between overlapping satellite images.

High-resolution multiview images of a scene captured by low-flying aircrafts contain considerable local geometric differences. Local neighborhoods in the scene may appear differently in multiview images due to variation in local scene relief and difference in imaging view angle. Global transformation functions that successfully registered satellite images might not be able to satisfactorily register multiview aerial images.

A new transformation function for the registration of multiview aerial images is proposed. The transformation function is defined by a weighted sum of linear functions, each containing information about the geometric difference between corresponding local neighborhoods in the images.

Type
Chapter
Information
Image Registration for Remote Sensing , pp. 153 - 178
Publisher: Cambridge University Press
Print publication year: 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alfeld, P. (1989). Scattered data interpolation in three or more variables. In Ly-che, T. and Schumaker, L. L., eds., Mathematical Methods in Computer Aided Geometric Design. New York: Academic Press, pp. 1–33.Google Scholar
Barnhill, R. E. (1977). Representation and approximation of surfaces. In Rice, J. R., ed., Mathematical Software III. New York: Academic Press, pp. 69–120.CrossRefGoogle Scholar
Chui, H. and Rangarajan, A. (2003). A new point pattern matching algorithm for nonrigid registration. Computer Vision and Image Understanding, 89, 114–141.CrossRefGoogle Scholar
Dyn, N. (1987). Interpolation of scattered data by radial functions. In Chui, C. K., Schumaker, L. L., and Utrerus, F., eds., Topics in Multivariate Approximation. New York: Academic Press, pp. 47–61.CrossRefGoogle Scholar
Franke, R. (1982). Scattered data interpolation: Tests of some methods. Mathematics of Computation, 38, 181–200.Google Scholar
Franke, R. (1987). Recent advances in the approximation of surfaces from scattered data. In Chui, C. K., Schumaker, L. L., and Utrerus, F., eds., Topics in Multivariate Approximation, New York: Academic Press, pp. 79–98.CrossRefGoogle Scholar
Franke, R. and Nielson, G. (1980). Smooth interpolation of large sets of scattered data. International Journal of Numerical Methods in Engineering, 15, 1691–1704.CrossRefGoogle Scholar
Gordon, W. J. and Wixom, J. A. (1978). Shepard's method of ‘metric interpolation’ to bivariate and multivariate interpolation. Mathematics of Computation, 32, 253–264.Google Scholar
Goshtasby, A. (1986). Piecewise linear mapping functions for image registration. Pattern Recognition, 19, 459–466.CrossRefGoogle Scholar
Goshtasby, A. (1987). Piecewise cubic mapping functions for image registration. Pattern Recognition, 20, 525–533.CrossRefGoogle Scholar
Goshtasby, A. (1988). Registration of images with geometric distortions. IEEE Transactions on Geoscience and Remote Sensing, 26, 50–64.CrossRefGoogle Scholar
Goshtasby, A. (2005). 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications. New Hoboken, NJ: Wiley Press.Google Scholar
Grosse, E. (1990). A catalogue of algorithms for approximation. In Mason, J. and Cox, M., eds., Algorithms for Approximation II. London: Chapman and Hall, pp. 479–514.CrossRefGoogle Scholar
Harder, R. L. and Desmarais, R. N. (1972). Interpolation using surface spline. Journal of Aircraft, 9, 189–191.CrossRefGoogle Scholar
Hardy, R. L. (1990). Theory and applications of the multiquadric-biharmonic method: 20 years of discovery 1969–1988. Computers and Mathematics with Applications, 19, 163–208.CrossRefGoogle Scholar
Jackson, I. R. H. (1988). Convergence properties of radial basis functions. Constructive Approximation, 4, 243–264.CrossRefGoogle Scholar
Kanade, T. and Okutomi, M. (1994). A stereo matching algorithm with an adaptive window: Theory and experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 920–932.CrossRefGoogle Scholar
LeMéhauté, A., Schumaker, L. L., and Traversoni, L. (1996). Multivariate scattered data fitting. Journal of Computational and Applied Mathematics, 73, 1–4.CrossRefGoogle Scholar
Mount, D. M., Netanyahu, N. S., and Moigne, J. (1999). Efficient algorithms for robust feature matching. Pattern Recognition, 32, 17–38.CrossRefGoogle Scholar
Mundy, J. L. and Zisserman, A. (1992). Geometric Invariance in Computer Vision. Cambridge, MA: MIT Press.Google Scholar
Olson, C. F. (1997). Efficient pose clustering using a randomized algorithm. International Journal of Computer Vision, 23, 131–147.CrossRefGoogle Scholar
O'Neill, M. and Denos, M. (1995). Automated system for coarse-to-fine pyramidal area correlation stereo matching. Image and Vision Computing, 14, 225–236.CrossRefGoogle Scholar
Orrite, C. and Herrero, J. E. (2004). Shape matching of partially occluded curves invariant under projective transformation. Computer Vision and Image Understanding, 93, 34–64.CrossRefGoogle Scholar
Renka, R. J. (1988). Multivariate interpolation of large sets of scattered data. ACM Transactions on Mathematical Software, 14, 139–148.CrossRefGoogle Scholar
Sabin, M. A. (1980). Contouring: A review of methods for scattered data. In Brodlie, K., ed., Mathematical Methods in Computer Graphics and Design. New York: Academic Press, pp. 63–86.Google Scholar
Schumaker, L. L. (1976). Fitting surfaces to scattered data. In Lorentz, G. G., Chui, C. K., and Schumaker, L. L., eds., Approximation Theory II. New York: Academic Press, pp. 203–268.Google Scholar
Shepard, D. (1968). A two-dimensional interpolation function for irregularly spaced data. In Proceedings of the Twenty Third National Conference on ACM. New York, pp. 517–523.Google Scholar
Stockman, G., Kopstein, S., and Benett, S. (1982). Matching images to models for registration and object detection via clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4, 229–241.CrossRefGoogle ScholarPubMed
Tomasi, C. and Kanade, T. (1992). Shape and motion from image streams under orthography: A factorization method. International Journal of Computer Vision, 9, 137–154.CrossRefGoogle Scholar
Umeyama, S. (1993). Parametrized point pattern matching and its application to recognition of object families. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, 136–144.CrossRefGoogle Scholar
Zagorchev, L. and Goshtasby, A. (2006). A comparative study of transformation functions for image registration. IEEE Transactions on Image Processing, 15, 529–538.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×