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-669899f699-7tmb6 Total loading time: 0 Render date: 2025-04-29T05:30:15.379Z Has data issue: false hasContentIssue false

10 - Feature-based image to image registration

from PART III - Feature Matching and Strategies for Image Registration

Published online by Cambridge University Press:  03 May 2011

By
Venu Madhav Govindu  and
Rama Chellappa
Edited by
Jacqueline Le Moigne ,
Nathan S. Netanyahu  and
Roger D. Eastman
Venu Madhav Govindu
Affiliation:
Indian Institute of Science, India
Rama Chellappa
Affiliation:
University of Maryland, Maryland
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

Recent advances in computer vision address the problem of registration of multiple images or entire video sequences. Such registration methods have a wide variety of application in constructing mosaics, video summarization, site modeling and as preprocessing for tasks such as object tracking and recognition. In this chapter we present a variety of registration techniques that utilize image features such as points and contours. Computational issues such as robustness to data outliers and recent developments in accurate feature extraction are discussed. A correspondenceless method that works on multimodal images is outlined. We also present approaches that efficiently utilize the information redundancy in a sequence of images to solve the problem of image registration. All of these methods are illustrated with appropriate examples.

Introduction

The underlying geometry of image formation has been well studied over the recent years in the discipline of computer vision (Hartley and Zisserman, 2004). This understanding of the image geometry has been accompanied by increasingly sophisticated computational models that can be solved on modern hardware. Many methods and ideas developed for solving various aspects of the motion estimation problem in computer vision are applicable to problems relating to image registration. In particular, image features like points, edges, and contours have been used in a range of applications like the construction of mosaics from video sequences, shape estimation, object tracking and recognition, etc. In this chapter, we describe a variety of methods dedicated to utilizing image features for solving the problem of registration.

Type
Chapter
Information
Image Registration for Remote Sensing , pp. 215 - 239
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.)

Book purchase

Temporarily unavailable

References

Brown, L. G. (1992). A survey of image registration techniques. ACM Computing Surveys, 24(4), 325–376.CrossRefGoogle Scholar
Chellappa, R., Zheng, Q., Burlina, P., Shekhar, C., and Eom, K. B. (1997). On the positioning of multisensor imagery for exploitation and target recognition. Proceedings of the IEEE, 85(1), 120–138.CrossRefGoogle Scholar
Chum, O. and Matas, J. (2005). Matching with PROSAC – progressive sample consensus. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, Vol. 1, pp. 220–226.CrossRef
Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395.CrossRefGoogle Scholar
Gold, S., Rangarajan, A., Lu, C. P., Pappu, S., and Mjolsness, E. (1998). New algorithms for 2D and 3D point matching: Pose estimation and correspondence. Pattern Recognition, 31(8), 1019–1031.CrossRefGoogle Scholar
Govindu, V. M. (2001). Combining two-view constraints for motion estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, pp. 218–225.CrossRef
Govindu, V. M. (2004). Lie-algebraic averaging for globally consistent motion estimation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, pp. 684–691.CrossRef
Govindu, V. and Shekhar, C. (1999). Alignment using distributions of local geometric properties. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(2), 1031–1043.CrossRefGoogle Scholar
Harris, C. and Stephens, M. (1988). A combined corner and edge detector. In Proceedings of the Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151.CrossRef
Hartley, R. and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, 2nd edn. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Li, H., Manjunath, B. S., and Mitra, S. (1995). A contour-based approach to multi-sensor image registration. IEEE Transactions on Image Processing, 4, 320–334.CrossRefGoogle Scholar
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
Malis, E. and Cipolla, R. (2000). Multi-view constraints between collineations: Application to self-calibration from unknown planar structures. In Proceedings of the Sixth European Conference on Computer Vision, Dublin, Ireland; Computer Vision – ECCV 2000, D. Vernon, ed. Berlin: Springer, Vol. 2, pp. 610–624.CrossRef
Mikolajczyk, K. and Schmid, C. (2003). A performance evaluation of local descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, Vol. 2, pp. 257–263.CrossRef
Rousseeuw, P. J. and Leroy, A. M. (1987). Robust Regression and Outlier Detection. New York: Wiley.CrossRefGoogle Scholar
Schmid, C., Mohr, R., and Baukhage, C. (2000). Evaluation of interest point detectors. International Journal of Computer Vision, 37(2), 151–172.CrossRefGoogle Scholar
Shekhar, C., Govindu, V., and Chellappa, R. (1999). Multisensor image registration by feature consensus. Pattern Recognition, 32(1), 39–52.CrossRefGoogle Scholar
Shi, J. and Tomasi, C. (1994). Good features to track. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, Washington, pp. 593–600.
Szeliski, R. (2006). Image alignment and stitching. In N. Paragios, Y. Chen, and O. Faugeras, eds., Mathematical Models in Computer Vision: The Handbook. New York: Springer, pp. 273–292.CrossRef
Torr, P. and Zisserman, A. (2000). MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78(1), 138–156.CrossRefGoogle Scholar
Viola, P. and Wells, W. (1997). Alignment by maximization of mutual information. International Journal of Computer Vision, 24(2), 137–154.CrossRefGoogle Scholar
Zhang, Z., Deriche, R., Faugeras, O., and Luong, Q. T. (1995). A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 78(1–2), 87–119.CrossRefGoogle Scholar
Zheng, Q. and Chellappa, R. (1993). A computational vision approach to image registration. IEEE Transactions on Image Processing, 2(3), 311–326.CrossRefGoogle ScholarPubMed
Zitová, B. and Flusser, J. (2003). Image registration methods: A survey. Image and Vision Computing, 21(11), 977–1000.CrossRefGoogle Scholar

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
×