Abstract
This paper proposed a robust point matching method for wide baseline in order to achieve a large number of correct correspondences and high accuracy. To cope with large variations of scale and rotation, a feature descriptor, that is robust to scale and view-point, is added to the feature detection phase and it is included in the equations of the correspondence matrix that is central to the matching algorithm. Furthermore, the image window for normalized cross correlation is modified with adaptive scale and orientation. At the same time we remove from the matrix all the proximity information about the distance between points’ locations which is the source of mismatches. Thus, the proposed algorithm is invariant to changes of scale, rotation, light and partially invariant to viewpoint. Experimental results show that the proposed algorithm can be used for large scene variations and provide evidence of better performance.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Marr, D.: Early Processing of Visual Information. Philosophical Transactions of the Royal Society of London (B) 275, 483–524 (1976)
Zitov, B., Flusser, J.: Image Registration Methods: A Survey. Image and Vision Computing 21, 977–1000 (2003)
Xiaoye, L., Manduchi, R.: Wide Baseline Feature Matching Using the Cross Epipolar Ordering Constraint. In: Proceedings of IEEE Conference on CVPR, pp. 16–23. IEEE Press, New York (2004)
Pritchett, P., Zisserman, A.: Wide Baseline Stereo Matching. In: Proceedings of the International Conference on Computer Vision, pp. 754–760. IEEE Press, New York (1998)
Zhang, Z., Deriche, R., Faugeras, O., Luong, Q.T.: A Robust Technique for Matching two Uncalibrated Images through the Recovery of the Unknown Epipolar Geometry. Artificial Intelligence 78, 87–119 (1995)
Ferrari, V., Tuytelaars, T., Gool, L.V.: Wide-baseline multiple-view correspondences. In: Proceedings of IEEE Conference on CVPR, pp. 718–725. IEEE Press, New York (2003)
Carcassoni, M., Hancock, E.: Spectral correspondence for point pattern matching. Pattern Recognition 36, 193–204 (2003)
Sclaroff, S., Pentland, A.: Modal Matching for Correspondence and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 545–561 (1995)
Scott, G., Longuet-Higgins, H.: An algorithm for associating the features of two patterns. In: Proceedings of the Royal Society of London, pp. 21–26 (1991)
Pilu, M.: A Direct Method for Stereo Correspondence Based on Singular Value Decomposition. In: Proc. of IEEE Conference on CVPR, pp. 261–266. IEEE Press, New York (1997)
Lowe, D.: Distinctive Image Features from Scale-Invariant Key-Points. International Journal of Computer Vision 60, 91–110 (2004)
Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yue, S., Wang, Q., Zhao, R. (2008). Robust Wide Baseline Feature Point Matching Based on Scale Invariant Feature Descriptor. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-87442-3_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87440-9
Online ISBN: 978-3-540-87442-3
eBook Packages: Computer ScienceComputer Science (R0)