Abstract
We propose an accurate method for image matching in scenes including repetitive patterns like buildings, walls, and so on. We construct our matching method with two phases: matching between the elements of repetitive regions; matching between the points in the remained regions. We first detect the elements of repetitive patterns in each image and find matches between the elements in the regions without using any descriptors depended on a view-point. We then find matches between the points in the remained regions of the two images using the informations of the detected matches. The advantage of our method is to use an efficient matching information in the repetitive patterns. We show the effectiveness of our method by real image examples.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. 4th Alvey Vision Conf., Manchester, U.K, pp. 147–151 (1988)
Kanazawa, Y., Kanatani, K.: Robust image matching preserving global consistency. In: Proc. 6th Asian Conf. Comput, Jeju Island, Korea, pp. 1128–1133 (2004)
Lowe, D.: Distinctive image features from scale-invariant keypoint. Int. J. Comput. Vision 60(2), 91–110 (2004)
Matas, J., et al.: Robust wide baseline stereo from maximally stable extremal regions. In: Proc. 13th British Machine Vision Conf., Cardiff, U.K, pp. 384–393 (2002)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detector. Int. J. Comput. Vision 60(1), 63–86 (2004)
Mikolajczyk, K., et al.: A comparizon of affine region detectors. Int. J. Comput. Vision 65(1–2), 43–72 (2005)
Kanazawa, Y., Uemura, K.: Wide baseline matching using triplet vector descriptor. In: Proc. 17th British Machine Vision Conf., Edinburgh, U.K, vol. 1, pp. 267–276 (2006)
Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Patt. Anal. Mach. Intell. 13(6), 583–598 (1991)
Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381–395 (1981)
Roerdink, M.: The watershed transform: Definitions, algorithms and parallelization strategies. FUNDINF: Fundamenta Informatica 41 (2000)
Hartley, R., Zisserman, A.: Multiple View Geometry. Cambridge University press, Cambridge (2000)
Kanatani, K., Ohta, N., Kanazawa, Y.: Optimal homography computation with a reliability measure. IEICE Trans. Inf. & Syst. E83-D(7), 1369–1374 (2000)
Kanazawa, Y., Sakamoto, T., Kawakami, H.: Robust 3-d reconstruction using one or more homographies with uncalibrated stereo. In: Proc. 6th Asian Conf. Comput. Vision, Jeju Island, Korea, pp. 503–508 (2004)
Kanatani, K.: Optimal fundamental matrix computation: algorithm and reliability analysis. In: Proc. 6th Symposium on Sensing via Imaging Information (SSII), Yokohama, Japan, pp. 291–296 (2000)
Sugaya, Y., Kanatani, K., Kanazawa, Y.: Generating dense point matches using epipolar geometry. Memoirs of the Faculty of Engineering, Okayama University 40, 44–57 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kamiya, S., Kanazawa, Y. (2008). Accurate Image Matching in Scenes Including Repetitive Patterns. In: Sommer, G., Klette, R. (eds) Robot Vision. RobVis 2008. Lecture Notes in Computer Science, vol 4931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78157-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-78157-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78156-1
Online ISBN: 978-3-540-78157-8
eBook Packages: Computer ScienceComputer Science (R0)