Abstract
SIFT flow is a method to align an image to its neighbors in a large image collection consisting of a variety of scenes. Originally proposed for cross-scene alignment, it has good potential for robust dense matching between images of a scene taken under large viewing condition changes. However, this potential has not been fully exploited due to dense SIFT in SIFT flow is extracted under single pre-defined scale. In this paper, we explore this potential and propose new algorithms to select proper scales while applying SIFT flow for dense image matching. By studying the behavior of SIFT flow under scale changes, we propose two new concepts, namely, the optimal relative scale factor (ORSF) and the optimal matching scale (OMS) using gradient-enhanced normalized mutual information. ORSF and OMS define the most proper scales during SIFT flow matching. Suboptimal and optimal methods for estimating ORSF and OMS are proposed. It is shown that by applying ORSF and OMS, the accuracy of SIFT flow for dense image matching is greatly improved on images with significant scale changes.
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References
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. In: IEEE International Conference on Computer Vision (ICCV), pp. 1–8 (2007)
Liu, C., Yuen, J., Torralba, A., Sivic, J., Freeman, W.T.: SIFT flow: Dense correspondence across different scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 28–42. Springer, Heidelberg (2008)
Hassner, T., Mayzels, V., Zelnik-Manor, L.: On sifts and their scales. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1522–1528 (2012)
Tuytelaars, T.: Dense interest points. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2281–2288 (2010)
Xu, L., Dai, Z., Jia, J.: Scale invariant optical flow. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 385–399. Springer, Heidelberg (2012)
Barnes, C., Shechtman, E., Goldman, D.B., Finkelstein, A.: The generalized patchmatch correspondence algorithm. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 29–43. Springer, Heidelberg (2010)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual information based registration of medical images: a survey. IEEE Transactions on Medical Imaging (22), 986–1004 (2003)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Image registration by maximization of combined mutual information and gradient information. IEEE Transcations on Medical Imaging (19), 809–814 (2000)
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Li, S., Xiong, W. (2013). Scale Selection in SIFT Flow for Robust Dense Image Matching. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_10
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DOI: https://doi.org/10.1007/978-3-319-03731-8_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03730-1
Online ISBN: 978-3-319-03731-8
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