Abstract
The SIFT flow algorithm has been widely used for the image matching/ registration task and it is particularly effective in handling image pairs from similar scenes but with different object configurations. The way in which the dense SIFT features are computed at a fixed scale in the SIFT flow method might however limit its capability of dealing with scenes having great scale changes. In this work, we propose a simple, intuitive, and effective approach, Scale-Space SIFT flow, to deal with the large object scale differences. We introduce a scale field to the SIFT flow function to automatically explore the scale changes. Our approach achieves a similar performance as the SIFT flow method for natural scenes but obtains significant improvement for the images with large scale differences. Compared with a recent method that addresses a similar problem, our approach shows its advantage being more effective and efficient.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (grant No. 61222308) and the NSF awards IIS-1216528 (IIS-1360566), IIS-0844566 (IIS-1360568), and IIS-0917141.
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Qiu, W., Wang, X., Bai, X., Yuille, A., Tu, Z. (2016). Scale-Space SIFT Flow. In: Hassner, T., Liu, C. (eds) Dense Image Correspondences for Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-319-23048-1_4
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DOI: https://doi.org/10.1007/978-3-319-23048-1_4
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