Dense Correspondences across Scenes and Scales

IEEE Transactions on Pattern Analysis and Machine Intelligence
Moria Tau, Tal Hassner

Abstract

We seek a practical method for establishing dense correspondences between two images with similar content, but possibly different 3D scenes. One of the challenges in designing such a system is the local scale differences of objects appearing in the two images. Previous methods often considered only few image pixels; matching only pixels for which stable scales may be reliably estimated. Recently, others have considered dense correspondences, but with substantial costs associated with generating, storing and matching scale invariant descriptors. Our work is motivated by the observation that pixels in the image have contexts-the pixels around them-which may be exploited in order to reliably estimate local scales. We make the following contributions. (i) We show that scales estimated in sparse interest points may be propagated to neighboring pixels where this information cannot be reliably determined. Doing so allows scale invariant descriptors to be extracted anywhere in the image. (ii) We explore three means for propagating this information: using the scales at detected interest points, using the underlying image information to guide scale propagation in each image separately, and using both images together. Finally, (iii), we p...Continue Reading

References

Sep 24, 2002·The Annals of Pharmacotherapy·Rafael Mikolajczyk
Mar 21, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ashutosh SaxenaAndrew Y Ng
Jun 2, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ronen BasriLihi Zelnik-Manor
Jun 19, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yasutaka Furukawa, Jean Ponce
Aug 18, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ce LiuAntonio Torralba
Jun 29, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ce LiuAntonio Torralba

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Citations

Jul 23, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Tal HassnerLihi Zelnik-Manor
Jul 15, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Bumsub HamJean Ponce

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