The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Deformable Spatial Pyramid Matching for Fast Dense Correspondences
Abstract
We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences. Dense matching methods typically enforce both appearance agreement between matched pixels as well as geometric smoothness between neighboring pixels. Whereas the prevailing approaches operate at the pixel level, we propose a pyramid graph model that simultaneously regularizes match consistency at multiple spatial extents-ranging from an entire image, to coarse grid cells, to every single pixel. This novel regularization substantially improves pixel-level matching in the face of challenging image variations, while the "deformable" aspect of our model overcomes the strict rigidity of traditional spatial pyramids. Results on Label Me and Caltech show our approach outperforms state-of-the-art methods (SIFT Flow [15] and Patch-Match [2]), both in terms of accuracy and run time.
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Cites
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Cited by
Learning to generate chairs with convolutional neural networks
Dosovitskiy, A., Springenberg, J. T., & Brox, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298761 · Cited by 640
Learning Correspondence From the Cycle-Consistency of Time
Wang, X., Jabri, A., & Efros, A. A. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00267 · Cited by 445
Shamwell, E. J., Nothwang, W. D., & Perlis, D. (2018) · Sensors · doi:10.3390/s18051427 · Cited by 4
Metadata
- Authors
- Kim, J., Liu, C., Sha, F., & Grauman, K.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 2307-2314
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2124861766
- Cited by
- 243
- References
- 3
- FWCI
- 21.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kim, J., Liu, C., Sha, F., & Grauman, K. (2013). Deformable Spatial Pyramid Matching for Fast Dense Correspondences. 2013 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2013.299
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