The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Ordinal measures for image correspondence
Abstract
We present ordinal measures of association for image correspondence in the context of stereo. Linear correspondence measures like correlation and the sum of squared difference between intensity distributions are known to be fragile. Ordinal measures which are based on relative ordering of intensity values in windows-rank permutations-have demonstrable robustness. By using distance metrics between two rank permutations, ordinal measures are defined. These measures are independent of absolute intensity scale and invariant to monotone transformations of intensity values like gamma variation between images. We have developed simple algorithms for their efficient implementation. Experiments suggest the superiority of ordinal measures over existing techniques under nonideal conditions. These measures serve as a general tool for image matching that are applicable to other vision problems such as motion estimation and texture-based image retrieval.
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Cites
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Ordinal measures for visual correspondence
Bhat, D. N., & Nayar, S. K. (1996) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1996.517096 · Cited by 91
Self-citationA Rank Correlation Coefficient Resistant to Outliers
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3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Choy, C. B., Xu, D., Gwak, J., Chen, K., & Savarese, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_38 · Cited by 1,632
Imaging with terahertz radiation
Chan, W. L., Deibel, J., & Mittleman, D. M. (2007) · Reports on Progress in Physics · doi:10.1088/0034-4885/70/8/r02 · Cited by 965
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
Hirschmuller, H., & Scharstein, D. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.221 · Cited by 721
CENTRIST: A Visual Descriptor for Scene Categorization
Wu, J., & Rehg, J. M. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.224 · Cited by 709
Visual features of intermediate complexity and their use in classification
Ullman, S., Vidal-Naquet, M., & Sali, E. (2002) · Nature Neuroscience · doi:10.1038/nn870 · Cited by 600
Metadata
- Authors
- Bhat, D. N., & Nayar, S. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1998-04
- Volume
- 20
- Issue
- 4
- Pages
- 415-423
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2115520217
- Cited by
- 353
- References
- 9
- FWCI
- 7.23
- Type
- journal article
- Language
- en
- Open access
- closed
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