The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A pixel dissimilarity measure that is insensitive to image sampling
Abstract
Because of image sampling, traditional measures of pixel dissimilarity can assign a large value to two corresponding pixels in a stereo pair, even in the absence of noise and other degrading effects. We propose a measure of dissimilarity that is provably insensitive to sampling because it uses the linearly interpolated intensity functions surrounding the pixels. Experiments on real images show that our measure alleviates the problem of sampling with little additional computational overhead.
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Citations
Cites
Non-parametric local transforms for computing visual correspondence
Zabih, R., & Woodfill, J. (1994) · Lecture Notes in Computer Science · doi:10.1007/bfb0028345 · Cited by 1,763
A Maximum Likelihood Stereo Algorithm
Cox, I. J., Hingorani, S., Rao, S. B., et al. (1996) · Computer Vision and Image Understanding · doi:10.1006/cviu.1996.0040 · Cited by 473
Depth Discontinuities by Pixel-to-Pixel Stereo
Birchfield, S., & Tomasi, C. (1999) · International Journal of Computer Vision · doi:10.1023/a:1008160311296 · Cited by 417
Self-citationOcclusions and binocular stereo
Geiger, D., Ladendorf, B., & Yuille, A. (1992) · Lecture Notes in Computer Science · doi:10.1007/3-540-55426-2_48 · Cited by 243
Depth discontinuities by pixel-to-pixel stereo
Birchfield, S. T., & Tomasi, C. (2002) · International Conference on Computer Vision · doi:10.1109/iccv.1998.710850 · Cited by 229
Self-citationPractical Real-Time Imaging Stereo Matcher
Nishihara, H. K. (1984) · Optical Engineering · doi:10.1117/12.7973334 · Cited by 209
Disparity-space images and large occlusion stereo
Intille, S. S., & Bobick, A. F. (1994) · Lecture Notes in Computer Science · doi:10.1007/bfb0028349 · Cited by 169
Occlusions and binocular stereo
Geiger, D., Ladendorf, B., & Yuille, A. (1995) · International Journal of Computer Vision · doi:10.1007/bf01679683 · Cited by 169
A Bayesian treatment of the stereo correspondence problem using half-occluded regions
Belhumeur, P. N., & Mumford, D. (2003) · 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.1992.223143 · Cited by 153
Matching images by comparing their gradient fields
Scharstein, D. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1994.576363 · Cited by 81
Cited by
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
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
Tola, E., Lepetit, V., & Fua, P. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.77 · Cited by 1,405
Evaluation of Cost Functions for Stereo Matching
Hirschmuller, H., & Scharstein, D. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383248 · Cited by 1,122
Computing visual correspondence with occlusions using graph cuts
Kolmogorov, V., & Zabih, R. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937668 · Cited by 1,119
Metadata
- Authors
- Birchfield, S., & Tomasi, C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1998-04
- Volume
- 20
- Issue
- 4
- Pages
- 401-406
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2137017911
- Cited by
- 585
- References
- 10
- FWCI
- 4.65
- Type
- journal article
- Language
- en
- Open access
- closed
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- Birchfield, S., & Tomasi, C. (1998). A pixel dissimilarity measure that is insensitive to image sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.677269
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
