The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Adaptive support-weight approach for correspondence search
Abstract
We present a new window-based method for correspondence search using varying support-weights. We adjust the support-weights of the pixels in a given support window based on color similarity and geometric proximity to reduce the image ambiguity. Our method outperforms other local methods on standard stereo benchmarks.
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Cites
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Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 1×
“Equation (6) used for support-weight computation is very similar to the functions used for computing adaptive weights in structurepreserving noise filters such as those in [24], [25.”
MethodA Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
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Cited by
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
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
Spatial-Depth Super Resolution for Range Images
Yang, Q., Yang, R., Davis, J., & Nister, D. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383211 · Cited by 745
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
Recurrent MVSNet for High-Resolution Multi-View Stereo Depth Inference
Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., & Quan, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00567 · Cited by 666
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Massively Parallel Multiview Stereopsis by Surface Normal Diffusion
Galliani, S., Lasinger, K., & Schindler, K. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.106 · Cited by 599
Bilateral Filtering: Theory and Applications
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Cross-Based Local Stereo Matching Using Orthogonal Integral Images
Zhang, K., Lu, J., & Lafruit, G. (2009) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2009.2020478 · Cited by 503
Fast cost-volume filtering for visual correspondence and beyond
Rhemann, C., Hosni, A., Bleyer, M., Rother, C., & Gelautz, M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995372 · Cited by 485
Optical flow modeling and computation: A survey
Fortun, D., Bouthemy, P., & Kervrann, C. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.02.008 · Cited by 431
A non-local cost aggregation method for stereo matching
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Review of stereo vision algorithms and their suitability for resource-limited systems
Tippetts, B., Lee, D. J., Lillywhite, K., & Archibald, J. (2016) · Journal of Real-Time Image Processing · doi:10.1007/s11554-012-0313-2 · Cited by 274
Robust guided image filtering using Nonconvex potentials
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Massively parallel GPU computing for fast stereo correspondence algorithms
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Metadata
- Authors
- Yoon, K.-J., & Kweon, I. S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2006-04
- Volume
- 28
- Issue
- 4
- Pages
- 650-656
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2123782500
- Cited by
- 1,194
- References
- 3
- FWCI
- 39.60
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
