The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Cross-Based Local Stereo Matching Using Orthogonal Integral Images
Abstract
We propose an area-based local stereo matching algorithm for accurate disparity estimation across all image regions. A well-known challenge to local stereo methods is to decide an appropriate support window for the pixel under consideration, adapting the window shape or the pixelwise support weight to the underlying scene structures. Our stereo method tackles this problem with two key contributions. First, for each anchor pixel an upright cross local support skeleton is adaptively constructed, with four varying arm lengths decided on color similarity and connectivity constraints. Second, given the local cross-decision results, we dynamically construct a shape-adaptive full support region on the fly, merging horizontal segments of the crosses in the vertical neighborhood. Approximating image structures accurately, the proposed method is among the best performing local stereo methods according to the benchmark Middlebury stereo evaluation. Additionally, it reduces memory consumption significantly thanks to our compact local cross representation. To accelerate matching cost aggregation performed in an arbitrarily shaped 2-D region, we also propose an orthogonal integral image technique, yielding a speedup factor of 5-15 over the straightforward integration.
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Cites
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
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
High-quality video view interpolation using a layered representation
Zitnick, C. L., Kang, S. B., Uyttendaele, M., Winder, S., & Szeliski, R. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015766 · Cited by 1,385
Adaptive support-weight approach for correspondence search
Yoon, K.-J., & Kweon, I. S. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.70 · Cited by 1,194
Summed-area tables for texture mapping
Crow, F. C. (1984) · Proceedings of the 11th annual conference on Computer graphics and interactive techniques · doi:10.1145/800031.808600 · Cited by 1,125
Multi-camera Scene Reconstruction via Graph Cuts
Kolmogorov, V., & Zabih, R. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47977-5_6 · Cited by 739
Summed-area tables for texture mapping
Crow, F. C. (1984) · ACM SIGGRAPH Computer Graphics · doi:10.1145/964965.808600 · Cited by 661
High-quality video view interpolation using a layered representation
Zitnick, C. L., Kang, S. B., Uyttendaele, M., Winder, S., & Szeliski, R. (2004) · ACM SIGGRAPH 2004 Papers · doi:10.1145/1186562.1015766 · Cited by 367
Handling occlusions in dense multi-view stereo
Kang, S. B., Szeliski, R., & Chai, J. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990462 · Cited by 353
Efficient stereo with multiple windowing
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Cited by
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., & Brox, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.438 · Cited by 2,488
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
Intel(R) RealSense(TM) Stereoscopic Depth Cameras
Keselman, L., Woodfill, J. I., Grunnet-Jepsen, A., & Bhowmik, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.167 · Cited by 448
Massively parallel GPU computing for fast stereo correspondence algorithms
Wang, H., Zhang, N., Créput, J.-C., Ruichek, Y., & Moreau, J. (2016) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2016.03.002 · Cited by 4
Metadata
- Authors
- Zhang, K., Lu, J., & Lafruit, G.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2009-07
- Volume
- 19
- Issue
- 7
- Pages
- 1073-1079
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2113873920
- Cited by
- 503
- References
- 16
- FWCI
- 19.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, K., Lu, J., & Lafruit, G. (2009). Cross-Based Local Stereo Matching Using Orthogonal Integral Images. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/tcsvt.2009.2020478
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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).
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