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Recovering Occlusion Boundaries from a Single Image
Open accessAbstract
Occlusion reasoning, necessary for tasks such as navigation and object search, is an important aspect of everyday life and a fundamental problem in computer vision. We believe that the amazing ability of humans to reason about occlusions from one image is based on an intrinsically 3D interpretation. In this paper, our goal is to recover the occlusion boundaries and depth ordering of free-standing structures in the scene. Our approach is to learn to identify and label occlusion boundaries using the traditional edge and region cues together with 3D surface and depth cues. Since some of these cues require good spatial support (i.e., a segmentation), we gradually create larger regions and use them to improve inference over the boundaries. Our experiments demonstrate the power of a scene-based approach to occlusion reasoning.
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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
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
(2005) · Cited by 981
Understanding Line drawings of Scenes with Shadows
Waltz, D. L. (1975) · Cited by 917
Putting Objects in Perspective
Hoiem, D., Efros, A. A., & Hebert, M. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0137-5 · Cited by 803
Self-citationContextual Priming for Object Detection
Torralba, A. B. (2003) · International Journal of Computer Vision · doi:10.1023/a:1023052124951 · Cited by 792
Logistic Regression, AdaBoost and Bregman Distances
Collins, M., Schapire, R. E., & Singer, Y. (2002) · Machine Learning · doi:10.1023/a:1013912006537 · Cited by 686
Clowes, M. B. (1971) · Artificial Intelligence · doi:10.1016/0004-3702(71)90005-1 · Cited by 678
Recovering Surface Layout from an Image
Hoiem, D., Efros, A. A., & Hebert, M. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0031-y · Cited by 678
Self-citationUsing Multiple Segmentations to Discover Objects and their Extent in Image Collections
Russell, B. C., Freeman, W. T., Efros, A. A., Šivic, J., & Zisserman, A. (2006) · 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.2006.326 · Cited by 618
Self-citation
Research works printed but not linked: 3
- martin, 2002, Learning to find brightness and texture boundaries in natural images, NIPS
- gibson, 1968, Purple Perils, The perception of surface layout A classification of types
- roberts, 1965, Machine perception of 3-D solids, OEOIP, 159
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Kluckner, S., & Bischof, H. (2009) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2009.5457638 · Cited by 16
Metadata
- Authors
- Hoiem, D., Stein, A. N., Efros, A. A., & Hebert, M.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- 2007 IEEE 11th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2007
- Pages
- 1-8
- OpenAlex
- W2103380420
- Cited by
- 206
- References
- 35
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 3 · 2021: 8 · 2020: 9 · 2019: 3 · 2018: 4 · 2017: 7 · 2016: 15 · 2015: 19 · 2014: 14 · 2013: 17 · 2012: 14
- FWCI
- 17.24
- Type
- conference paper
- Language
- en
- Open access
- green
- figshare.com
- Licence
- cc-by
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- KiltHub Repository (submittedVersion)
- Figshare (submittedVersion, cc-by)
- KiltHub Repository
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- occlusion boundaries, occlusion reasoning, depth ordering, free-standing structures, depth cues, single image
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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