The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Visual object-action recognition: Inferring object affordances from human demonstration
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Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
The Ecological Approach to Visual Perception
Bornstein, M. H., & Gibson, J. J. (1980) · The Journal of Aesthetics and Art Criticism · doi:10.2307/429816 · Cited by 22,935
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., & Bottou, L. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315150 · Cited by 1,324
Cited by
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response
Koppula, H. S., & Saxena, A. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2430335 · Cited by 596
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
Do, T.-T., Nguyen, A., & Reid, I. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460902 · Cited by 305
Affordance detection of tool parts from geometric features
Myers, A., Teo, C. L., Fermuller, C., & Aloimonos, Y. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139369 · Cited by 273
Interactive perception: Leveraging action in perception and perception in action
Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., & Sukhatme, G. S. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2721939 · Cited by 227
Grounded Human-Object Interaction Hotspots From Video
Nagarajan, T., Feichtenhofer, C., & Grauman, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00878 · Cited by 147
Metadata
- Authors
- Kjellström, H., Romero, J., & Kragić, D.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2011-01
- Volume
- 115
- Issue
- 1
- Pages
- 81-90
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2149173366
- Cited by
- 232
- References
- 4
- FWCI
- 23.90
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Visual object-action recognition: Inferring object affordances from human demonstration. https://app.cochat.ai/papers/10.1016/j.cviu.2010.08.002
- The paper
- Kjellström, H., Romero, J., & Kragić, D. (2011). Visual object-action recognition: Inferring object affordances from human demonstration. Computer Vision and Image Understanding. https://doi.org/10.1016/j.cviu.2010.08.002
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.
