The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Color invariance
Abstract
This paper presents the measurement of colored object reflectance, under different, general assumptions regarding the imaging conditions. We exploit the Gaussian scale-space paradigm for color images to define a framework for the robust measurement of object reflectance from color images. Object reflectance is derived from a physical reflectance model based on the Kubelka-Munk theory for colorant layers. Illumination and geometrical invariant properties are derived from the reflectance model. Invariance and discriminative power of the color invariants is experimentally investigated, showing the invariants to be successful in discounting shadow, illumination, highlights, and noise. Extensive experiments show the different invariants to be highly discriminative, while maintaining invariance properties. The presented framework for color measurement is well-founded in the physics of color as well as in measurement science. Hence, the proposed invariants are considered more adequate for the measurement of invariant color features than existing methods.
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Citations
Cites
Koenderink, J. J. (1984) · Biological Cybernetics · doi:10.1007/bf00336961 · Cited by 2,452
Cited by
Selective Search for Object Recognition
Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980
Self-citationA Feature-Enriched Completely Blind Image Quality Evaluator
Zhang, L., Zhang, L., & Bovik, A. C. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2426416 · Cited by 1,311
Multi-spectral SIFT for scene category recognition
Brown, M., & Susstrunk, S. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995637 · Cited by 469
Performance evaluation of local colour invariants
Burghouts, G. J., & Geusebroek, J.-M. (2009) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2008.07.003 · Cited by 295
Self-citationDehazing Evaluation: Real-World Benchmark Datasets, Criteria, and Baselines
Zhao, S., Zhang, L., Huang, S., Shen, Y., & Zhao, S. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2995264 · Cited by 169
Metadata
- Authors
- Geusebroek, J.-M., van den Boomgaard, R., Smeulders, A. W. M., & Geerts, H.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2001
- Volume
- 23
- Issue
- 12
- Pages
- 1338-1350
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2296249689
- Cited by
- 553
- References
- 1
- FWCI
- 10.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- Geusebroek, J.-M., van den Boomgaard, R., Smeulders, A. W. M., & Geerts, H. (2001). Color invariance. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.977559
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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.
