The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Edge-Based Color Constancy
Open accessAbstract
Color constancy is the ability to measure colors of objects independent of the color of the light source. A well-known color constancy method is based on the gray-world assumption which assumes that the average reflectance of surfaces in the world is achromatic. In this paper, we propose a new hypothesis for color constancy namely the gray-edge hypothesis, which assumes that the average edge difference in a scene is achromatic. Based on this hypothesis, we propose an algorithm for color constancy. Contrary to existing color constancy algorithms, which are computed from the zero-order structure of images, our method is based on the derivative structure of images. Furthermore, we propose a framework which unifies a variety of known (gray-world, max-RGB, Minkowski norm) and the newly proposed gray-edge and higher order gray-edge algorithms. The quality of the various instantiations of the framework is tested and compared to the state-of-the-art color constancy methods on two large data sets of images recording objects under a large number of different light sources. The experiments show that the proposed color constancy algorithms obtain comparable results as the state-of-the-art color constancy methods with the merit of being computationally more efficient.
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Cites
Land, E. H., & McCann, J. J. (1971) · Journal of the Optical Society of America · doi:10.1364/josa.61.000001 · Cited by 4,095
The Retinex Theory of Color Vision
Land, E. H. (1977) · Scientific American · doi:10.1038/scientificamerican1277-108 · Cited by 2,981
A spatial processor model for object colour perception
Buchsbaum, G. (1980) · Journal of the Franklin Institute · doi:10.1016/0016-0032(80)90058-7 · Cited by 1,694
A novel algorithm for color constancy
Forsyth, D. A. (1990) · International Journal of Computer Vision · doi:10.1007/bf00056770 · Cited by 722
Land, E. H. (1986) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.83.10.3078 · Cited by 623
Shades of Gray and Colour Constancy
Finlayson, G. D., & Trezzi, E. (2004) · Color and Imaging Conference · doi:10.2352/cic.2004.12.1.art00008 · Cited by 614
Color by correlation: a simple, unifying framework for color constancy
Finlayson, G. D., Hordley, S. D., & HubeL, P. M. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969113 · Cited by 592
Funt, B., & Finlayson, G. D. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.391390 · Cited by 560
Brainard, D. H., & Freeman, W. T. (1997) · Journal of the Optical Society of America A · doi:10.1364/josaa.14.001393 · Cited by 521
Barnard, K., Cardei, V., & Funt, B. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.802531 · Cited by 440
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Tschandl, P., Rosendahl, C., & Kittler, H. (2018) · Scientific Data · doi:10.1038/sdata.2018.161 · Cited by 3,628
Color balance and fusion for underwater image enhancement
Ancuti, C. O., Ancuti, C., De Vleeschouwer, C., & Bekaert, P. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2759252 · Cited by 1,357
Bayesian retinex underwater image enhancement
Zhuang, P., Li, C., & Wu, J. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104171 · Cited by 434
Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case
Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A. D., & Ortiz-Barredo, A. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.04.013 · Cited by 426
Zhang, J., Cao, Y., Wang, Y., Wen, C., & Chen, C. W. (2018) · Proceedings of the 26th ACM international conference on Multimedia · doi:10.1145/3240508.3240653 · Cited by 40
Metadata
- Authors
- van de Weijer, J., Gevers, T., & Gijsenij, A.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2007-09
- Volume
- 16
- Issue
- 9
- Pages
- 2207-2214
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W1966855663
- Cited by
- 928
- References
- 16
- FWCI
- 13.65
- Type
- journal article
- Language
- en
- Open access
- green
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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.
