Abstract
Color constancy is a problem related to how to make captured image color closer to biological vision, which is important for a lot of vision application including image stitching, visual tracking etc. Color constancy consists of light source color estimation and image white balance processing. Although, color constancy is ill-posed problem, there still are much study on it. In this paper, we try to solve this problem from illuminant invariant pixel estimation angle. LBP (Local Binary Pattern)-based statistical method is adopted as key tool for light source color estimation. Finally, experimental results demonstrate our algorithm advantage.
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References
Barnard, K., Martin, L., Coath, A., Funt, B.: A comparison of computational color constancy algorithms. ii. experiments with image data. IEEE Trans. Image Process. 11(9), 985–996 (2002)
Gao, S., Yang, K., Li, C., Li, Y.: A color constancy model with double-opponency mechanisms. In: IEEE International Conference on Computer Vision, pp. 929–936. IEEE (2013)
Gao, S.-B., Yang, K.-F., Li, C.-Y., Li, Y.-J.: Color constancy using double-opponency. IEEE Trans. Pattern Anal. Mach. Intell. 37(10), 1973–1985 (2015)
Gijsenij, A., Gevers, T., Van De Weijer, J.: Computational color constancy: survey and experiments. IEEE Trans. Image Process. 20(9), 2475–2489 (2011)
Gijsenij, A., Gevers, T., Van DeWeijer, J.: Improving color constancy by photometric edge weighting. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 918–929 (2012)
Bianco, S., Schettini, R.: Color constancy using faces. In: CVPR (2012)
Gijsenij, A., Gevers, T.: Color constancy using natural image statistics and scene semantics. In: TPAMI (2011)
Gehler, P., Rother, C., Blake, A., Minka, T., Sharp, T.: Bayesian color constancy revisited. In: CVPR (2008)
Taskar, B., Chatalbashev, V., Koller, D., Guestrin, C.: Learning structured prediction models: a large margin approach. In: ICML (2005)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Tan, R.T., Nishino, K., Ikeuchi, K.: Color constancy through inverse-intensity chromaticity space. JOSA A 21(3), 321–334 (2004). IICS
Buchsbaum, G.: A spatial processor model for object colour perception. J. Franklin Inst. 310(1), 1–26 (1980). GW
Land, E.H., McCann, J.J., et al.: Lightness and retinex theory. J. Opt. Soc. Am. 61(1), 1–11 (1971). WP
Van De Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Trans. Image Process. 16(9), 2207–2214 (2007). GE
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Xia, Y., Yao, C. (2017). Color Constancy Using Illuminant-Invariant LBP Features. In: Yang, X., Zhai, G. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2016. Communications in Computer and Information Science, vol 685. Springer, Singapore. https://doi.org/10.1007/978-981-10-4211-9_1
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DOI: https://doi.org/10.1007/978-981-10-4211-9_1
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