Abstract
Most of the original image feature detectors are not able to cope with large photometric variations, and their extensions that should improve detection eventually increase the computational cost and introduce more noise to the system. Here we extend the original SURF algorithm increasing its invariance to illumination changes. Our approach uses the local space average color descriptor as working space to detect invariant features. A theoretical analysis demonstrates the impact of distinct photometric variations on the response of blob-like features detected with the SURF algorithm. Experimental results demonstrate the effectiveness of the approach in several illumination conditions including the presence of two or more distinct light sources, variations in color, in offset and scale.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Ancuti, C., Bekaert, P.: Sift-cch: Increasing the sift distinctness by color co-occurrence histograms. In: International Symposium on Image and Signal Processing and Analysis, Istanbul, Turkey, September 27-29, pp. 130–135 (2007)
Fan, P., Men, A.D., Chen, M.Y., Yang, B.: Color-surf: A surf descriptor with local kernel color histograms. In: IEEE International Conference on Network Infrastructure and Digital Content, Beijing, China, November 6-8, pp. 726–730 (2009)
Abdel-Hakim, A.E., Farag, A.A.: Csift: A sift descriptor with color invariant characteristics. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition - CVPR, June 17-22, vol. 2, pp. 1978–1983. IEEE Computer Society, New York (2006)
Burghouts, G.J., Geusebroek, J.M.: Performance evaluation of local colour invariants. Computer Vision and Image Understanding 113, 48–62 (2009)
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)
Buluswar, S.D., Draper, B.A.: Color recognition in outdoor images. In: International Conference on Computer Vision, Bombay, India, January 04-07, pp. 171–177 (1998)
Finlayson, G.D., Hordley, S.D., Xu, R.: Convex programming colour constancy with a diagonal-offset model. In: IEEE International Conference on Image Processing - ICIP, Genova, Italy, September 11-14, vol. 3, pp. III–948–III–951 (2005)
van de Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Transactions on Image Processing 16(9), 2207–2214 (2007)
Buchsbaum, G.: A spatial processor model for object color-perception. Journal of the Franklin Institute-Engineering and Applied Mathematics 310(1), 1–26 (1980)
Ebner, M.: How does the brain arrive at a color constant descriptor? In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds.) BVAI 2007. LNCS, vol. 4729, pp. 84–93. Springer, Heidelberg (2007)
Zeki, S., Marini, L.: Three cortical stages of colour processing in the human brain. Brain 121, 1669–1685 (1998)
Gijsenij, A., Gevers, T., van de Weijer, J.: Generalized gamut mapping using image derivative structures for color constancy. International Journal of Computer Vision 86(2-3), 127–139 (2010)
Barnard, K., Cardei, V., Funt, B.: A comparison of computational color constancy algorithms - part i: Methodology and experiments with synthesized data. IEEE Transactions on Image Processing 11(9), 972–984 (2002)
Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 37(2), 151–172 (2000)
Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The amsterdam library of object images. International Journal of Computer Vision 61(1), 103–112 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Petry, M.R., Moreira, A.P., Reisinst, L.P. (2013). Increasing Illumination Invariance of SURF Feature Detector through Color Constancy. In: Correia, L., Reis, L.P., Cascalho, J. (eds) Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science(), vol 8154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40669-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-40669-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40668-3
Online ISBN: 978-3-642-40669-0
eBook Packages: Computer ScienceComputer Science (R0)