Abstract
Color texture algorithms have been under investigation for quite a few years now. However, the results of these algorithms are still under considerable influence of the illumination conditions under which the images were captured. It is strongly desireable to reduce the influence of illumination as much as possible to obtain stable and satisfying classification results even under difficult imaging conditions, as they can occur e.g. in medical applications like endoscopy. In this paper we present the analysis of a well-known texture analysis algorithm, namely the sum- and difference-histogram features, with respect to illumination changes. Based on this analysis, we propose a novel set of features factoring out the illumination influence from the majority of the original features. We conclude our paper with a quantitative, experimental evaluation on artificial and real image samples.
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References
Barnard, K.: Modeling scene illumination colour for computer vision and image reproduction: A survery of computational approaches. Technical report, Simon Fraser University, Vancouver, B.C., Canada (1998)
de Wouwer, G.V., Scheunders, P., Livens, S., Dyck, D.V.: Wavelet correlation signatures for color texture characerization. Pat. Rec. 32(3), 443–451 (1999)
Drimbarean, A., Whelan, P.: Experiments in colour texture analysis. Pat. Rec. Letters 22, 1161–1167 (2001)
Finlayson, G., Chatterjee, S., Funt, B.: Color angular indexing. In: ECCV (2), pp. 16–27 (1996)
Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cyb. SMC-3(6), 610–621 (1973)
Healey, G., Slater, D.: Computing illumination-invariant descriptors of spatially filtered color image regions. IEEE Trans. Image Process. 6(7), 1002–1013 (1997)
Jain, A., Healey, G.: A Multiscale Representation Including Opponent Color Features for Texture Recognition. IEEE Trans. Image Process. 7(1), 124–128 (1998)
Funt, J.H.B., Drew, M.: Separating a color signal into illumination and surface reflectance components: Theory and applications. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 966–977 (1990)
Lakmann, R.: Statistische Modellierung von Farbtexturen. PhD thesis, Universität Koblenz-Landau, Koblenz (1998)
Münzenmayer, C., Volk, H., Küblbeck, C., Spinnler, K., Wittenberg, T.: Multispectral Texture Analysis using Interplane Sum- and Difference-Histograms. In: Gool, L.V. (ed.) Pattern Recognition - Proceedings of the 24th DAGM Symposium Zurich, Switzerland, September 2002, pp. 25–31. Springer, Berlin (2002)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Palm, C.: Integrative Auswertung von Farbe und Textur. PhD thesis, RWTH Aachen, Osnabrück (2003)
Paschos, G.: Perceptually Uniform Color Spaces for Color Texture Analysis: An Empirical Evaluation. IEEE Trans. Image Process. 10(6), 932–937 (2001)
Randen, T., Husoy, J.H.: Filtering for texture classification: A comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 291–310 (1999)
Tan, T., Kittler, J.: Colour texture analysis using colour histogram. IEE Proc.-Vis. Image Signal Process. 141, 260–266 (1994)
Unser, M.: Sum and difference histograms for texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(1), 118–125 (1986)
Vision, M.M.L., Group, M.: Vistex vision texture database (1995), http://www-white.media.mit.edu/vismod/imagery/visiontexture
Vora, P., Farrell, J., Tietz, J., Brainard, D.: Linear models for digital cameras. In: Proc. of the 1997 IS&T 50th Annual Conference, Cambridge, MA, pp. 377–382 (1997)
Wandell, B.: The synthesis and analysis of color images. IEEE Trans. Pattern Anal. Mach. Intell. 9(1), 2–13 (1987)
Wanderley, J.F.C., Fisher, M.H.: Multiscale color invariants based on the human visual system. IEEE Trans. Image Process. 10(11), 1630–1638 (2001)
Wang, L., Healey, G.: Using zernike moments for the illumination and geometry invariant classification of multispectral texture. IEEE Trans. Image Process. 7(2), 196–203 (1998)
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Münzenmayer, C., Wilharm, S., Hornegger, J., Wittenberg, T. (2005). Illumination Invariant Color Texture Analysis Based on Sum- and Difference-Histograms. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_3
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DOI: https://doi.org/10.1007/11550518_3
Publisher Name: Springer, Berlin, Heidelberg
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