Abstract
The median-shift, a new clustering algorithm, is proposed to automatically identify the palette of colored graphics, a pre-requisite for graphics vectorization. The median-shift is an iterative process which shifts each data point to the “median” point of its neighborhood defined thanks to a distance measure and a maximum radius, the only parameter of the method. The process is viewed as a graph transformation which converges to a set of clusters made of one or several connected vertices. As the palette identification depends on color perception, the clustering is performed in the L*a*b* feature space. As pixels located on edges are made of mixed colors not expected to be part of the palette, they are removed from the initial data set by an automatic pre-processing. Results are shown on scanned maps and on the Macbeth color chart and compared to well established methods.
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References
Diebel, J.R.: Bayesian Image Vectorization: The Probabilistic Inversion of Vector Image Rasterization. Phd thesis, Standford University (2008)
Hilaire, X.: RANVEC and the Arc Segmentation Contest: Second Evaluation. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 362–368. Springer, Heidelberg (2006)
Chen, Y., et al.: Extracting Contour Lines From Common-Conditioned Topograpic Maps. IEEE TGARS 44(4), 1048–1057 (2006)
Deseilligny, M.P.: Lecture automatique de cartes, Phd thesis, Université René Descartes, Paris, France (1994)
Robert, R.: Contribution à la lecture automatique de cartes, Phd thesis, Université de Rouen, Rouen, France (1997)
Braquelaire, J.-P., Brun, L.: Comparison and Optimization of Methods of Color Image Quantization. IEEE Trans. on Image Processing 6(7), 1048–1052 (1997)
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Trans. PAMI 22(1), 4–37 (2000)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Trans. PAMI 24(5), 603–619 (2002)
Tran, T.N., Wehrens, H.R.M.J., Buydens, L.M.C.: Clustering multispectral images: a tutorial. Chemometrics and Int. Lab. Syst. 77, 3–17 (2005)
Price, K.: Computer Vision Biography, 8.8.3 MRF Models for Segmentation, http://www.visionbib.com/bibliography/segment369.html (accessed 05-09)
Koschan, A., Abidi, M.: Detection and Classification of Edges in Color Images. Sig. Proc. Mag., Spec. Issue on Color Img. Proc. 22(1), 64–73 (2005)
Lacroix, V., Acheroy, M.: Feature-Extraction Using the Constrained Gradient. ISPRS J. of Photogram. and RS 53(2), 85–94 (1998)
Palus, H.: On color image quantization by the k-means algorithm. 10. In: Droege, D., Paulus, D. (eds.) Workshop Farbbildverarbeitung, Universität Koblenz-Landau, Tönning, Der Andere Verlag (2004)
Bouman, C.A.: Cluster: An unsupervised algorithm for modeling Gaussian mixtures (1997), http://www.ece.purdue.edu/~bouman
Ohta, N., Robertson, A.R.: Colorimetry Fundamentals and Applications. John Wiley & Sons, Ltd., Chichester (2005)
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Lacroix, V. (2010). Automatic Palette Identification of Colored Graphics. In: Ogier, JM., Liu, W., Lladós, J. (eds) Graphics Recognition. Achievements, Challenges, and Evolution. GREC 2009. Lecture Notes in Computer Science, vol 6020. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13728-0_6
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DOI: https://doi.org/10.1007/978-3-642-13728-0_6
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