Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
In this paper a novel definition and understanding of colour correlogram has been proposed. The proposed colour correlogram generalizes the spatial graylevel dependency matrix (SGLDM) to the case of colour textures. This generalization is based on perceptual colour difference measure expressed in the language of the CIELab colour space components. Application of the colour difference instead of arbitrary colour indices or colour components themselves allows to avoid colour-shuffling palletization and introduction of multidimensional objects, respectively; the proposed perceptual colour correlogram is a single 2D matrix. At the same time, a simple relation of the proposed colour correlogram to the spatial graylevel dependency matrix for graylevel textures is retained. Based on this relation it will be shown that there exists a vector of statistical features built from the perceptual colour correlogram which can be used to describe textures in perceptual terms. These statistical features and their abovementioned perceptual interpretation generalize Haralick concepts derived for the SGLDM.
Rocznik
Tom
Strony
12--17
Opis fizyczny
Bibliogr. 14 poz., rys.
Twórcy
autor
- Laboratory of Autonomous Defense Systems, Industrial Research Institute for Automation and Measurements PIAP, Al. Jerozolimskie 202, 02-486 Warsaw, Poland, www: http://www.piap.pl
Bibliografia
- [1] Arvis V. et al., “Generalization of the cooccurrence matrix for colour images: application to colour texture classification”, Image Analysis & Stereology, vol. 23, 2004, 63–72. DOI: 10.5566/ias.v23.p63-72.
- [2] Berry J., Goutsias J., “A comparative study of matrix measures for maximum likelihood texture classification”, IEEE Transaction on Systems, Man and Cybernetics, vol. 21, no. 1, 1991, 252–261. DOI:10.1109/21.101156.
- [3] Bojar K., Nieniewski M., “Analysis of temporal variations of dynamic textures by means of the SGLDM with application to solar EIT images”, Machine Graphics & Vision, 2008, vol. 17, no. 3, 219–247.
- [4] Dixit A., Hedge N. P., “Image Texture Analysis Techniques – Survey”, 3rd International Conference on Advanced Computing & Communication Technologies, Rohtak, April 6–7, 2013.DOI 10.1109/ACCT.2013.49.
- [5] Haralick R., Shanmugam K., Dinstein I., “Textural features for image classification”, IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, 1973, 610–621. DOI: 10.1109/TSMC.1973.4309314.
- [6] Huang J. et al., “Spatial Colour Indexing and Applications”, International Journal of Computer Vision, vol. 35, no. 3, 1999, 245–268. DOI:10.1023/A:1008108327226.
- [7] Kiranyaz S., Birinci M., Gabbouj M., “Perceptual colour descriptor based on spatial distribution: A top-down approach”, Image and Vision Computing, vol. 28, 2010, 1309–1326. DOI:10.1016/j.imavis.2010.01.012.
- [8] Melgosa M., “Testing CIELAB-based colour difference formulas”, Colour Research and Application, vol. 25, 2000, no. 1, 49–55. DOI: 10.1002/(SICI)1520-6378(200002)25:1<49::AIDCOL7>3.0.CO;2-4.
- [9] Nanni L., Lumini A., Branham S., “Survey on LBP based texture descriptors for image classification”, Expert Systems with Applications, Vol. 39, 2013, 3634–3641. DOI: 10.1016/j.eswa.2011.09.054.
- [10] S. Shim, T. Choi, “Image indexing by modified colour cooccurrence matrix”. In: Acoustics, Speech, and Signal Processing. Proceedings, Hong Kong, April 6–10, 2003, vol. 3, III–577.DOI: 10.1109/ICASSP.2003.1199540.
- [11] Swain M., Ballard D., “Colour indexing”, International Journal of Computer Vision, vol. 7, no. 1, 1991, 11–32. DOI: 10.1007/BF00130487.
- [12] Raheja J., Ajay B., Chaudhary, “Real time fabric defect detection system on an A. embedded DSP platform”, Optik-International Journal for Light and Electron Optics, 2013, vol. 124, no. 21, 5280–5284.DOI: 10.1016/j.ijleo.2013.03.038.
- [13] Vadivel A., Sural S., Majumdar A., “An integrated colour and intensity cooccurrence matrix”, Pattern Recognition Letters, 2007, vol. 28, 974–983.DOI: 10.1016/j.patrec.2007.01.004.
- [14] http://vismod.media.mit.edu/pub/VisTex/VisTex.tar.gz, MIT Texture Database, accessed on 13 Dec. 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-11ec3f4d-320d-44c3-91c1-fa54c6410e2d