Abstract
There are presented formal properties of morphological spectra as a novel tool for analysis of textures. It is described a multi-level structure of the system of morphological spectra and the method of calculation of spectral components. Formal properties of morphological spectra: symmetries, ability to describe parallel shifts and rotations of analyzed images are presented. Several comments concerning practical aspects of using morphological spectra to analysis of textures are also given.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhu Y.M., Gao Y., Goutte R., Amiel M. (1992). “Textural Boundary Detection Using Local Spatial Frequency Analysis”. Proc. 11th IAPR International Conference on Pattern Recognition, Hague, vol. III. IEEE Computer Society Press, Los Alamitos; 53–56.
J.L Kulikowski., D. Wierzbicka “A Method of Microvascular Systems Analysis Based on Statistical Texture Parameters Evaluation”. Biocybernetics and Biomedical Eng., vol. 23. No 3, 2003, pp. 21–37.
J.L. Kulikowski, M. Przytulska, D. Wierzbicka. “Recognition of Textures Based on Analysis of Multilevel Morphological Spectra”. IFMBE Proceedings, Vol. 14. World Congress on Medical Physics and Biomedical Engineering, Seoul, 2006, pp. 2164–2167.
J.F Haddon., J.F. Boyce (1992). “Texture Segmentation and Region Classification by Orthogonal Decomposition of Cooccurence Matrices”. Proc. 11th IAPR International Conference on Pattern Recognition, Hague, vol. I. IEEE Computer Society Press, Los Alamitos: 692–695.
T. Ojala, M. Pietikajnen. “Unsupervised Texture Segmentation Using Feature Distributions, Texture Analysis Using Pairwise Interaction Maps”, Image Analysis and Processing, 9th International Conference, ICIAP’97, Florence, Proc. vol. I. (A. Del Bimbo ed.), 1997, pp. 311–318.
G. Loum, J. Lemoine et al. “An Application of Wavelet Transform to Texture Analysis”. Proc. of the 9th Scandinavian Conference on Image Analysis, vol. 1, Uppsala, 1995, pp. 583–590.
Y. Xiaohan, J. Yla-Jaaski. “Unsupervised Texture Segmentation Based On the Modified Markov Random Field Model”. Proc. 11th IAPR International Conference on Pattern Recognition, Hague, vol. III. IEEE Computer Society Press, Los Alamitos, 1992, pp. 88–91.
T.G Smith., G.D. Lange. “Biological Cellular Morphometry-Fractal Dimensions, Lacunarity and Multifractals”. Fractals in Biology and Medicine, vol. II (Losa G.A., Merlini D. et al. Eds.). Birkhauser, Basel, 1998, pp. 30–49.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kulikowski, J.L., Przytulska, M., Wierzbicka, D. (2007). Morphological Spectra as Tools for Texture Analysis. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75175-5_64
Download citation
DOI: https://doi.org/10.1007/978-3-540-75175-5_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75174-8
Online ISBN: 978-3-540-75175-5
eBook Packages: EngineeringEngineering (R0)