Abstract
In this paper, the deterministic component of 2-D Wold decomposition is used to obtain texture descriptors in industrial plastic quality images, and hidden geometry of tree crown in remote sensing images. The texture image is decomposed into two texture images: a non-deterministic texture and a deterministic one. In order to obtain texture descriptors, a set of discriminant texture features is selected from the deterministic component. The texture descriptors have been used to distinguish among three kinds of plastic quality. The obtained texture descriptors are compared against texture descriptors obtained from the original image. With the objective to find hidden geometry of tree crown in remote sensing images, the deterministic component of the original image is analyzed. The observed geometry is compared against the modeled geometry in the literature of marked point processes.
Chapter PDF
References
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1966)
Cross, G., Jain, A.: Markov Random Field Texture Models. IEEE Transactions on Pattern Analysis and Machine Intellige 5, 25–39 (1983)
Francos, J.M., Meiri, A.Z., Porat, B.: A Unified Texture Model Based on a 2-D Wold-Like Decomposition. IEEE Transactions on Signal Processing 41, 2665–2678 (1993)
Li, F., Peng, J., Zheng, X.: Object-Based and Semantic Image Segmentation Using MRF. EURASIP Journal on Applied Signal Processing 6, 840–883 (2004)
Liu, F., Picard, R.W.: A Spectral 2-D Wold Decomposition Algorithm for Homogeneous Random Fields. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 3501–3504. IEEE Signal Processing Society, Piscataway New Jersey (1999)
Liu, F., Picard, R.W.: Periodicity, Directionality, and Randomness: Wold Features for Perceptual Pattern Recognition. Computer Vision & Image. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 2, pp. 184–189. IEEE Computer Society, Los Alamitos California (1994)
Perrin, G., Descombes, X., Zerubia, J.: 2D and 3D Vegetation Resource Parameters Assesment using Marked Point Processes. In: ICPR 2006, vol. 1, pp. 1–4. IEEE Computer Society, Washington DC USA (2006)
Sriram, R., Francos, J.M., Pearlman, W.A.: Texture Coding Using a Wold Decomposition Model. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition. IEEE Transactions on Image Processing, vol. 3, pp. 35–39. IEEE Signal Processing Society, Piscataway New Jersey (1996)
Ramananjarasoa, C., Alata, O., Najim, M.: 2-D Wold Decomposition: New Parameter Estimation Approach to Evanescent Field Spectral Supports. In: EUSIPCO, vol. 2, pp. 913–916 (2000)
Huang, Y., Chan, K.L.: Texture Decomposition by Harmonics Extraction From Higher Order Statistics. IEEE Transactions on Image Processing 13, 1–14 (2004)
Stitou, Y., Turcu, F., Najim, M., Redouane, L.: 3-D Texture Characterization Based on Wold Decomposition and Higher Order Statistics. In: ICASSP, vol. 2, pp. 165–168. IEEE Signal Processing Society, Piscataway New Jersey (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
López-Espinoza, E.D., Altamirano-Robles, L. (2007). Deterministic Component of 2-D Wold Decomposition for Geometry and Texture Descriptors Discovery. In: Rueda, L., Mery, D., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2007. Lecture Notes in Computer Science, vol 4756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76725-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-76725-1_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76724-4
Online ISBN: 978-3-540-76725-1
eBook Packages: Computer ScienceComputer Science (R0)