Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion
Resumo
Texture is one of the primary visual features used to computationally describe the patterns found in nature. Existing computational methods, however, do not successfully discriminate the complexity of texture patterns. Such methods disregard the possibility of describing images by benefiting from the complex systems properties that are characteristic to textures. To do so, we created approaches based on the Bouligand-Minkowski fractal dimension, swarm-system Artificial Crawlers, and non-linear diffusion of Perona-Malik, techniques that led to methodologies with efficacy and efficiency comparable to the state-of-the-art. The results achieved in the four methodologies described in this work demonstrated the validity and the potential of our hypothesis in tasks of pattern recognition. The contributions of our methodologies shall support advances in materials engineering, computer vision, and agriculture.
Referências
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C. S. Tsang, H. Y. Ngan, and G. K. Pang, “Fabric inspection based on the elo rating method,” Pattern Recognition, vol. 51, no. 3, pp. 378–394, 2016.
N. Zaglam, P. Jouvet, O. Flechelles, G. Emeriaud, and F. Cheriet, “Computer-aided diagnosis system for the acute respiratory distress syndrome from chest radiographs,” Computers in Biology and Medicine, vol. 52, no. 0, pp. 41–48, 2014.
R. Mehta, J. Yuan, and K. Egiazarian, “Face recognition using scale-adaptive directional and textural features,” Pattern Recognition, vol. 47, no. 5, pp. 1846–1858, 2014.
Y. Chen and E. Dougherty, “Gray-scale morphological granulometric texture classification,” Optical Engineering, vol. 33, no. 8, pp. 2713–2722, 1994.
R. M. Haralick, “Statistical and structural approaches to texture,” Proceedings of the IEEE, vol. 67, no. 5, pp. 786–804, 1979.
D. Chetverikov, “Texture analysis using feature based pairwise interaction maps,” Pattern Recognition, vol. 32, pp. 487–502, March 1999.
T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, pp. 971–987, July 2002.
X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” Image Processing, IEEE Transactions on, vol. 19, pp. 1635–1650, June 2010.
G. R. Cross and A. K. Jain, “Markov random field texture models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 5, pp. 25–39, 1983.
C. Tricot, Curves and fractal dimension. Springer-Verlag, 1995.
R. Azencott, J.-P. Wang, and L. Younes, “Texture classification using windowed fourier filters,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, pp. 148–153, February 1997.
D. Gabor, “Theory of communication,” Journal of Institute of Electronic Engineering, vol. 93, pp. 429–457, November 1946.
S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 7, pp. 710–732, 1992.
D. Zhang and Y. Q. Chen, “Artificial life: a new approach to texture classification,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 19, no. 3, pp. 355–374, 2005.
B. B. Machado, W. N. Goncalves, and O. M. Bruno, “Artificial crawler model for texture analysis on silk fibroin scaffolds,” Computational Science and Discovery, vol. 0, no. 7, p. 015004, 2014.
W. N. Goncalves, B. B. Machado, and O. M. Bruno, “Texture descriptor combining fractal dimension and artificial crawlers,” Physica A: Statistical Mechanics and its Applications, vol. 395, pp. 358–370, 2014.
B. B. Machado, W. N. Goncalves, M. do Santos, and J. F. R. Jr., “Multiscale fractal description using non-liner diffusion of perona-malik for texture analysis,” Pattern Recognition Letters, 2016.
B. B. Machado, W. N. Goncalves, and O. M. Bruno, “Enhancing the texture attribute with partial differential equations: a case of study with gabor filters,” in Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems, ACIVS’11, pp. 337–348, Springer-Verlag, 2011.
B. B. Machado, J. Orue, M. dos Santos, D. Sarath, G. Goncalves, W. N. Goncalves, H. Pistori, R. R. Mauro, and J. F. R. Jr., “Bioleaf: a professional mobile application to measure foliar damage caused by insect herbivory,” Computer Electronics and Agriculture, vol. 129, no. 3, pp. 44–55, 2016.
B. B. Machado, L. Scabini, M. do Santos, W. N. Goncalves, R. Moraes, and J. F. R. Jr., “A complex network approach for nanoparticle agglomeration analysis in nanoscale images,” Journal of Nanoparticle Research, vol. 19, no. 2, pp. 65–73, 2017.
Publicado
02/07/2017
Como Citar
MACHADO, Bruno Brandoli; RODRIGUES JUNIOR, Jose Fernando.
Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 30. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 2409-2414.
ISSN 2763-8820.
DOI: https://doi.org/10.5753/ctd.2017.3457.
