, Dynamic texture analysis with diffusion in networks, Digital Signal Processing, vol.92, pp.109-126, 2019.

A. Mishchuk and F. R. Dmytro-mishkin, Working hard to know your neighbor's margins: Local descriptor learning loss, 2017.

R. Azencott, J. P. Wang, and L. Younes, Texture classification using windowed Fourier filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.2, pp.148-153, 1997.

A. R. Backes and O. M. Bruno, Plant leaf identification using color and multi-scale fractal dimension, International Conference on Image and Signal Processing, pp.463-470, 2010.

A. R. Backes, D. Casanova, and O. M. Bruno, Plant leaf identification based on volumetric fractal dimension, International Journal of Pattern Recognition and Artificial Intelligence, vol.23, issue.06, pp.1145-1160, 2009.

A. R. Backes, D. Casanova, and O. M. Bruno, Color texture analysis based on fractal descriptors, Pattern Recognition, vol.45, issue.5, pp.1984-1992, 2012.

A. R. Backes, D. Casanova, and O. M. Bruno, Texture analysis and classification: A complex network-based approach, Information Sciences, vol.219, pp.168-180, 2013.

P. Brodatz, Textures: A photographic album for artists and designers, 1966.

O. M. Bruno, R. De-oliveira-plotze, M. Falvo, and M. De-castro, Fractal dimension applied to plant identification, Information Sciences, vol.178, issue.12, pp.2722-2733, 2008.

T. Chalumeau, L. D. Costa, O. Laligant, F. Meriaudeau, E. Damiani et al., Texture discrimination using hierarchical complex networks, Signal Processing for Image Enhancement and Multimedia Processing, vol.31, pp.95-102, 2008.

D. C. Correa, J. H. Saito, and L. Costa, Musical genres: beating to the rhythms of different drums, New Journal of Physics, vol.12, issue.5, p.53030, 2010.

L. D. Costa, F. A. Rodrigues, G. Travieso, and P. R. Villas-boas, Characterization of complex networks: A survey of measurements, Advances in Physics, vol.56, issue.1, pp.167-242, 2007.

R. Fabbri, L. D. Costa, J. C. Torelli, and O. M. Bruno, 2d euclidean distance transform algorithms: A comparative survey, ACM Computing Surveys (CSUR), vol.40, issue.1, 2008.

J. B. Florindo and O. M. Bruno, Fractal descriptors based on Fourier spectrum applied to texture analysis, Physica A: statistical Mechanics and its Applications, vol.391, issue.20, pp.4909-4922, 2012.

J. B. Florindo, D. Casanova, and O. M. Bruno, Fractal measures of complex networks applied to texture analysis, Journal of Physics: Conference Series, vol.410, p.12091, 2013.

K. Fukunaga, Introduction to Statistical Pattern Recognition, 1990.

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.

Y. Guo, G. Zhao, and M. Pietikäinen, Texture classification using a linear configuration model based descriptor, BMVC, pp.1-10, 2011.

Z. Guo, L. Zhang, and D. Zhang, A completed modeling of local binary pattern operator for texture classification, IEEE Transactions on Image Processing, vol.19, issue.6, pp.1657-1663, 2010.

Z. Guo, L. Zhang, and D. Zhang, Rotation invariant texture classification using lbp variance (lbpv) with global matching, Pattern recognition, vol.43, issue.3, pp.706-719, 2010.

R. M. Haralick, Statistical and structural approaches to texture, Proceedings of the IEEE, vol.67, issue.5, pp.786-804, 1979.

R. M. Haralick, K. Shanmugam, and I. H. Dinstein, Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions, issue.6, pp.610-621, 1973.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

A. K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Conference Proceedings., IEEE International Conference on, pp.14-19, 1990.

J. Kannala and E. Rahtu, Bsif: Binarized statistical image features, Pattern Recognition (ICPR), 2012 21st International Conference on, pp.1363-1366, 2012.

J. K. Kim and H. W. Park, Statistical textural features for detection of microcalcifications in digitized mammograms, IEEE transactions on medical imaging, vol.18, issue.3, pp.231-238, 1999.

W. K. Lam and C. K. Li, Rotated texture classification by improved iterative morphological decomposition. IEE Proceedings-Vision, Image and Signal Processing, vol.144, issue.3, pp.171-179, 1997.

R. Maani, S. Kalra, and Y. H. Yang, Noise robust rotation invariant features for texture classification, Pattern Recognition, vol.46, issue.8, pp.2103-2116, 2013.

E. N. Malamas, E. G. Petrakis, M. Zervakis, L. Petit, and J. D. Legat, A survey on industrial vision systems, applications and tools, Image and vision computing, vol.21, issue.2, pp.171-188, 2003.

B. S. Manjunath and W. Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions, vol.18, issue.8, pp.837-842, 1996.

T. Ojala, T. Mäenpää, M. Pietikäinen, J. Viertola, J. Kyllönen et al., Outex: New framework for empirical evaluation of texture analysis algorithms, 2002.

T. Ojala, M. Pietikainen, and T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on pattern analysis and machine intelligence, vol.24, issue.7, pp.971-987, 2002.

V. Ojansivu and J. Heikkilä, Blur insensitive texture classification using local phase quantization, International conference on image and signal processing, pp.236-243, 2008.

D. K. Panjwani and G. Healey, Markov random field models for unsupervised segmentation of textured color images, IEEE Transactions, vol.17, issue.10, pp.939-954, 1995.

R. Picard, C. Graczyk, S. Mann, J. Wachman, L. Picard et al., Vision texture database, 1995.

L. C. Ribas, J. J. Junior, L. F. Scabini, and O. M. Bruno, Fusion of complex networks and randomized neural networks for texture analysis, 2018.

L. C. Ribas, D. N. Gonçalves, J. P. Oruê, and W. N. Gonçalves, Fractal dimension of maximum response filters applied to texture analysis, Pattern Recognition Letters, vol.65, pp.116-123, 2015.

L. C. Ribas, M. B. Neiva, and O. M. Bruno, Distance transform network for shape analysis, Information Sciences, vol.470, pp.28-42, 2019.

S. Da, M. W. Oliveira, D. Casanova, J. B. Florindo, and O. M. Bruno, Enhancing fractal descriptors on images by combining boundary and interior of minkowski dilation, Physica A: Statistical Mechanics and its Applications, vol.416, pp.41-48, 2014.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.2818-2826, 2016.

E. De-ves, D. Acevedo, A. Ruedin, and X. Benavent, A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval, Pattern Recognition, vol.47, issue.9, pp.2925-2939, 2014.

H. R. Wenk, Preferred Orientation in Deformed Metal and Rocks: An Introduction to Modern Texture Analysis, 2013.

A. M. Zimer, E. C. Rios, P. D. Mendes, W. N. Gonçalves, O. M. Bruno et al., Investigation of aisi 1040 steel corrosion in h2s solution containing chloride ions by digital image processing coupled with electrochemical techniques, Corrosion Science, vol.53, issue.10, pp.3193-3201, 2011.