Abstract
Image processing and pattern recognition are one of the most important area of research in computer science. Recently, several studies have been made and efficient approaches have been proposed to provide efficient solutions to many real and industrial problems. Texture analysis is a fundamental field of image processing because all surfaces of objects are textured in nature. Thus, we proposed a new texture analysis method. In this paper, we proposed a novel texture analysis approach based on a recent feature extraction method called neighbor based binary pattern (NBP). The NBP method extract the local micro texture and is robust against rotation, which is a key problem in image processing. The proposed system extract two-reference NBP histograms from the texture in order to calculate a model of the texture. Finally, several models have been constructed to be able to recognize textures even after rotation. Textured images from Brodatz album database were used in the evaluation. Experimental studies have illustrated that the proposed system obtain very encouraging results robust to rotation compared to classical method.
Chapter PDF
Similar content being viewed by others
References
Richards, W., Polit, A.: Texture matching. Kybernatic 16, 155–162 (1974)
Baohua, Y., Yuan, H., Jiuliang, C.: Combining Local Binary Pattern and Local Phase Quantization for Face Recognition. In: Biometrics and Security Technologies (ISBAST), pp. 51–53 (March 2012)
Jain, A.K., Ross, A., Prabhakar, S.: Fingerprint matching using minutiae and texture features. In: International Conference on Image Processing, vol. 3, pp. 282–285 (2001)
Hamouchene, I., Aouat, S., Lacheheb, H.: Texture Segmentation and Matching Using LBP Operator and GLCM Matrix. In: Chen, L., Kapoor, S., Bhatia, R. (eds.) Intelligent Systems for Science and Information. SCI, vol. 542, pp. 389–407. Springer, Heidelberg (2014)
Harlick, R.: Statistical and structural approaches to texture. Proc. of IEEE 67(5), 786–804 (1979)
Hamouchene, I., Aouat, S.: A New Texture Analysis Approach for Iris Recognition. In: AASRI Conference on Circuit and Signal Processing (CSP 2014), vol. 9, pp. 2–7 (2014)
Hamouchene, I., Aouat, S.: A cognitive approach for texture analysis using neighbors-based binary patterns. In: IEEE 13th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), August 18-20, pp. 94–99 (2014)
Ojala, T., Pietikäinen, M., Harwood, D.: A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition 29, 51–59 (1996)
Ojala, T., Pietikäinen, M.: Unsupervised Texture Segmentation Using Feature Distributions. Pattern Recognition 32, 477–486 (1999)
Guo, Z., Zhang, L., Zhang, D.: A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Transactions on Image Processing 19(6), 1657–1663 (2010)
Xueming, Q., Xian-Sheng, H., Ping, C., Liangjun, K.: An effective local binary patterns texture descriptor with pyramid representation. Pattern Recognition 44(10-11), 2502–2515 (2011)
Baohua, Y., Yuan, H., Jiuliang, C.: Combining Local Binary Pattern and Local Phase Quantization for Face Recognition. In: Biometrics and Security Technologies (ISBAST), pp. 51–53 (March 2012)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1966)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 IFIP International Federation for Information Processing
About this paper
Cite this paper
Hamouchene, I., Aouat, S. (2015). A New Rotation-Invariant Approach for Texture Analysis. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, E., Wrembel, R. (eds) Computer Science and Its Applications. CIIA 2015. IFIP Advances in Information and Communication Technology, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-19578-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-19578-0_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19577-3
Online ISBN: 978-3-319-19578-0
eBook Packages: Computer ScienceComputer Science (R0)