Automatic Extraction Method for Texture Periodicity Based on Improved Normalized Distance Matching Function
JIANG Sheng1,2, TANG Guo-An1, TAO Yang1,2
1.Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 2100462. 2.Provincial Fundamental Geomatics Center of Jiangsu, Nanjing 210013
Abstract Based on improved normalized distance matching function (INDMF), an automatic extraction method for regular and near-regular structural texture periodicity is proposed. Firstly, the dissimilarity of gray level co-occurrence matrices is calculated as the texture characteristic, and the INDMF edge is removed. Thus, the values between different peak intervals are more stable. Secondly, an adaptive and anti-noise peak searching approach is adopted to find initial periodic sequence and extract texture periodicity. Next, with the consideration of the characteristics of artificial and natural texture, the final periodicity is calculated by sequence mode. The results of extraction experiments on Brodatz and PSU datasets show the effectiveness and the efficiency of the proposed method. Moreover, the proposed method is more stable and accurate than the method of forward difference of accumulative DMF for impulsive salt and pepper noisy images and projective deformed images.
[1] Julesz B. Textons, the Elements of Texture Perception, and Their Interactions. Nature, 1981, 290(5802): 91-97 [2] Haralick R M. Statistical and Structural Approaches to Texture. Proceedings of the IEEE, 1979, 67(5): 786-804 [3] Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and Machine Vision. London, UK: Chapman & Hall Computing, 1993 [4] Soliman S S, Srinath M D. Continuous and Discrete Signals and Systems. Upper Saddle River, USA: Prentice Hall, 1990 [5] Starovoitov V V, Jeong S Y, Park R H. Investigation of Texture Periodicity Extraction. Proceedings of the SPIE, 1995, 2501: 870-881 [6] Asha V, Nagabhushan P, Bhajantri N U. Automatic Extraction of Texture-Periodicity Using Superposition of Distance Matching Functions and Their Forward Differences. Pattern Recognition Letters, 2012, 33(5): 629-640 [7] Lin H C, Wang L L, Yang S N. Extracting Periodicity of a Regular Texture Based on Autocorrelation Functions. Pattern Recognition Letters, 1997, 18(5): 433-443 [8] Liu Y X, Collins R T, Tsin Y H. A Computational Model for Periodic Pattern Perception Based on Frieze and Wallpaper Groups. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(3): 354-371 [9] Grigorescu S E, Petkov N. Texture Analysis Using Renyi's Genera-lized Entropies // Proc of the International Conference on Image Processing. Barcelona, Spain, 2003, I: 241-244 [10] Oh G, Lee S, Shin S Y. Fast Determination of Textural Periodicity Using Distance Matching Function. Pattern Recognition Letters, 1999, 20(2): 191-197 [11] Gonzalez R C, Woods R E, Eddins S L. Digital Image Processing Using MATLAB. Upper Saddle River, USA: Pearson Prentice Hall, 2003 [12] Lizarraga-Morales R A, Sanchez-Yanez R E, Ayala-Ramirez V. Fast Texel Size Estimation in Visual Texture Using Homogeneity Cues. Pattern Recognition Letters, 2013, 34(4): 414-422 [13] Chetverikov D. Pattern Regularity as a Visual Key. Image and Vision Computing, 2000, 18(12): 975-985
2014, Vol. 27 
