{"title":"A New Voting Approach to Texture Defect Detection Based on Multiresolutional Decomposition ","authors":"B. B. M. Moasheri, S. Azadinia","volume":49,"journal":"International Journal of Computer and Information Engineering","pagesStart":119,"pagesEnd":124,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9688","abstract":"Wavelets have provided the researchers with\r\nsignificant positive results, by entering the texture defect detection domain. The weak point of wavelets is that they are one-dimensional\r\nby nature so they are not efficient enough to describe and analyze two-dimensional functions. In this paper we present a new method to\r\ndetect the defect of texture images by using curvelet transform.\r\nSimulation results of the proposed method on a set of standard\r\ntexture images confirm its correctness. Comparing the obtained results indicates the ability of curvelet transform in describing\r\ndiscontinuity in two-dimensional functions compared to wavelet\r\ntransform","references":"[1] Y. Han, P.Shi, 2007, Image and Vision Computing 25, An adaptive level-selecting wavelet transform for texture defect detection, pp. 1239-1248.\r\n[2] A.Monadjemi, PHD thesis, October 2004, university of Bristol, Towards\r\nEfficient Texture Classification and Abnormality Detection\r\n[3] X X.Xie, M.Mirmehdi, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2007, TEXEMS: Texture Exemplars for Defect\r\nDetection on Random Textured Surfaces, vol. 29, no. 8, pp. 1454-1464.\r\n[4] J.L.Sobral, Springer 2005, Leather Inspection based on Wavelets.\r\n[5] H.Yan-fang, S.Peng-fei,2006, Zhejiang University Press, co-published\r\nwith Springer-Verlag GmbH, Mean shift texture surface detection based\r\non WT and COM feature image selection, Vol. 7, Np. 6, pp 969-975.\r\n[6] A. Latif-Amet, A. Ertu\u252c\u00bfzu\u252c\u00bfn, A. Ercil, 2000, Image and Vision Computing, An efficient method for texture defect detection: sub-band\r\ndomain co-occurrence matrices, Vol. 18, pp 543-553.\r\n[7] Y. X. Zhi, K. H. Pang and H. C. Yung, IEEE 2001, Fabric Defect Detection Using Adaptive Wavelet, pp. 3697-3700.\r\n[8] E.Kulak, October 2002, Sabanc1 University, Analysis of Textural Image\r\nFeatures for Content Based Retrieva.\r\n[9] G.Fan, and X.Xia, 2003, IEEE Trans, Wavelet-Based Texture Analysis\r\nand Synthesis Using Hidden Markov Models, pp. 106-120.\r\n[10] C.K.Mohan, M.Vijayaraghavan, J.Rengarajan, 2002, Dept. of IEEE, Real Time Texture Defect Detection using Sub-Band Domain Feature\r\nExtraction.\r\n[11] M.C.Lee, C.Pun, Image Analysis and Interpretation, 2000, Texture\r\nClassification Using Dominant Wavelet Packet Energy Features, pp. 301-304.\r\n[12] G.Lambert, F.Bock, IEEE Proceedings of the International Conference\r\non Image Processing, 1997 , Wavelet Methods for Texture Defect\r\nDetection,Vol. 3, p. 201.\r\n[13] S.Kim, H.Bae, S.Cheon, and K.Kim, Computational Science and Its\r\nApplications - ICCSA 2005, On-line Fabric-Defects Detection Based on\r\nWavelet Analysis, pp. 1075-1084.\r\n[14] D.A. Karras and B.G. Mertzios, Springer-Verlag Berlin Heidelberg\r\n2002, Improved Defect Detection in Manufacturing Using Novel\r\nMultidimensional Wavelet Feature Extraction Involving Vector\r\nQuantization and PCA Techniques.\r\n[15] C.kwak, J. A.Ventura and K.Tfang-sazi, Journal of Intelligent\r\nManufacturing, 2000, A neural network approach for defect\r\nidentification and classification on leather fabric, pp. 485-499.\r\n[16] E.J.Cand\u00e9s and L.Demanet; 2004, Curvelets and Wave Equations.\r\n[17] L.Dettori,and L.Semler; April 2007, A comparison of wavelet, ridgelet,\r\nand curvelet-based texture classification algorithms in computed\r\ntomography , vol. 37, no. 4 , pp. 486-498.\r\n[18] Boubchir,L. Fadili, J.M.; \"Multivariate statistical modeling of images\r\nwith the curvelet transform\", Proceedings of the 8th International\r\nSymposium, pp. 747- 750, vol. 2, 2005.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 49, 2011"}