A New Voting Approach to Texture Defect Detection Based on Multiresolutional Decomposition
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Wavelets have provided the researchers with significant positive results, by entering the texture defect detection domain. The weak point of wavelets is that they are one-dimensional by nature so they are not efficient enough to describe and analyze two-dimensional functions. In this paper we present a new method to detect the defect of texture images by using curvelet transform. Simulation results of the proposed method on a set of standard texture images confirm its correctness. Comparing the obtained results indicates the ability of curvelet transform in describing discontinuity in two-dimensional functions compared to wavelet transform Keywords— Curvelet, Defect detection, Wavelet.
[1] Pengfei Shi,et al. An adaptive level-selecting wavelet transform for texture defect detection , 2007, Image Vis. Comput..
[2] Georg Lambert,et al. Wavelet methods for texture defect detection , 1997, Proceedings of International Conference on Image Processing.
[3] Mohamed-Jalal Fadili,et al. Multivariate statistical modeling of images with the curvelet transform , 2005, Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005..