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Introduction
Texture defect detection [1] can be defined as the process of determining the location and/or extend of a collection of pixels in a textured image with remarkable deviation in their intensity [2] values or spatial arrangement with respect to the background texture [3]. They are well rooted in the computer vision world and have been extensively applied to various tasks. A large number of statistical texture features have been proposed, ranging from first order statistics to higher order statistics. Amongst many, histogram statistics [4], co-occurrence matrices [5], auto correlation [6], and local binary patterns [7, 8] have been applied to texture analysis or classification [9, 10]. The finding the defects in that texture level make a quality of and turn over the organizations.
Related work
Xie and Mirmehdi [11] have presented an approach to detecting and localizing defects in random color textures which require only a few defect free samples for unsupervised training with superposition of various-size image. Osareh et al. [12] using fuzzy c-means clustering with genetic based algorithm was used to rank the features and identify the subset that gives the best classification results. Ngan and Pang [13] have considered regularity analysis for patterned texture material inspection. With detecting defects in 16 out of 17 wallpaper groups [14] in 2-D patterned texture. The motif-based method evolves from the concept that every wallpaper group was defined by a lattice. Ershad and Tajeripour [15] have proposed for detecting abnormalities in surface textures based on single dimensional Ershad and Tajeripour local binary patterns. Rebhi et al. [16] have proposed a defect detection algorithm based on local homogeneity and discrete cosine transform (DCT) to eliminate the texture elements in the digital image by isolating the defected area. Nilesh Bhaskarrao Bahadure had approached BWT and SVM to detect brain tumors. It’s well executed in terms of accuracy for clinical systems [17]. Long Chen, is well executed multiple kernel fuzzy. its lead to high dimensional feature space using kernel trick .
Proposed method
The identification of anomalies has surfaced as a daunting challenge in the domain of the computer vision. In the innovative technique, a novel approach is envisioned for the dissimilating of locating the deficiencies of the pattern texture appraisal. The innovative approach flows through four phases...