Rusen Meylani

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In this paper, the eight parameter two-dimensional adaptive lattice filter is used to detect defects in textures corresponding to raw textile fabrics. A novel histogram modification technique is also applied for pre-processing the grey level texture image. Moreover, with the proposed scheme, it is possible to detect defects using the defective image only.(More)
Quality control is one of the basic issues in textile industry. Texture analysis plays an important role in the automated visual inspection of texture images to detect their defects. For this purpose, model-based and feature-based methods are implemented and tested for textile images in a laboratory environment. The methods are compared in terms of their(More)
In this paper, the three-, the six-, and the eight-parameter two-dimensional gradient based adaptive lattice filters are compared in the context of defect detection in textures corresponding to textile fabrics. A novel histogram modification technique is also applied for preprocessing the gray level texture image. Moreover, with the proposed scheme, it is(More)
In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D.(More)
In this paper, a 2-D robust recursive least squares lattice algorithm is introduced and is applied to defect detection problem in textured images. The algorithm combines concepts of 1-D robust regression with the recursive least squares lattice algorithm. The philosophy of using different optimization functions that results in weighted least-squares(More)
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