Comparison of Image Preprocessing Techniques for Textile Texture Images
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Date
2010
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Abstract
Texture analysis is an important approach in textile quality control. The investment in automated unit is
more economical when reduction in labor cost and associated benefits are considered. Preprocessing in textile image
is a crucial initial step before texture analysis is performed. Many preprocessing methods are available in the
literature. Datasets are limited by laboratory constraints so that the need is for guidelines on quality and robustness,
to proceed experimentation while image are yet restricted. In this paper, the performance of four preprocessing
methods are compared namely Contrast adjustment. Intensity adjustment. Histogram equalization, Binarization and
Morphological operation. The performances of these methods are evaluated using Peak Signal to Noise Ratio
(PSNR) and Mean Square Error (MSE).