Browsing by Author "Hassan, Mounir"
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- ItemImage Processing Based Method Evaluating Fabric Structure Characteristics(Inst Chemical Fibres, 2012) Shady, Ebraheem; Qashqary, Khadijah; Hassan, Mounir; Militký, JiříA digital image processing approach was developed to evaluate fabric structure characteristics and to recognise the weave pattern utilising a Wiener filter. Images of six different groups were obtained and used for analysis. The groups included three different fabric structures with two different constructions for each. The approach developed decomposed the fabric image into two images, each of which included either warp or weft yarns. Yarn boundaries were outlined to evaluate the fabric surface characteristics and further used to identify the areas of interlaces to detect the fabric structure. The results showed success in evaluating the surface fabric characteristics and detecting the fabric structure for types of fabrics having the same colors of warp and weft yarns. The approach was also able to obtain a more accurate evaluation for yarn spacing and the rational fabric cover factor compared to the analytical techniques used to estimate these characteristics.
- ItemIntegrated Computer Vision and Soft Computing System for Classifying the Pilling Resistance of Knitted Fabrics(Inst Chemical Fibres, 2014) Eldessouki, Mohamed; Bukhari, Hanan A.; Hassan, Mounir; Qashqari, KhadijahFabric pilling is one of the important properties that affect fabric appearance. The testing of fabric pilling using the standard methods available, however, depends on subjective sample evaluation. Objective fabric pilling evaluation using image processing techniques comprises four main stages that include binarisation, segmentation, quantisation, and classification. Literature on the topic focuses only on one or more of these stages while there is a growing need for an integrated system that combines the most effective techniques of each stage and introduces them in a way that does not depend on the subjective evaluation of human operators. This work tries to tackle this problem and creates an integrated system for classifying the pilling resistance of knitted fabrics. The system introduced a new method for generating an image library based on photographs of the EMPA Standards to allow the training and testing of a soft-computing classifier. The method suggested was tested using knitted samples of different structures and colours and the results show their high robustness performance. The quantitative pilling classification produced from the system suggested shows high agreement with the subjective operatorsevaluation with a Spearman's correlation coefficient of +0.85.