@article{shim_pourdeyhimi_latifi_2010, title={Three-dimensional analysis of segmented pie bicomponent nonwovens}, volume={101}, ISSN={["1754-2340"]}, DOI={10.1080/00405000903357938}, abstractNote={Three‐dimensional structural analysis utilizing digital volumetric imaging is used to fully understand the splitting of bicomponent fibers by hydroentangling. It was found that lower fabric density measured by solid volume fraction, higher degree of splitting and a higher thickness fiber orientation direction was evident at the jet streak valley position. Splitting was found to be more dominant on the surface of the fabrics. Washing the fabric increased fiber splitting and also resulted in more uniform splitting, but did not result in any significant change in local fiber orientation, that is, the structure.}, number={9}, journal={JOURNAL OF THE TEXTILE INSTITUTE}, author={Shim, E. and Pourdeyhimi, B. and Latifi, M.}, year={2010}, pages={773–787} }
 @article{semnani_latifi_tehran_pourdeyhimi_merati_2006, title={Grading of yarn appearance using image analysis and an artificial intelligence technique}, volume={76}, ISSN={["0040-5175"]}, DOI={10.1177/0040517506056868}, abstractNote={ In this research, a new method is used for grading of yarn appearance based on yarn images of ASTM standard (section D 2255), by using an image processing technique and an artificial intelligence technique. In this method, grading of yarn appearance is based on computer vision and analyzing the images of standard pictorial boards of yarn. Therefore this method is very similar to human vision. The logic of the classification by ASTM is considered and then a new definition for classification of yarn appearance grade is presented. In this method of classification, the grading procedure is not dependent on yarn structure and raw materials. Thus it is possible to use this method for grading of any type of yarn based on apparent features. }, number={3}, journal={TEXTILE RESEARCH JOURNAL}, author={Semnani, D and Latifi, M and Tehran, MA and Pourdeyhimi, B and Merati, AA}, year={2006}, month={Mar}, pages={187–196} }
 @article{pourdeyhimi_semnani_latifi_tehran_merati_2005, title={Development of appearance grading method of cotton yarns for various types of yarns}, volume={9}, DOI={10.1108/rjta-09-04-2005-b009}, abstractNote={In this research, a new method is developed for grading various types of yarn for appearance using image analysis and an artificial neural network. The images of standard yarn boards were analyzed by image analysis and four different faults factors were defined and measured for each series of yarn counts. For each series of yarn counts, a neural network with one layer was trained by measured fault factors of standard boards. The trained neural networks were used for grading various types of yarns. The yarns were also graded by the conventional standard method. The results of grading various types of yarns by image analysis and conventional standard method are compared. We found a strong correlation between the results of grading by two methods. Whereas, in the image analysis method, the grading procedure is not dependent on yarn structure and raw materials, we concluded that it is possible to use this method for grading of any types of yarns based on their apparent features.}, number={4}, journal={Research Journal of Textile & Apparel}, author={POURDEYHIMI, BEHNAM and Semnani, D. and Latifi, M. and Tehran, M. A. and Merati, A. A.}, year={2005}, pages={886–893} }
 @article{pourdeyhimi_semnani_latifi_tehran_merati_2005, title={Effect of yarn appearance on apparent quality of weft knitted fabrics}, volume={96}, DOI={10.1533/joti.2005.0003}, abstractNote={Abstract This research has attempted to present a novel definition for apparent quality of weft knitted fabrics and their used yarns using the image analysis method and linear functions, which are calculated by neural networks. First, standard boards of yarn were analyzed using the image analysis method and artificial neural networks. Then, samples of plain, cross-miss and plain pique fabrics and their used yarns were tested for appearance. The results show that the correlation between apparent quality of knitted fabrics and their yarns is very strong. The ANOVA test confirmed that there is a strong influence of yarn type and fabric structure on fabric apparent quality. Although the yarn type has a strong effect on fabric appearance, the effect of fabric structure on its appearance is not remarkable. Other results show that the quality of the knitted fabric depends on the features of the raw materials and the effects of different knit elements.}, number={5}, journal={Journal of the Textile Institute}, author={POURDEYHIMI, BEHNAM and Semnani, D. and Latifi, M. and Tehran, M. A. and Merati, A. A.}, year={2005}, pages={295–301} }
 @article{latifi_kim_pourdeyhimi_2001, title={A note on pilling due to fabric to fabric abrasion}, volume={71}, number={7}, journal={Textile Research Journal}, author={Latifi, M. and Kim, H. S. and Pourdeyhimi, B.}, year={2001}, pages={640–644} }
 @article{kim_latifi_pourdeyhimi_2000, title={Characterizing fuzz in nonwoven fabrics}, volume={9}, number={1}, journal={International Nonwovens Journal}, author={Kim, H. S. and Latifi, M. and Pourdeyhimi, B.}, year={2000}, pages={18–22} }