Fabric Pilling Hairiness Extraction From Depth Images Based on the Predicted Fabric Surface Plane

Zhi, Chao; Gao, Zhongyuan; Wang, Guanlin; Chen, Meng-Qi; Fan, Wei; Yu, Lingjie

doi:10.1109/access.2019.2962917

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…Early pilling is generally characterized by an increase in the amount of protruding fibers and by slight degradation of the fabric surface. This stage is characterized in the literature by the initial formation of pills 46 , 47 . The fabrics were graded by experts after machine testing.…”

Section: Discussionmentioning

confidence: 99%

Prediction of textile pilling resistance using optical coherence tomography

Gocławski

Sekulska‐Nalewajko

Korzeniewska

2022

Sci Rep

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This paper describes a new method of textile pilling prediction, based on multivariate analysis of the spatial layer above the surface. The original idea of the method is the acquisition of 3D fabric image using optical coherence tomography (OCT) with infrared light, which allows for the fabric fuzz visualization with high sensitivity. The pilling layer, reconstructed with the resolution of $$10\times 10\times 5.5 \; \upmu \mathrm {m}$$ 10 × 10 × 5.5 μ m , includes reliable textural information related to the amount of loose fibers and bunches appearing as a result of abrasion. Pilling intensity was assigned by supervised classification of the textural features using both linear (PLS-DA - partial least squares discriminant analysis, LDA - linear discriminant analysis) and non-linear (SVM - support vector machine) classifiers. The results demonstrated that the method is more suitable for fabrics after short-term abrasion, when the fuzz prevails over tangled fibers in the pilling layer. In that case, pilling grades were predicted with $$>98\%$$ > 98 % accuracy, sensitivity and specificity (for SVM model). The validation accuracy of the tested models after machine abrasion achieves lower values (up to $$90.4\%$$ 90.4 % for LDA model). With our method, we clearly showed that OCT can be used to quantitatively trace appearance changes of fabric samples due to test abrasion.

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Section: Discussionmentioning

confidence: 99%

Prediction of textile pilling resistance using optical coherence tomography

Gocławski

Sekulska‐Nalewajko

Korzeniewska

2022

Sci Rep

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“…The sharpness evaluation criterion proposed and verified effective in our previous study was utilized. 30…”

Section: Methodsmentioning

confidence: 99%

“…The sharpness evaluation criterion proposed and verified effective in our previous study was utilized. 30 As mentioned above, let the set {X n |n ¼ 1, 2, . .…”

Section: Fusion Rulementioning

confidence: 99%

“…The spatial domain method was designed based on a sharpness evaluation algorithm proposed in our previous study. 30…”

mentioning

confidence: 99%

“…The spatial domain method was designed based on a sharpness evaluation algorithm proposed in our previous study. 30 The rest of this paper is organized as follows: the second section briefly introduces the nonwoven fiber image acquisition process and our proposed algorithm; the third section reports our experimental results; finally, the fourth section gives our discussion.…”

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confidence: 99%

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Spectral- and spatial-based multi-focus image fusion method towards multi-layer nonwovens

Zhu

Sun

et al. 2023

Textile Research Journal

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In the microscopic imaging scenario where the object thickness exceeds the depth of field of the microscope, multi-focus image fusion (MFF) is an effective method to generate an all-in-focus image. However, for nonwoven fabric for which the captured image number is up to 100 or more, the existing methods often underperform in areas near the fiber edges, owing to image ghosting and noise accumulation caused by the platform moving. To address the above problem, this paper presents a method designed to fuse multi-layer micro-images based on the combination of spectral and spatial features of the images. Firstly, the spectral domain-based map is generated by decomposition and reconstruction of the high-frequency and low-frequency components of the images, aimed at obtaining the edge information. Simultaneously, the spatial domain-based fuse map is built through sharpness measurement, referring to visual perception. Finally, the two methods are combined via an optimized weight to obtain an all-in-focus fused image. Four groups of real-world data consisting of 100 multi-focus nonwoven images are utilized to verify the superiority of this method. The experimental results demonstrate that the proposed method can obtain satisfactory performance in terms of both human visual evaluation and objective evaluation compared with the image fusion framework based on the convolutional neural network, MFF, region-based image fusion algorithm and convolutional neural network state-of-the-art fusion methods.

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Applications of Machine Learning in Knowledge Management System: A Comprehensive Review

Simon

Jhanjhi

Wei

et al. 2022

J. Info. Know. Mgmt.

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As new generations of technology appear, legacy knowledge management solutions and applications become increasingly out of date, necessitating a paradigm shift. Machine learning presents an opportunity by foregoing rule-based knowledge intensive systems inundating the marketplace. An extensive review was made on the literature pertaining to machine learning which common machine learning algorithms were identified. This study has analysed more than 200 papers extracted from Scopus and IEEE databases. Searches ranged with the bulk of the articles from 2018 to 2021, while some articles ranged from 1959 to 2017. The research gap focusses on implementing machine learning algorithm to knowledge management systems, specifically knowledge management attributes. By investigating and reviewing each algorithm extensively, the usability of each algorithm is identified, with its advantages and disadvantages. From there onwards, these algorithms were mapped for what area of knowledge management it may be beneficial. Based on the findings, it is evidently seen how these algorithms are applicable in knowledge management and how it can enhance knowledge management system further. Based on the findings, the paper aims to bridge the gap between the literature in knowledge management and machine learning. A knowledge management–machine learning framework is conceived based on the review done on each algorithm earlier and to bridge the gap between the two literatures. The framework highlights how machine learning algorithm can play a part in different areas of knowledge management. From the framework, it provides practitioners how and where to implement machine learning in knowledge management.

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Fabric Pilling Hairiness Extraction From Depth Images Based on the Predicted Fabric Surface Plane

Cited by 3 publications

References 13 publications

Prediction of textile pilling resistance using optical coherence tomography

Prediction of textile pilling resistance using optical coherence tomography

Spectral- and spatial-based multi-focus image fusion method towards multi-layer nonwovens

Applications of Machine Learning in Knowledge Management System: A Comprehensive Review

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