Applied vision about Green marketing for Trademark in the field of textile design and finishing

Amir, Wessam; Ahmed, Alaa

doi:10.21608/jdsaa.2021.30396.1052

Cited by 2 publications

(2 citation statements)

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“…In order to improve the accuracy of image recognition, a parallel fusion method based on contour and texture features is proposed in the literature to extract the structural and statistical features of clothing images. is method can recognize and describe clothing style images more comprehensively [29].…”

Section: Results and Analysismentioning

confidence: 99%

Application of Digital Image Processing Technology in Textile and Garment Field

Shih

2022

Security and Communication Networks

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In order to study the shortcomings of traditional manual inspection of clothing, improve the quality of clothing. A method of applying digital image processing technology in the field of textile and clothing is proposed. This method combines computer digital image technology to propose an image processing and measurement process technology based on the Labview platform. Through the effective combination of neural networks and support vector machines, methods such as deep learning can better realize the recognition and classification of clothing images. According to a random interview survey of 80 people, 78 people think that the quality of clothing is the most important, and 2 people think that beauty is the most important. The experimental results show that the digital book processing technology platform can further improve the accuracy of clothing detection.

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Section: Results and Analysismentioning

confidence: 99%

Application of Digital Image Processing Technology in Textile and Garment Field

Shih

2022

Security and Communication Networks

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“…After the Industrial Revolution and technological advances, few people need to know how to spin or weave, but they do need to know how to evaluate the durability of machine-made materials. [19][20] Fibre identification and textile research are increasingly crucial for textile producers and end consumers. [21] Experience and practise help some people identify fabric quality, but "learning by making mistakes" is time-consuming, stressful, and expensive.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploratory Data Analysis for Textile Defect Detection

Thivakaran,

Priyanka,

Antony

et al. 2023

IJRITCC

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The capacity to recognize anomalies in real-world visual data is essential for many computer vision uses. New approaches and ideas in unsupervised defective garments identification require data for training and evaluation. Understanding the constraints of the currently employed approach of human inspection is crucial for improving clothing quality. Uses for digital image processing in the textile sector are suggested. This method proposes a novel quantitative measuring strategy by fusing digital image processing with the Lab view platform. As this study progresses, it becomes clear that the FLDA yields the best results, with 95% accuracy, while the Hoeffiding Tree yields the lowest results, with 60% accuracy. When compared to other models, the FLDA's precision of 0.96 is the best you'll find, while the Hoeffiding Tree's is the lowest at 0.62. The FLDA provides the best result, with a recall value of 0.95, while the Hoeffiding Tree shows the lowest result, with a recall value of 0.60. The FLDA yields the best results (0.90 kappa value), whereas the Hoeffiding Tree yields the worst (0.20 kappa value).The FLDA exhibits the best results, with an F-Measure value of 0.95, while the Hoeffiding Tree displays the lowest results, with an F-Measure value of 0.58. The FLDA provides the best results, with an MCC value of 0.91, while the Hoeffiding Tree displays the worst results, with an MCC value of 0.22. The FLDA yields the best results (0.98 ROC value), whereas the Decision Table produces the worst results (0.69 ROC value). The best prediction accuracy is shown by the FLDA, at 0.98 of the PRC value, while the worst is shown by the Decision Table, at 0.67. The MAE is lowest (0.07) for the FLDA and highest (0.39) for the Hoeffiding Tree. The MAE deviation of the Bayes Net is 0.19. The best result is shown by the FLDA, with an RMSE of 0.22, while the largest RMSE deviation is found in the Hoeffiding Tree, at 0.62. The RMSEdeviation for Bayes Net is 0.41. The finest RAE is shown by the FLDA, at 13.39%, while the largest RAE deviation is 78.28% for the Hoeffiding Tree. The Bayes Net explains 38.74% of the variation in RAE. The best result is shown by the FLDA, with an RRSE of 44.36%; the largest RRSE variation is shown by the Hoeffiding Tree, with 123.99%. When compared to other models, the IBK's preparation time of 0 seconds is by far the shortest. While the Bayes Net completes its task in 0.03 seconds, FLDA can take up to 0.17 seconds. The FLDA model is found to have superior performance in this study.

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Applied vision about Green marketing for Trademark in the field of textile design and finishing

Cited by 2 publications

References 0 publications

Application of Digital Image Processing Technology in Textile and Garment Field

Application of Digital Image Processing Technology in Textile and Garment Field

Exploratory Data Analysis for Textile Defect Detection

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