Applying Image Processing to the Textile Grading of Fleece Based on Pilling Assessment

Huang, Meiling; Fu, Chien-Chang

doi:10.3390/fib6040073

Cited by 17 publications

(13 citation statements)

References 10 publications

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“…In order to avoid uneven training data and testing data at each grade when randomly sampling images, we randomly chose 80% of the images of each grade of fabric pilling in the database as training samples and the remaining 20% as testing samples. Several researchers, such as Huang and Fu [15] and Lee and Lin [16], also adopted this method to obtain training and testing samples. As for the ratio of training samples to testing samples, researchers can determine this according to the total image database obtained.…”

Section: Resultsmentioning

confidence: 99%

“…The overall average accuracy rates using the proposed DPCANN with neural network classifier and SVM classifier were 98.68% and 99.84%, respectively. The study by Huang and Fu [15] involved textile grading of fleece based on pilling assessment which was performed using two image processing methods and two machine learning methods. For the image processing methods, the first method involved using the discrete Fourier transform combined with Gaussian filtering, and the second method involved using the Daubechies wavelet.…”

Section: Resultsmentioning

confidence: 99%

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Using Deep Principal Components Analysis-Based Neural Networks for Fabric Pilling Classification

2019

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A manufacturer’s fabric first undergoes an abrasion test and manual visual inspection to grade the fabric prior to shipment to ensure that there are no defects present. Manual visual classification consumes a considerable amount of human resources. Furthermore, extended use of the eyes during visual inspection often causes occupational injuries, resulting in a decrease in the efficiency of the entire operation. In order to overcome and avoid such situations, this study proposed the use of deep principal components analysis-based neural networks (DPCANNs) for fabric pilling identification. In the proposed DPCANN, the characteristics of the hairball were automatically captured using deep principal components analysis (DPCA), and the hairball class was identified using the neural network and the support vector machine (SVM). The experimental results showed that the proposed DPCANN has an average accuracy of 99.7% at the hairball level, which is in line with the needs of the industry. The results also confirmed that the proposed hairball classification method is superior to other methods.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Using Deep Principal Components Analysis-Based Neural Networks for Fabric Pilling Classification

2019

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“…Recently, Huang and Fu [23] reported textile grading of fleece based on pilling assessment performed using image processing and machine learning methods. Two image processing methods were used.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, the proposed method was compared with other methods [2][3][4][23][24][25]. The experiments were also performed 10 times.…”

Section: Resultsmentioning

confidence: 99%

“…The second row of Table 3 illustrates a comparison of results of various methods. In the study by Huang and Fu [23], when the Fourier-Gaussian method was used, the classification accuracies of the ANN and SVM were 96.6% and 95.3%, and the overall accuracies of the Daubechies wavelet were 96.3% and 90.9%, respectively. The results indicate that the proposed method exhibits superior average accuracy in fabric pilling grade detection to other methods.…”

Section: Resultsmentioning

confidence: 99%

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Integrated Computer Vision and Type-2 Fuzzy CMAC Model for Classifying Pilling of Knitted Fabric

Lee

Lin

2018

Electronics

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Human visual inspection for classifying the pilling of knitted fabric not only consumes human resources but also causes occupational hazard because of long-term observation using human eyes. This reduces the efficiency of the entire operation. To overcome this, an integrated computer vision and type-2 fuzzy cerebellar model articulation controller (T2FCMAC) was devised for classifying the pilling of knitted fabric. First, the fast Fourier transform was used for image preprocessing to strengthen the characteristics of the pilling in the fabric image. The background and the pilling of knitted fabric were then segmented through binary and morphological operations. Characteristics of the pilling on the fabric were extracted by using image topography. A novel T2FCMAC based on the hybrid of group strategy and artificial bee colony (HGSABC) was proposed to evaluate the pilling grade of knitted fabric. The proposed T2FCMAC classifier embedded a type-2 fuzzy system within a traditional cerebellar model articulation controller (CMAC). The proposed HGSABC learning algorithm was used for adjusting the parameters of T2FCMAC classifiers and preventing the fall into a local optimum. A group search strategy was used to obtain balanced search capabilities and improve the performance of the artificial bee colony algorithm. The experimental results of the fixed and different illuminations indicated that the proposed method exhibited a superior average accuracy (97.3% and 94.6%, respectively) to other methods.

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Determination of Numerical Papillae Distribution Affecting the Taste Sensitivity on the Tongue with Image Processing Techniques

Çetinkol

Üncü

2020

Artificial Intelligence and Applied Mathematics in Engineering Problems

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Applying Image Processing to the Textile Grading of Fleece Based on Pilling Assessment

Cited by 17 publications

References 10 publications

Using Deep Principal Components Analysis-Based Neural Networks for Fabric Pilling Classification

Using Deep Principal Components Analysis-Based Neural Networks for Fabric Pilling Classification

Integrated Computer Vision and Type-2 Fuzzy CMAC Model for Classifying Pilling of Knitted Fabric

Determination of Numerical Papillae Distribution Affecting the Taste Sensitivity on the Tongue with Image Processing Techniques

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