Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection

Vermaak, H.J.; Nsengiyumva, Philibert; Luwes, Nicolaas

doi:10.1155/2016/9794723

Cited by 25 publications

(19 citation statements)

References 13 publications

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“…Particularly, the DTCWT satisfies the well-known condition of Hilbert Analytic, which is illustrated in top of Figure 1 (denoted in dashed box), indicating that the scaling complex function and the wavelet complex function collaborate to generate Hilbert pair. Equation 1shows its mathematical presentation as previously described [29,31,32].…”

Section: Dual Tree Complex Wavelet Transformmentioning

confidence: 99%

Assessment of Dual Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

Al-Naami

Fraihat

Al-Nabulsi

et al. 2021

Preprint

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Here we propose a novel de-noising method to improve the outcome of heart sound (HS)-based heart condition identification. We applied Dual Tree Complex Wavelet Transform (DTCWT) in collaboration with Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The method consisted of three steps. First, preprocess to eliminate 50 Hz noise. Second, application of DTCWT to de-noise and reconstruct time-domain HS signal. Third, evaluation of ANFIS on total 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was a 11% increase in SNR after DTCWT, representing a significant improvement in de-noising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc in comparison to other attempts on the same dataset. Therefore, DTCWT is a successful technique in de-noising information such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.

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Section: Dual Tree Complex Wavelet Transformmentioning

confidence: 99%

Assessment of Dual Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

Al-Naami

Fraihat

Al-Nabulsi

et al. 2021

Preprint

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show abstract

“…The authors from [13] also used the TILDA database. In their case, they applied Dual-Tree Complex Wavelet Transform (DTCWT) for defect detection in fabrics with Euclidean Distance Classifier in the classification step.…”

Section: Related Workmentioning

confidence: 99%

A Block-Processing Approach Using Texture Analysis for Fabric Defect Detection

Buschetto¹,

Roque²,

Casagrande³

et al. 2020

Anais Do XI Computer on the Beach - COTB '20

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The quality control is an essential step in fabric industries. Detectdefects in the early stages can reduce costs and increase the qualityof the products. Currently, this task is mainly done by humans,whose judgment can be affected by fatigue. Computer vision-basedtechniques can automatically detect defects, reducing the need forhuman intervention. In this context, this work proposes an imageblock-processing approach, where we compare the Segmentation-Based Fractal Texture Analysis, Gray Level Co-Occurrence Matrix,and Local Binary Pattern in the feature extraction step. Aimingto show the efficiency of this approach for the problem, these resultswere compared with the same algorithms without the blockprocessingapproach. A Support Vector Machine optimized by Grid-Search Algorithm was used to classify the fabrics. The databaseused, which is available online, is composed of 479 images fromsamples with defects and without it. The results show that thisblock processing approach can improve the classification results,achieving 100% in this work.

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“…The DWT is used quite intensively in the field of NDT for the noise suppression from the GW signals. 27–29 Generally, the DWT performs two functions: scaling with low pass filter (LPF) and wavelet function with high pass filter (HPF). The test signals are decomposed into the various elementary signals with the help of available mother wavelets (wavelet function).…”

Section: Overview Of Signal Processing Of Ultrasonic Signalsmentioning

confidence: 99%

Post-processing of ultrasonic signals for the analysis of defects in wind turbine blade using guided waves

Tiwari

Raišutis

2018

The Journal of Strain Analysis for Engineering Design

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In this work, the most promising ultrasonic signal processing methods-discrete wavelet transform, variational mode decomposition and Hilbert transform-are applied for the analysis of disbond-type defects in the segment of wind turbine blade. Two disbond-type artificial defects having diameters of 81 and 25 mm were located on the main spar of wind turbine blade. The low-frequency ultrasonic system developed by Ultrasound Research Institute of the Kaunas University of Technology was used for the experimental investigation of wind turbine blade using guided waves and only one side of the blade segment was accessed. Two contact type ultrasonic transducers separated by 50 mm distance and fixed on a movable mechanical panel were used as a transmitter-receiver pair during the experiment for the ultrasonic signals recording up to the scanning distance of 250 mm with the scanning step of 1 mm. Both types of defects were marginally detected from the conventional experimental B-scan and therefore appropriate signal processing techniques were used to improve the accuracy of the analysis of defects. The discrete wavelet transform was combined with the amplitude detection method for estimating the size and location of defects. Finally, the variational mode decomposition is combined with the Hilbert transform to compare the instantaneous frequencies and amplitudes of the defect-free and defective signals as well as for the measurement of time-delays between the defect-free and defective signals.

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Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection

Cited by 25 publications

References 13 publications

Assessment of Dual Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

Assessment of Dual Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

A Block-Processing Approach Using Texture Analysis for Fabric Defect Detection

Post-processing of ultrasonic signals for the analysis of defects in wind turbine blade using guided waves

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