A self-organizing feature map for automated visual inspection of textile products

Tolba, A. S.; Abu-Rezeq, A. N.

doi:10.1016/s0166-3615(96)00076-0

Cited by 22 publications

(11 citation statements)

References 11 publications

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“…While textile inspection and inspection of printed products share some characteristics, the main difference is that textiles generally contain repeated patterns that can be characterized and statistically analyzed. In addition, defects in textiles fall into well-defined categories 7 that make it less difficult to develop defect-specific detection algorithms. A recent survey of fabric defect detection looked only at uniform textured materials (nonprinted textures) and focused on the difficulty of and myriad approaches to finding differences even when there are not random or patterned textures.…”

Section: Prior Workmentioning

confidence: 99%

Automatic visual inspection and defect detection on variable data prints

Vans

Schein

Staelin

et al. 2011

J. Electron. Imaging

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We present a system for automatic, on-line visual inspection and print defect detection for variable data printing (VDP). This system can be used to automatically stop the printing process and alert the operator to problems. We lay out the components required for constructing a vision-based inspection system and show that our approach is novel for the high-speed detection of defects on variable data. When implemented in a high-speed digital printing press, the system allows a single skilled operator to monitor and maintain several presses, reducing the number of operators required to run a shop floor of presses as well as reduce wasted consumables when a defect goes undetected. © 2011 SPIE and IS&T.

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Section: Prior Workmentioning

confidence: 99%

Automatic visual inspection and defect detection on variable data prints

Vans

Schein

Staelin

et al. 2011

J. Electron. Imaging

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“…Excellent reviews of recent advances in surface defect detection using texture analysis approaches have been provided in [2][3][4][5][6][7]. These reviews have discussed statistical-, structural-, filter-and modelbased approaches to AVI.…”

Section: Overview Of Multiscale-multidirectional Feature Extraction Amentioning

confidence: 99%

“…This emphasizes the importance of the multi-scale and multi-directional (MSMD) approaches in solving the defect detection problem. The most well-known MSMD approaches are those based on features extracted using the Wavelet Transform [4], the Gabor filters [4][5][6], and the DWHT [9].…”

Section: Overview Of Multiscale-multidirectional Feature Extraction Amentioning

confidence: 99%

A novel multiscale-multidirectional autocorrelation approach for defect detection in homogeneous flat surfaces

Tolba

2011

Machine Vision and Applications

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Defect detection in flat web surface products is a challenging task. Reliable vision-based systems for detection of defects require the suitable selection of a huge set of parameters which highly impact the performance of these systems such as image resolution/scale, size of the scanning window, feature extraction, direction of scanning, classifier type and parameters and system performance evaluation measures. This paper addresses these issues and introduces a novel multi-scale and multi-directional (MSMD) autocorrelation function (ACF)-based approach for reliable defect detection and localization in homogeneous web surfaces. The proposed approach has been experimentally tested on samples from the well-known TILDA textiles database and wallboards. Performance evaluation using the system Precision, Recall (Sensitivity), Specificity, Accuracy, Youden's index, F-measure and Matthews correlation coefficient has shown that the MSMD ACF approach outperforms the state-of-theart approaches like MSMD Log-Gabor filters. The MSMD ACFs approach results in better performance indicators for defect detection than the Log-Gabor based approach in addition to being about 2-6 times faster in defect detection.

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“…Automation of the quality assessment in this process can be performed by the use of an automated visual inspection (AVI) system. 1 AVI systems allow abnormal structures of a regular flat surface to be detected, analyzed and classified using machine vision approaches. When a defective product reaches the consumer, the company's reputation will be ruined.…”

mentioning

confidence: 99%

Homoscedasticity for defect detection in homogeneous flat surface products

Raafat

Tolba

2014

Textile Research Journal

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Homoscedasticity of homogeneous flat surface products is a necessary condition for a high quality product. The quality of homogeneous flat surface products, like paper sheets, steel slabs, textiles, and glasses, plays a crucial role in raising the profile of the manufacturing companies. This paper presents a new approach for defect detection using the Levene's test, which is used for testing the homogeneity of variances of samples drawn from the same population. It is assumed that the variances of samples taken from the same population are equal. Occurrence of defects results in a Levene's test measure that is higher than some critical value indicating that the null hypothesis of equal variances is rejected. Noise immunity of the proposed technique is ensured through pre-filtering the fabric image using the Wiener filter that is an edge preserving filter. The robustness to variations of the sliding window size and the structures of fabric is analyzed. The major advantages of the proposed technique are the low computational complexity and noise immunity while maintaining high accuracy.

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A self-organizing feature map for automated visual inspection of textile products

Cited by 22 publications

References 11 publications

Automatic visual inspection and defect detection on variable data prints

Automatic visual inspection and defect detection on variable data prints

A novel multiscale-multidirectional autocorrelation approach for defect detection in homogeneous flat surfaces

Homoscedasticity for defect detection in homogeneous flat surface products

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