Automatic texture defect detection using Gaussian mixture entropy modeling

Susan, Seba; Sharma, Monika

doi:10.1016/j.neucom.2017.02.021

Cited by 75 publications

(31 citation statements)

References 21 publications

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“…There are some latest reported model-based defect detection methods. Susan et al [96] proposed Gaussian mixture entropy model for defect detection, which is specialized in identifying miscellaneous defects such as holes and stains. Based on low-rank representation, Yan et al [97] utilized smooth-sparse decomposition (SSD) model for anomaly detection in images, Huangpeng et al [98] proposed a novel weighted low-rank reconstruction model for automatic visual defect detection, and Zhou et al [99] presented a double low-rank and sparse decomposition (DLRSD) model to obtain the defective region of steel sheet surface.…”

Section: ) Other Latest Reported Model-basedmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

304

111

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Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot-and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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Section: ) Other Latest Reported Model-basedmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

304

111

View full text Add to dashboard Cite

show abstract

“…Statistical modeling is one of the most popular texture analysis methods. In this approach, the pixel values are assumed to be random variables drawn from a specific distribution, then the distribution's parameters are estimated and can be used as features in different tasks such as image segmentation [24][25][26] or denoising [27]. Various distributions have been used for texture modeling and segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Various distributions have been used for texture modeling and segmentation. Gaussian distribution is one of the most commonly used models for texture analysis [24,28,29]. Rayleigh, Weibull [30,31], and Wishart [25] distributions have been also used for texture feature extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

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Skewed alpha-stable distribution for natural texture modeling and segmentation in contourlet domain

Sarafrazi

Yazdi

2019

J Image Video Proc.

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Texture modeling is a very useful tool in image analysis. This model can be used in texture segmentation, denoising or texture synthesis. In this work, alpha-stable distribution has been proposed to model and segment textured images in contourlet domain. Contourlet transform's ability to extract texture features in different scales and directions combined with alpha-stable distribution's modeling capabilities prove to be an effective method for texture feature extraction. Kolmogorov-Smirnov distance has been used to evaluate how well the proposed distribution fits to the image in the contourlet domain. The performance of the proposed features on image segmentation has been also compared with that of features extracted using different texture analysis methods in the presence of noise. Experimental results have demonstrated the superior performance of the proposed features and their robust performance in the presence of noise.

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“…Currently, automated defect detection methods based on machine vision have drawn much attention. Gaussian mixture entropy modeling [3] and wavelet transform [4] were used to detect defect in simple plain and twill fabric images via transformation and reconstruction processes. However, most of these methods designed for the simplest plain and twill fabrics, which cannot be effectively applied on complicated patterns fabric, such as the dot-patterned fabric, star-patterned fabrics and statistical-texture fabrics.…”

Section: Introductionmentioning

confidence: 99%

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

Zhang

et al. 2019

Applied Sciences

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In this paper, we present a robust and reliable framework based on L0 gradient minimization (LGM) and the fuzzy c-means (FCM) method to detect various fabric defects with diverse textures. In our framework, the L0 gradient minimization is applied to process the fabric images to eliminate the influence of background texture and preserve sharpened significant edges on fabric defects. Then, the processed fabric images are clustered by using the fuzzy c-means. Through continuous iterative calculation, the clustering centers of fabric defects and non-defects are updated to realize the defect regions segmentation. We evaluate the proposed method on various samples, which include plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric and statistical-texture fabric with different defect types and shapes. Experimental results demonstrate that the proposed method has a good detection performance compared with other state-of-the-art methods in terms of both subjective and objective tests. In addition, the proposed method is applicable to industrial machine vision detection with limited computational resources.

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Automatic texture defect detection using Gaussian mixture entropy modeling

Cited by 75 publications

References 21 publications

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Skewed alpha-stable distribution for natural texture modeling and segmentation in contourlet domain

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

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