Random Texture Defect Detection Using 1-D Hidden Markov Models Based on Local Binary Patterns

Hadizadeh, Hadi; Shokouhi, Shahriar B.

doi:10.1093/ietisy/e91-d.7.1937

Cited by 9 publications

(4 citation statements)

References 15 publications

(5 reference statements)

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“…In order to improve the accuracy of scratch detection, it was necessary to extract the location relationship between regions, the gradient direction of regions, and the local gray distribution around regions [24]. These features will be used to further determine whether the segmentation area is scratched or not.…”

Section: Scratch Area Growth Based On Multiple Featuresmentioning

confidence: 99%

Image-Based Scratch Detection by Fuzzy Clustering and Morphological Features

Tan

Fei

et al. 2020

Applied Sciences

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Detection of scratch defects on randomly textured surfaces remains challenging due to their unnoticeable visual features. In this paper, an algorithm for piezoelectric ceramic plate surface scratch defects based on the combination of fuzzy c-means clustering and morphological features is proposed. Foreground membership of each gray value is calculated firstly on a reference set of training images by fuzzy c-means clustering and the interpolation method, then an enhanced image is obtained by multiplying the foreground membership function and gray image. The location relationship between regions and the gradient direction of regions is extracted from the binary image of the enhanced image. Based on the morphological features, isolated non-scratched defects are filtered out and the intermittent scratches are merged. Experiments show that the algorithm can be used to detect scratch defects on the surface of a piezoelectric ceramics plate with randomly textured surfaces.

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Section: Scratch Area Growth Based On Multiple Featuresmentioning

confidence: 99%

Image-Based Scratch Detection by Fuzzy Clustering and Morphological Features

Tan

Fei

et al. 2020

Applied Sciences

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“…However, methods in the field of detection of thin structures are numerous (Xie, 2008), for instance: in medical images (Chaudhuri et al, 1989), in satellite images (Geman and Jedynak, 1996), and, also in any kind of specific applications: crack detection in ceramic (Elbehiery et al, 2005;Hadizadeh and Baradaran Shokouhi, 2010), paintings (Abas and Martinez, 2003), or concrete Sinha, 2005, 2006). However, methods in the field of detection of thin structures are numerous (Xie, 2008), for instance: in medical images (Chaudhuri et al, 1989), in satellite images (Geman and Jedynak, 1996), and, also in any kind of specific applications: crack detection in ceramic (Elbehiery et al, 2005;Hadizadeh and Baradaran Shokouhi, 2010), paintings (Abas and Martinez, 2003), or concrete Sinha, 2005, 2006).…”

Section: Detection Of Road Cracksmentioning

confidence: 99%

Automatic Road Defect Detection by Textural Pattern Recognition Based on AdaBoost

Cord¹,

Chambon²

2011

Computer aided Civil Eng

163

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The state of roads is continuously degrading due to meteorological conditions, ground movements, and traffic, leading to the formation of defects, such as grabbing, holes, and cracks. In this article, a method to automatically distinguish images of road surfaces with defects from road surfaces without defects is presented. This method, based on supervised learning, is generic and may be applied to all type of defects present in those images. They typically present strong textural information with patterns that show fluctuations at small scales and some uniformity at larger scales. The textural information is described by applying a large set of linear and nonlinear filters. To select the most pertinent ones for the current application, a supervised learning based on AdaBoost is performed. The whole process is tested both on a textural recognition task based on the VisTex image database and on road images collected by a dedicated road imaging system. A comparison with a recent cracks detection algorithm from Oliveira and Correia demonstrates the proposed method's efficiency

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“…An application to surface inspection based on CHMMs is presented by Pernkopf [2]. A defect localization in texture using HMMs was presented by Hadizadeh and Shokouhi [15]. They utilize the HMM as a texture unit descriptor and predict the pixel values of the texture.…”

Section: Related Workmentioning

confidence: 99%

HMM-Based Defect Localization in Wire Ropes – A New Approach to Unusual Subsequence Recognition

Platzer

Nägele

Wehking

et al. 2009

Lecture Notes in Computer Science

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Abstract. Automatic visual inspection has become an important application of pattern recognition, as it supports the human in this demanding and often dangerous work. Nevertheless, often missing abnormal or defective samples prohibit a supervised learning of defect models. For this reason, techniques known as one-class classification and novelty-or unusual event detection have arisen in the past years. This paper presents a new strategy to employ Hidden Markov models for defect localization in wire ropes. It is shown, that the Viterbi scores can be used as indicator for unusual subsequences. This prevents a partition of the signal into sufficient small signal windows at cost of the temporal context. Our results outperform recent time-invariant one-class classification approaches and depict a great advance for an automatic visual inspection of wire ropes.

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Random Texture Defect Detection Using 1-D Hidden Markov Models Based on Local Binary Patterns

Cited by 9 publications

References 15 publications

Image-Based Scratch Detection by Fuzzy Clustering and Morphological Features

Image-Based Scratch Detection by Fuzzy Clustering and Morphological Features

Automatic Road Defect Detection by Textural Pattern Recognition Based on AdaBoost

HMM-Based Defect Localization in Wire Ropes – A New Approach to Unusual Subsequence Recognition

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