A template matching approach based on the discrepancy norm for defect detection on regularly textured surfaces

Bouchot, Jean-Luc; Stübl, Gernot; Moser, Bernhard

doi:10.1117/12.889865

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…which shows that condition (6) implies formula (5). Formulas ( 7) and ( 8) reveal that the bounds 0 and 1 in inequality ( 9) are assumed, showing the necessity of condition (6).…”

Section: The Unit Ball Of the Discrepancy Norm As Convex Polytopementioning

confidence: 87%

“…Such problems encounter as autocorrelation in signal processing, see [5]. In computer vision such problems are particularly encountered in stereo matching as point correspondence problem, see, e.g., [32] and [26], in template matching, e.g., for the purpose of print inspection, see, e.g., [7] and [23], in superpixel matching [16] or in defect detection in textured images, see [6,25,35]. In these cases, for high-frequency patterns, the discrepancy norm leads to cost functions with less local extrema and a more distinctive region of convergence in the neighborhood of the global minimum compared to commonly used (dis-)similarity measures.…”

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confidence: 99%

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Geometric Characterization of Weyl’s Discrepancy Norm in Terms of Its n-Dimensional Unit Balls

Moser

2012

Discrete Comput Geom

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Weyl's discrepancy measure induces a norm on R n which shows a monotonicity and a Lipschitz property when applied to differences of index-shifted sequences. It turns out that its n-dimensional unit ball is a zonotope that results from a multiple sheared projection from the (n + 1)-dimensional hypercube which can be interpreted as a discrete differentiation. This characterization reveals that this norm is the canonical metric between sequences of differences of values from the unit interval in the sense that the n-dimensional unit ball of the discrepancy norm equals the space of such sequences. MotivationIn the mathematical literature discrepancy theory is devoted to problems related to irregularities of distributions. In this context the term discrepancy refers to a measure that evaluates to which extent a given distribution deviates from total uniformity in measure-theoretic, combinatorial and geometric settings. This theory goes back to Weyl [39] and is still an active field of research, see, e.g., [3,12,19]. Applications can be found in the field of numerical integration, especially for Monte Carlo methods in high dimensions, see, e.g., [28,36,40], or in computational geometry, see, e.g., [1,9,21]. For applications to data storage problems on parallel disks, see [10,13] and for halftoning images, see [31].This paper is motivated by [24] which applies Weyl's discrepancy concept in order to derive an ordering-dependent norm for measuring the (dis-)similarity between patterns. In this context the focus lies on evaluating the auto-misalignment that measures

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“…which shows that condition (6) implies formula (5). Formulas ( 7) and ( 8) reveal that the bounds 0 and 1 in inequality ( 9) are assumed, showing the necessity of condition (6).…”

Section: The Unit Ball Of the Discrepancy Norm As Convex Polytopementioning

confidence: 87%

mentioning

confidence: 99%

Geometric Characterization of Weyl’s Discrepancy Norm in Terms of Its n-Dimensional Unit Balls

Moser

2012

Discrete Comput Geom

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“…The defect detection system based on template matching can usually get the difference between the template image and the image to be tested [9]. Then, the system makes a judgment whether there are defects in the image to be tested [10]. This method has many advantages, such as high efficiency and low error.…”

Section: Defect Detection Systemmentioning

confidence: 99%

Industrial Internet of Things for Mobile Phone Shell Intelligent Detection in Smart Cities

Zheng

Bao

Zhou

2021

Wireless Communications and Mobile Computing

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Industrial Internet of Things is the core field of smart city. And intelligent detection is an important application field of industrial Internet of Things. Demand of the industrial is particularly urgent. In particular, the defect detection of mobile phone shells (MPS) has always been a common problem for famous mobile phone companies. A compression-free defect detection method (CFDDM) for MPS based on machine vision is proposed in this paper. Firstly, affine transformation is utilized to solve the angle deviation of MPS in different images. Then, edge detection, binarization, and open operation are combined to highlight the edge region based on the results of angle adjustment. It is convenient for region of interest (ROI) extraction and clipping. Finally, the method of gray histogram contrasting is utilized for defect detection according to the results of ROI clipping. And the detection results are obtained. In this paper, MPS data set is utilized for many tests. The results show that the proposed method can effectively detect whether there are defects in MPS data set without image compression. The recognition accuracy is 100%. The recognition time of a single image is about 4.56 s, which is better than other defect detection methods.

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“…Xien Cheng et al [5] presented a technique for ceramic bowls they utilized the bowl's curved surface unfolding model derived from helicoid unfolding method. Jean-Luc Bouchot et al [6] analyzed the problem of finding a defect in regularly textured images with an approach based on a template matching principle.…”

Section: Introductionmentioning

confidence: 99%

An Automated Tiles Defect Detection

Mohan¹,

Kumar²

2015

IJCA

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It presents an automatic defect identification system for detecting crack of titles from captured digital images based on defect classification and segmentation. Image classification will be used for automated visual inspection to classify defect and protects from quality one. It will be performed through textures analysis and probabilistic neural network. The textures are extracted using wavelet filters with cooccurrence features. The defect detection process involves the preprocessing, segmentation and morphological filtering to make processing system more flexible with accuracy.

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A template matching approach based on the discrepancy norm for defect detection on regularly textured surfaces

Cited by 8 publications

References 35 publications

Geometric Characterization of Weyl’s Discrepancy Norm in Terms of Its n-Dimensional Unit Balls

Geometric Characterization of Weyl’s Discrepancy Norm in Terms of Its n-Dimensional Unit Balls

Industrial Internet of Things for Mobile Phone Shell Intelligent Detection in Smart Cities

An Automated Tiles Defect Detection

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