Fourier descriptors for defect indication in a multiscale and multisensor measurement system

Burla, Avinash; Haist, Tobias; Lyda, Wolfram

doi:10.1117/1.3562319

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…The efficacy of this type of segmentation for the identification and extraction of individual topography features has been documented elsewhere [16]. Many other segmentation techniques are possible, and are the subject of active research [8][9][10][11][12]17]. This work explores texture-based segmentation.…”

Section: Measurement Science and Technologymentioning

confidence: 99%

“…Typical scenarios involve the localisation and verification of structured features, i.e. individual topographic formations explicitly designed and manufactured over the surface [5][6][7][8][9][10][11], and/or the identification and measurement of localised defects and other random singularities [12]. Algorithmic feature identification, an essential element of the characterisation process, is often heavily reliant on areal segmentation, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Texture-based segmentation with Gabor filters, wavelet and pyramid decompositions for extracting individual surface features from areal surface topography maps

Senin

Leach

Pini

et al. 2015

Meas. Sci. Technol.

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Areal topography segmentation plays a fundamental role in those surface metrology applications concerned with the characterisation of individual topography features. Typical scenarios include the dimensional inspection and verification of micro-structured surface features, and the identification and characterisation of localised defects and other random singularities. While morphological segmentation into hills or dales is the only partitioning operation currently endorsed by the ISO specification standards on surface texture metrology, many other approaches are possible, in particular adapted from the literature on digital image segmentation. In this work an original segmentation approach is introduced and discussed, where topography partitioning is driven by information collected through the application of texture characterisation transforms popular in digital image processing. Gabor filters, wavelets and pyramid decompositions are investigated and applied to a selected set of test cases. The behaviour, performance and limitations of the proposed approach are discussed from the viewpoint of the identification and extraction of individual surface topography features.

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Section: Measurement Science and Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Texture-based segmentation with Gabor filters, wavelet and pyramid decompositions for extracting individual surface features from areal surface topography maps

Senin

Leach

Pini

et al. 2015

Meas. Sci. Technol.

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“…Khoje and Bodhe [17] compared classifi cation performance of the Fourier transform based shape descriptors and its derivatives for fruit quality grading of mangoes. Burla et al [18] developed two modifi ed Fourier descriptors methods for fast detection of surface defects on microlens arrays, one kind of micro-optical elements. Zheng et al [19] introduced a revised Fourier descriptors based on multiscale centroid contour distance to recognize objects in remote sensing images.…”

Section: Literature Reviewmentioning

confidence: 99%

Effective image models for inspecting profile flaws of car mirrors with applications

Peter¹,

Lin²,

Lin³

2020

J Appl Eng Science

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Since car mirrors are standard accessories with cars, the demand for car mirrors is growing and manufacturers also pay more emphasis on the increase of product quality. Common appearance fl aws of car mirrors include: scratches, bubbles, pinholes causing surface fl aw type and burrs,damaged edges causing profi le fl aw type. Currently, the inspection tasks are conducted by human inspectors. Since the profi le fl aws will cause structural damages of car mirrors and reduce ability to withstand stress, the level of damage suffered even more than the surface fl aws. In addition, the angle diversity of capturing images makes it hard to implement automatic optical inspection. Therefore, this study develops an automated profi le fl aw detection system for car mirrors to replace visual inspection personnel. This study proposes a self-contrast defect detection method for the profi le fl aw inspection of car mirrors. It is not required to provide a standard fl awless sample in detection process and derive information compared with testing samples. The proposed method fi rst extracts the contour information of the testing image by Fourier descriptors. Then, after some middle and high-frequency coeffi cients were fi ltered out, an approximated contour image can be rebuilt from the Fourier domain for comparing with the testing image. Finally, the fl aw districts are easily separated by image subtraction. Experimental results demonstrate that the fl aw inspection rate reaches 85.05%, and the incorrect alert rate is smaller than 0.07%, and the correct classifi cation rate is up to 97.47%.

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“…Several researchers have been approaching the characterisation problem as a two-step process: firstly the tiles (or the notable topographic features within the tiles) are identified and extracted as separate geometric entities; then dedicated, custom parameters are devised aimed at capturing properties pertaining to the shape of the tiles, and/or their layout over the surface. Notable examples have been shown for hard disk drives [13], abrasives and optical depixelator surfaces [1] and microlens arrays [5], [16]. The same two-step, tile-centred approach has been adopted by the authors of this paper.…”

Section: Metrological Characterisation Of Tessellated Surfacesmentioning

confidence: 99%

“…on how the tiles are spread over the entire surface. This problem has been extensively investigated in the literature [1], [5], [16], [19] and, at the current state of this research, needs to be implemented as an add-on procedure, for example, by reconstructing the pattern lattice through topological connection of the tile centroids.…”

Section: Layout Information For Tessellated Surfacesmentioning

confidence: 99%

Shape descriptors and statistical classification on areal topography data for tile inspection in tessellated surfaces

2017

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Verification of conformance to design specifications in production, and identification of defects related to wear or other damage during maintenance, are key metrological aspects that must be addressed for micro-scale tessellated surfaces. A new algorithmic approach is presented that operates on topography datasets as obtained by areal topography instruments. The approach combines segmentation algorithms with a novel implementation of the angular radial transform, originally adopted by the MPEG-7 standard, to implement shape descriptors and associated similarity metrics. Applications to the inspection and verification of laser-manufactured micro-embossing topographies are illustrated. The topographies are first segmented to extract the individual tiles; the tiles are then encoded through shape descriptors. Principal component analysis and cluster analysis are used to investigate the behaviour of the angular radial transform coefficients. Finally, an algorithmic classifier based on supervised learning (k-nearest neighbours) is implemented and shown to be effective at identifying defects and at discriminating between defect types.

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Fourier descriptors for defect indication in a multiscale and multisensor measurement system

Cited by 10 publications

References 21 publications

Texture-based segmentation with Gabor filters, wavelet and pyramid decompositions for extracting individual surface features from areal surface topography maps

Texture-based segmentation with Gabor filters, wavelet and pyramid decompositions for extracting individual surface features from areal surface topography maps

Effective image models for inspecting profile flaws of car mirrors with applications

Shape descriptors and statistical classification on areal topography data for tile inspection in tessellated surfaces

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