Real-time product quality control system using optimized Gabor filter bank

Öztürk, Şaban; Akdemïr, Bayram

doi:10.1007/s00170-018-1585-x

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…Implementing a standard procedure for multiple crack identification on multi-temporal image sequences is challenging because binarization and following segmentation is dependent on algorithms' parameters that vary based on specific study site characteristics at the time of image acquisition. Recently, the deployment of machine learning (ML) methods have shown potential to implement automated and real-time systems for varied applications [23][24][25]. The automated detection of crack patterns with ML algorithms has recently been investigated in different scenarios [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Monitoring of Cracks: Effectiveness Analysis of an Open-Source Machine Learning-Assisted Procedure

et al. 2022

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The proper inspection of a cracks pattern over time is a critical diagnosis step to provide a thorough knowledge of the health state of a structure. When monitoring cracks propagating on a planar surface, adopting a single-image-based approach is a more convenient (costly and logistically) solution compared to subjective operators-based solutions. Machine learning (ML)- based monitoring solutions offer the advantage of automation in crack detection; however, complex and time-consuming training must be carried out. This study presents a simple and automated ML-based crack monitoring approach implemented in open sources software that only requires a single image for training. The effectiveness of the approach is assessed conducting work in controlled and real case study sites. For both sites, the generated outputs are significant in terms of accuracy (~1 mm), repeatability (sub-mm) and precision (sub-pixel). The presented results highlight that the successful detection of cracks is achievable with only a straightforward ML-based training procedure conducted on only a single image of the multi-temporal sequence. Furthermore, the use of an innovative camera kit allowed exploiting automated acquisition and transmission fundamental for Internet of Things (IoTs) for structural health monitoring and to reduce user-based operations and increase safety.

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Section: Introductionmentioning

confidence: 99%

Image-Based Monitoring of Cracks: Effectiveness Analysis of an Open-Source Machine Learning-Assisted Procedure

et al. 2022

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“…One aspect of research into Gabor filtering investigates methods to increase real time operation. In Reference 21, the authors address this issue by implementing a genetic algorithm to iteratively optimize the Gabor parameters to reduce the size of the filter bank for accurate deformation measurements in glass production. However, orientation measurement sensitivity is limited to the sampling interval Δ of the input by: Δ/2.…”

Section: Introductionmentioning

confidence: 99%

Polynomial edge reconstruction sensitivity, subpixel Sobel gradient kernel analysis

Wells

See

2020

Engineering Reports

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In digital image processing the accuracy and precision of edge detectors within noisy temporal environments can be imperative. In this article, a report on the polynomial reconstruction of 22.5 • Sobel kernels using additional points through imaging system noise are analyzed. In comparison to the accuracy of first-order interval kernel masks, the first-order 45 • kernel approximation using 5-points provides consistent stability in accuracy and precision across pixel and subpixel gradients. For a 22.5 • sampling interval, a 3-point polynomial evaluation minimizes the error of the subpixel orientation of the gradient. These characteristics are derived from the retention of kernel symmetry using noninteger coefficients. Critical to gradient precision is the distortion of the evaluated points due to system noise or their value away from second-order symmetry. For a refined measurement estimate, sensitivity up to 10 −5 is demonstrated.

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Detection of abnormalities in wireless capsule endoscopy based on extreme learning machine

Ellahyani

Jaafari

Charfi

et al. 2020

SIViP

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Real-time product quality control system using optimized Gabor filter bank

Cited by 8 publications

References 40 publications

Image-Based Monitoring of Cracks: Effectiveness Analysis of an Open-Source Machine Learning-Assisted Procedure

Image-Based Monitoring of Cracks: Effectiveness Analysis of an Open-Source Machine Learning-Assisted Procedure

Polynomial edge reconstruction sensitivity, subpixel Sobel gradient kernel analysis

Detection of abnormalities in wireless capsule endoscopy based on extreme learning machine

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