A Novel Method for Optical Distortion Quantification

Gerton, Katherine M; Novar, Brenda J.; Brockmeier, William; Putnam, Christopher M

doi:10.1097/opx.0000000000001335

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Transmitted deformation is the image degeneration of a visible object incurred by a transpicuous material. In inspection studies of transmitted deformation in industrial parts, Gerton et al (2019) investigated deformation patterns of Ronchi grids mathematically for determining the effects of distortions in eyewear products. Youngquist et al (2015) presented an explanation of optical deformation and permitted the use of a phase-shifting interferometer for determining the distortion of a large optical window.…”

Section: Literature Reviewsmentioning

confidence: 99%

Vision based deformation inspection system for automotive glass using Hough circle detectors

Lin,

Lo,

Lin

2023

JART

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Production of automotive glass with the accurate shape and form is a challenge that fabricates the glass products with properties of adequate transparency and lack of imaging deformations and optical flaws. An auto windscreen with deformation flaws will likely distort the driver’s view of surrounding objects, leading to errors in visual judgment that may be dangerous to other road users. In the traditional method of examining vehicle glass in the manufacturing process, human inspectors perform the bulk of the work. This study proposes a frequency reconstruction method established on computer vision to automatically detect deformation flaws in automotive glass. To quantify the deformation level of an outwardly curving glass product, we exploit the digital imaging of a known standard pattern with base dots through a testing sample to capture a transmitted and reflected deformation image of that sample. Then, the proposed method applies the circular Hough transform voting scheme to find the peak points of the base dots in parameter space and reconstructs an image with the base dots of the captured image. The binary testing image subtracts the binary reconstructed image to obtain a binary difference image that displays the detected deformation areas. Experimental outcomes present that the proposed approach using dots pattern reaches a high 82.76% probability of exactly discriminating deformation flaws and a low 1.14% probability of wrongly investigating regular regions as deformation flaws on transmitted appearances of transpicuous glass.

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Section: Literature Reviewsmentioning

confidence: 99%

Vision based deformation inspection system for automotive glass using Hough circle detectors

Lin,

Lo,

Lin

2023

JART

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“…Chiu et al [20] constructed an optical system based on small drift control charts to identify deformation defects in curved car mirrors. Gerton et al [21] analytically investigated the deformation patterns of Ronchi meshes to assess the impact of deformation on eyewear products. Lin et al [22] employed the Hough transform voting scheme to inspect deformation defects for see-through glass products.…”

Section: Literature Reviewmentioning

confidence: 99%

Optical Imaging Deformation Inspection and Quality Level Determination of Multifocal Glasses

Lin

Lee

Lin

et al. 2023

Sensors

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Multifocal glasses are a new type of lens that can fit both nearsighted and farsighted vision on the same lens. This property allows the glass to have various curvatures in distinct regions within the glass during the grinding process. However, when the curvature varies irregularly, the glass is prone to optical deformation during imaging. Most of the previous studies on imaging deformation focus on the deformation correction of optical lenses. Consequently, this research uses an automatic deformation defect detection system for multifocal glasses to replace professional assessors. To quantify the grade of deformation of curved multifocal glasses, we first digitally imaged a pattern of concentric circles through a test glass to generate an imaged image of the glass. Second, we preprocess the image to enhance the clarity of the concentric circles’ appearance. A centroid-radius model is used to represent the form variation properties of every circle in the processed image. Third, the deviation of the centroid radius for detecting deformation defects is found by a slight deviation control scheme, and we gain a difference image indicating the detected deformed regions after comparing it with the norm pattern. Fourth, based on the deformation measure and occurrence location of multifocal glasses, we build fuzzy membership functions and inference regulations to quantify the deformation’s severity. Finally, a mixed model incorporating a network-based fuzzy inference and a genetic algorithm is applied to determine a quality grade for the deformation severity of detected defects. Testing outcomes show that the proposed methods attain a 94% accuracy rate of the quality levels for deformation severity, an 81% recall rate of deformation defects, and an 11% false positive rate for multifocal glass detection. This research contributes solutions to the problems of imaging deformation inspection and provides computer-aided systems for determining quality levels that meet the demands of inspection and quality control.

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A Novel Method for Optical Distortion Quantification

Cited by 2 publications

References 3 publications

Vision based deformation inspection system for automotive glass using Hough circle detectors

Vision based deformation inspection system for automotive glass using Hough circle detectors

Optical Imaging Deformation Inspection and Quality Level Determination of Multifocal Glasses

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