YIN, Yingjie ;XU, De ;ZHANG, Zhengtao ;BAI, Mingran ;ZHANG, Feng ;TAO, Xian ;WANG, Xingang . Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 61, n.1, p. 24-32, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/surface-defect-detection-on-optical-devices-based-on-microscopic-dark-field-scattering-imaging/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2014.1644.
Yin, Y., Xu, D., Zhang, Z., Bai, M., Zhang, F., Tao, X., & Wang, X. (2015). Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging. Strojniški vestnik - Journal of Mechanical Engineering, 61(1), 24-32. doi:http://dx.doi.org/10.5545/sv-jme.2014.1644
@article{sv-jmesv-jme.2014.1644,
author = {Yingjie Yin and De Xu and Zhengtao Zhang and Mingran Bai and Feng Zhang and Xian Tao and Xingang Wang},
title = {Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {61},
number = {1},
year = {2015},
keywords = {SIFT, LDF, cluster algorithm, image segmentation, image mosaic, dark-field imaging, optical devices},
abstract = {Methods of surface defect detection on optical devices are proposed in this paper. First, a series of microscopic dark-field scattering images were collected with a line-scan camera. Translation transformation between overlaps of adjacent microscopic dark-field scattering images resulted from the line-scan camera’s imaging feature. An image mosaic algorithm based on scale invariance feature transform (SIFT) is proposed to stitch dark-field images collected by the line-scan camera. SIFT feature matching point-pairs were extracted from regions of interest in the adjacent microscopic dark-field scattering images. The best set of SIFT feature matching point-pairs was obtained via a parallel clustering algorithm. The transformation matrix of the two images was calculated by the best matching point-pair set, and then image stitching was completed through transformation matrix. Secondly, a sample threshold segmentation method was used to segment dark-field images that were previously stitched together because the image background was very dark. Finally, four different supervised learning classifiers are used to classify the defect represented by a six-dimensional feature vector by shape (point or line), and the performance of linear discriminant function (LDF) classifier is demonstrated to be the best. The experimental results showed that defects on optical devices could be detected efficiently by the proposed methods.},
issn = {0039-2480}, pages = {24-32}, doi = {10.5545/sv-jme.2014.1644},
url = {https://www.sv-jme.eu/sl/article/surface-defect-detection-on-optical-devices-based-on-microscopic-dark-field-scattering-imaging/}
}
Yin, Y.,Xu, D.,Zhang, Z.,Bai, M.,Zhang, F.,Tao, X.,Wang, X. 2015 June 61. Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 61:1
%A Yin, Yingjie %A Xu, De %A Zhang, Zhengtao %A Bai, Mingran %A Zhang, Feng %A Tao, Xian %A Wang, Xingang %D 2015 %T Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging %B 2015 %9 SIFT, LDF, cluster algorithm, image segmentation, image mosaic, dark-field imaging, optical devices %! Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging %K SIFT, LDF, cluster algorithm, image segmentation, image mosaic, dark-field imaging, optical devices %X Methods of surface defect detection on optical devices are proposed in this paper. First, a series of microscopic dark-field scattering images were collected with a line-scan camera. Translation transformation between overlaps of adjacent microscopic dark-field scattering images resulted from the line-scan camera’s imaging feature. An image mosaic algorithm based on scale invariance feature transform (SIFT) is proposed to stitch dark-field images collected by the line-scan camera. SIFT feature matching point-pairs were extracted from regions of interest in the adjacent microscopic dark-field scattering images. The best set of SIFT feature matching point-pairs was obtained via a parallel clustering algorithm. The transformation matrix of the two images was calculated by the best matching point-pair set, and then image stitching was completed through transformation matrix. Secondly, a sample threshold segmentation method was used to segment dark-field images that were previously stitched together because the image background was very dark. Finally, four different supervised learning classifiers are used to classify the defect represented by a six-dimensional feature vector by shape (point or line), and the performance of linear discriminant function (LDF) classifier is demonstrated to be the best. The experimental results showed that defects on optical devices could be detected efficiently by the proposed methods. %U https://www.sv-jme.eu/sl/article/surface-defect-detection-on-optical-devices-based-on-microscopic-dark-field-scattering-imaging/ %0 Journal Article %R 10.5545/sv-jme.2014.1644 %& 24 %P 9 %J Strojniški vestnik - Journal of Mechanical Engineering %V 61 %N 1 %@ 0039-2480 %8 2018-06-27 %7 2018-06-27
Yin, Yingjie, De Xu, Zhengtao Zhang, Mingran Bai, Feng Zhang, Xian Tao, & Xingang Wang. "Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging." Strojniški vestnik - Journal of Mechanical Engineering [Online], 61.1 (2015): 24-32. Web. 20 Dec. 2024
TY - JOUR AU - Yin, Yingjie AU - Xu, De AU - Zhang, Zhengtao AU - Bai, Mingran AU - Zhang, Feng AU - Tao, Xian AU - Wang, Xingang PY - 2015 TI - Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2014.1644 KW - SIFT, LDF, cluster algorithm, image segmentation, image mosaic, dark-field imaging, optical devices N2 - Methods of surface defect detection on optical devices are proposed in this paper. First, a series of microscopic dark-field scattering images were collected with a line-scan camera. Translation transformation between overlaps of adjacent microscopic dark-field scattering images resulted from the line-scan camera’s imaging feature. An image mosaic algorithm based on scale invariance feature transform (SIFT) is proposed to stitch dark-field images collected by the line-scan camera. SIFT feature matching point-pairs were extracted from regions of interest in the adjacent microscopic dark-field scattering images. The best set of SIFT feature matching point-pairs was obtained via a parallel clustering algorithm. The transformation matrix of the two images was calculated by the best matching point-pair set, and then image stitching was completed through transformation matrix. Secondly, a sample threshold segmentation method was used to segment dark-field images that were previously stitched together because the image background was very dark. Finally, four different supervised learning classifiers are used to classify the defect represented by a six-dimensional feature vector by shape (point or line), and the performance of linear discriminant function (LDF) classifier is demonstrated to be the best. The experimental results showed that defects on optical devices could be detected efficiently by the proposed methods. UR - https://www.sv-jme.eu/sl/article/surface-defect-detection-on-optical-devices-based-on-microscopic-dark-field-scattering-imaging/
@article{{sv-jme}{sv-jme.2014.1644},
author = {Yin, Y., Xu, D., Zhang, Z., Bai, M., Zhang, F., Tao, X., Wang, X.},
title = {Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {61},
number = {1},
year = {2015},
doi = {10.5545/sv-jme.2014.1644},
url = {https://www.sv-jme.eu/sl/article/surface-defect-detection-on-optical-devices-based-on-microscopic-dark-field-scattering-imaging/}
}
TY - JOUR AU - Yin, Yingjie AU - Xu, De AU - Zhang, Zhengtao AU - Bai, Mingran AU - Zhang, Feng AU - Tao, Xian AU - Wang, Xingang PY - 2018/06/27 TI - Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 61, No 1 (2015): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2014.1644 KW - SIFT, LDF, cluster algorithm, image segmentation, image mosaic, dark-field imaging, optical devices N2 - Methods of surface defect detection on optical devices are proposed in this paper. First, a series of microscopic dark-field scattering images were collected with a line-scan camera. Translation transformation between overlaps of adjacent microscopic dark-field scattering images resulted from the line-scan camera’s imaging feature. An image mosaic algorithm based on scale invariance feature transform (SIFT) is proposed to stitch dark-field images collected by the line-scan camera. SIFT feature matching point-pairs were extracted from regions of interest in the adjacent microscopic dark-field scattering images. The best set of SIFT feature matching point-pairs was obtained via a parallel clustering algorithm. The transformation matrix of the two images was calculated by the best matching point-pair set, and then image stitching was completed through transformation matrix. Secondly, a sample threshold segmentation method was used to segment dark-field images that were previously stitched together because the image background was very dark. Finally, four different supervised learning classifiers are used to classify the defect represented by a six-dimensional feature vector by shape (point or line), and the performance of linear discriminant function (LDF) classifier is demonstrated to be the best. The experimental results showed that defects on optical devices could be detected efficiently by the proposed methods. UR - https://www.sv-jme.eu/sl/article/surface-defect-detection-on-optical-devices-based-on-microscopic-dark-field-scattering-imaging/
Yin, Yingjie, Xu, De, Zhang, Zhengtao, Bai, Mingran, Zhang, Feng, Tao, Xian, AND Wang, Xingang. "Surface Defect Detection on Optical Devices based on Microscopic Dark-Field Scattering Imaging" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 61 Number 1 (27 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 61(2015)1, 24-32
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Methods of surface defect detection on optical devices are proposed in this paper. First, a series of microscopic dark-field scattering images were collected with a line-scan camera. Translation transformation between overlaps of adjacent microscopic dark-field scattering images resulted from the line-scan camera’s imaging feature. An image mosaic algorithm based on scale invariance feature transform (SIFT) is proposed to stitch dark-field images collected by the line-scan camera. SIFT feature matching point-pairs were extracted from regions of interest in the adjacent microscopic dark-field scattering images. The best set of SIFT feature matching point-pairs was obtained via a parallel clustering algorithm. The transformation matrix of the two images was calculated by the best matching point-pair set, and then image stitching was completed through transformation matrix. Secondly, a sample threshold segmentation method was used to segment dark-field images that were previously stitched together because the image background was very dark. Finally, four different supervised learning classifiers are used to classify the defect represented by a six-dimensional feature vector by shape (point or line), and the performance of linear discriminant function (LDF) classifier is demonstrated to be the best. The experimental results showed that defects on optical devices could be detected efficiently by the proposed methods.