The Experts below are selected from a list of 52518 Experts worldwide ranked by ideXlab platform
Lisa Dang - One of the best experts on this subject based on the ideXlab platform.
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Pixel Level decorrelation in service of the spitzer microlens parallax survey
Monthly Notices of the Royal Astronomical Society, 2020Co-Authors: Lisa Dang, Calchi S Novati, S Carey, Nicolas B CowanAbstract:Microlens parallax measurements combining space-based and ground-based observatories can be used to study planetary demographics. In recent years, the Spitzer Space Telescope was used as a microlens parallax satellite. Meanwhile, Spitzer IRAC has been employed to study short-period exoplanets and their atmospheres. As these investigations require exquisite photometry, they motivated the development of numerous self-calibration techniques now widely used in the exoplanet atmosphere community. Specifically, Pixel Level decorrelation (PLD) was developed for starring-mode observations in uncrowded fields. We adapt and extend PLD to make it suitable for observations obtained as part of the Spitzer Microlens Parallax Campaign. We apply our method to two previously published microlensing events, OGLE-2017-BLG-1140 and OGLE-2015-BLG-0448, and compare its performance to the state-of-the-art pipeline used to analyses Spitzer microlensing observation. We find that our method yields photometry 1.5–6 times as precise as previously published. In addition to being useful for Spitzer, a similar approach could improve microlensing photometry with the forthcoming Nancy Grace Roman Space Telescope.
Kelvin C P Wang - One of the best experts on this subject based on the ideXlab platform.
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Pixel Level cracking detection on 3d asphalt pavement images through deep learning based cracknet v
IEEE Transactions on Intelligent Transportation Systems, 2020Co-Authors: Yue Fei, Allen Zhang, Kelvin C P Wang, Yang Liu, Cheng Chen, Guangwei YangAbstract:A few recent developments have demonstrated that deep-learning-based solutions can outperform traditional algorithms for automated pavement crack detection. In this paper, an efficient deep network called CrackNet-V is proposed for automated Pixel-Level crack detection on 3D asphalt pavement images. Compared with the original CrackNet, CrackNet-V has a deeper architecture but fewer parameters, resulting in improved accuracy and computation efficiency. Inspired by CrackNet, CrackNet-V uses invariant spatial size through all layers such that supervised learning can be conducted at Pixel Level. Following the VGG network, CrackNet-V uses $3\times 3$ size of filters for the first six convolutional layers and stacks several $3\times 3$ convolutional layers together for deep abstraction, resulting in reduced number of parameters and efficient feature extraction. CrackNet-V has 64113 parameters and consists of ten layers, including one pre-process layer, eight convolutional layers, and one output layer. A new activation function leaky rectified tanh is proposed in this paper for higher accuracy in detecting shallow cracks. The training of CrackNet-V was completed after 3000 iterations, which took only one day on a GeForce GTX 1080Ti device. According to the experimental results on 500 testing images, CrackNet-V achieves a high performance with a Precision of 84.31%, Recall of 90.12%, and an F-1 score of 87.12%. It is shown that CrackNet-V yields better overall performance particularly in detecting fine cracks compared with CrackNet. The efficiency of CrackNet-V further reveals the advantages of deep learning techniques for automated Pixel-Level pavement crack detection.
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automated Pixel Level pavement crack detection on 3d asphalt surfaces with a recurrent neural network
Computer-aided Civil and Infrastructure Engineering, 2019Co-Authors: Allen Zhang, Kelvin C P Wang, Yue Fei, Yang Liu, Cheng Chen, Guangwei Yang, Enhui Yang, Shi QiuAbstract:Abstract A recurrent neural network (RNN) called CrackNet‐R is proposed in the article for fully automated Pixel‐Level crack detection on three‐dimensional (3D) asphalt pavement surfaces. In the ar...
Guangwei Yang - One of the best experts on this subject based on the ideXlab platform.
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Pixel Level cracking detection on 3d asphalt pavement images through deep learning based cracknet v
IEEE Transactions on Intelligent Transportation Systems, 2020Co-Authors: Yue Fei, Allen Zhang, Kelvin C P Wang, Yang Liu, Cheng Chen, Guangwei YangAbstract:A few recent developments have demonstrated that deep-learning-based solutions can outperform traditional algorithms for automated pavement crack detection. In this paper, an efficient deep network called CrackNet-V is proposed for automated Pixel-Level crack detection on 3D asphalt pavement images. Compared with the original CrackNet, CrackNet-V has a deeper architecture but fewer parameters, resulting in improved accuracy and computation efficiency. Inspired by CrackNet, CrackNet-V uses invariant spatial size through all layers such that supervised learning can be conducted at Pixel Level. Following the VGG network, CrackNet-V uses $3\times 3$ size of filters for the first six convolutional layers and stacks several $3\times 3$ convolutional layers together for deep abstraction, resulting in reduced number of parameters and efficient feature extraction. CrackNet-V has 64113 parameters and consists of ten layers, including one pre-process layer, eight convolutional layers, and one output layer. A new activation function leaky rectified tanh is proposed in this paper for higher accuracy in detecting shallow cracks. The training of CrackNet-V was completed after 3000 iterations, which took only one day on a GeForce GTX 1080Ti device. According to the experimental results on 500 testing images, CrackNet-V achieves a high performance with a Precision of 84.31%, Recall of 90.12%, and an F-1 score of 87.12%. It is shown that CrackNet-V yields better overall performance particularly in detecting fine cracks compared with CrackNet. The efficiency of CrackNet-V further reveals the advantages of deep learning techniques for automated Pixel-Level pavement crack detection.
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automated Pixel Level pavement crack detection on 3d asphalt surfaces with a recurrent neural network
Computer-aided Civil and Infrastructure Engineering, 2019Co-Authors: Allen Zhang, Kelvin C P Wang, Yue Fei, Yang Liu, Cheng Chen, Guangwei Yang, Enhui Yang, Shi QiuAbstract:Abstract A recurrent neural network (RNN) called CrackNet‐R is proposed in the article for fully automated Pixel‐Level crack detection on three‐dimensional (3D) asphalt pavement surfaces. In the ar...
Lam Siew Kei - One of the best experts on this subject based on the ideXlab platform.
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automatic Pixel Level pavement crack detection using information of multi scale neighborhoods
IEEE Access, 2018Co-Authors: Guiyuan Jiang, Lam Siew KeiAbstract:Robust automatic pavement crack detection is critical to automated road condition evaluation. However, research on crack detection is still limited and Pixel-Level automatic crack detection remains a challenging problem, due to heterogeneous Pixel intensity, complex crack topology, poor illumination condition, and noisy texture background. In this paper, we propose a novel approach for automatically detecting pavement cracks at Pixel Level, leveraging on multi-scale neighborhood information, and Pixel intensity. Using Pixel intensity information, a probabilistic generative model (PGM) based method is developed to calculate the probability of a crack for each Pixel. This produces a probability map consisting of the probability of each Pixel being part of the crack. We demonstrate that the neighborhoods of each Pixel contain critical information for crack detection, and propose a support vector machine (SVM) based method to calculate the probability maps using information of multi-scale neighborhoods. We develop a fusion algorithm to merge the multiple probability maps, obtained from both PGM and SVM approaches, into a fused map, which can detect cracks with accuracy higher than any of the original probability maps. We also propose a weighted dilation operation that relies on the fused probability map to enhance the recognition of borderline Pixels and improve the crack continuity without increasing the crack width improperly. Experimental results demonstrate that our algorithm achieves better performance in terms of precision, recall, f1-score, and receiver operating characteristic, in comparison with the state-of-the-art pavement crack detection algorithms.
Robert Street - One of the best experts on this subject based on the ideXlab platform.
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flat panel imagers with Pixel Level amplifiers based on polycrystalline silicon thin film transistor technology
Applied Physics Letters, 2002Co-Authors: K Van Schuylenbergh, Yunda Wang, J B Boyce, Robert StreetAbstract:We report here the realization of a large-area compatible, flat panel imager with Pixel Level amplifiers. The imager is based on excimer-laser crystallized, polycrystalline silicon (poly-Si) thin-film transistors. By incorporating Pixel Level amplification, flat panel imagers are expected to be able to achieve unprecedented noise performance, with the hope of achieving single photon detection. We have demonstrated good noise performance of 1300 erms, exceeding the commonly accepted industry standard of 2000 erms. We also briefly discuss the source of the extra noise, as well as the possibility of further reducing the noise Level.
