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Attention guided U-Net for accurate iris segmentation
Journal of Visual Communication and Image Representation, 2018Abstract Iris segmentation is a critical step for improving the accuracy of iris recognition, as well as for medical concerns. Existing methods generally use whole eye images as input for network learning, which do not consider the geometric constrain that iris only occur in a specific area in the eye. As a result, such methods can be easily affected
Sheng Lian +5 more
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Synergistic attention U-Net for sublingual vein segmentation
Artificial Life and Robotics, 2019The tongue is one of the most sensitive organs of the human body. The changes in the tongue indicate the changes of the human state. One of the features of the tongue, which can be used to inspect the blood circulation of human, is the shape information of the sublingual vein.
Tingxiao Yang +4 more
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U-Net with Attention Mechanism for Retinal Vessel Segmentation
2019Retinal vessel is the only vessel which can be observed directly, retinal vessel analysis is a crucial method for the screening and diagnosis of related diseases. In this paper, we propose a retinal vessel segmentation method based on U-Net and attention mechanism.
Ze Si, Dongmei Fu, Jiahao Li
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Malaria Parasite Detection using Residual Attention U-Net
2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2021Malaria is a life-threatening disease caused by Plasmodium parasites, and which is still a serious health concern worldwide nowadays. However, it is curable if early diagnosis could be performed. Due to the lack of access to expertise for diagnosis of the disease, often in poorly developed and remote areas, an automated yet accurate diagnostic solution
Chiang Kang Tan +4 more
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Crack Detecting by Recursive Attention U-Net
2020 3rd International Conference on Robotics, Control and Automation Engineering (RCAE), 2020Crack detecting is a specific domain of semantic segmentation task for solving real-world applications such as pavement crack detection, bridge bottom crack inspection, solar cells or battery components defect detection. Different with general image with rich texture sematic information, road/bridge crack images are often in lack of semantic ...
Zhihao Wu +4 more
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Seismic Signal Denoising with Attention U-Net
2023Seismic stations record superpositions of the seismic signals generated by all kinds of seismic sources. In earthquake seismology, seismic noise sources can be natural events such as wind or anthropogenic events such as cars. In this study, we developed a machine learning (ML) based algorithm to remove the noise from earthquake data.
Deniz Ertuncay +2 more
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Virtual try-on based on attention U-Net
The Visual Computer, 2022Xinrong Hu +5 more
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Stream Attention Based U-Net for L3DAS23 Challenge
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023Honglong Wang +5 more
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DRA U-Net: An Attention based U-Net Framework for 2D Medical Image Segmentation
2021 IEEE International Conference on Big Data (Big Data), 2021Xian Zhang +7 more
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Attention Wave-U-Net for Acoustic Echo Cancellation
Interspeech 2020, 2020Jung-Hee Kim, Joon-Hyuk Chang
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