Blood oxygen saturation is an important physiological parameter to measure human *** oxygen saturation can be measured in real time through an optical lens with high ***,when there is the light reflection of the measu...
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Blood oxygen saturation is an important physiological parameter to measure human *** oxygen saturation can be measured in real time through an optical lens with high ***,when there is the light reflection of the measured area,the accuracy of the measurement results will fluctuate,leading to the instability of the measurement *** this paper,we improved the original algorithm by detecting and removing the highlight *** experimental results show that the accuracy of the improved method is higher than that of the traditional method in the case of specular reflection and the stability of the measurement results is enhanced.
Traditional surveillance video contains a large amount of information which is too jumbled. Real-time video summarization can solve this problem but also face much challenges. Different from file summarization, real-t...
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ISBN:
(纸本)9781450376822
Traditional surveillance video contains a large amount of information which is too jumbled. Real-time video summarization can solve this problem but also face much challenges. Different from file summarization, real-time video summarization requires higher efficiency. Meanwhile, the validity and quality of a summarization should be ensured. To tackle these problems, we propose a real-time video summarization strategy based on dual-camera. In our strategy, a static camera and a PTZ camera are necessary. The static camera is used to monitor the scene to detect and track moving targets, and the PTZ camera is used to capture the close-up information of moving targets as video summarization, and the collaboration of these two cameras is crucial. Specifically, in order to obtain multi-target summarization efficiently and effectively, the priority of target capturing is determined by its spatial information and historical representation in the scene. Extensive experiments are performed on real-time outdoor scene with our method. Experimental results show that our proposed method is robust enough to capture multiple targets in the same scene at the same time.
Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges...
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ISBN:
(数字)9781728162157
ISBN:
(纸本)9781728162164
Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools.
Advances in neuroscience have suggested that addiction is not only an ongoing dynamic transaction between the person, their behavior, and the environment, but a disturbance of neurotransmitters in neurons involved in ...
This paper presents sparse slow feature analysis (SFA) for efficient process monitoring and fault isolation, which is a new latent variable model for time series data. We first recast sparse SFA in terms of a novel re...
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Human pose estimation has an important impact on a wide range of applications from human-computer interface to surveillance and content-based video retrieval. For human pose estimation, joint obstructions and overlapp...
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In this paper, we propose a photorealistic style transfer network to emphasize the natural effect of photo realistic image stylization. In general, distortion of the image content and lacking of details are two typica...
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Previous works for PCB defect detection based on image difference and imageprocessing techniques have already achieved promising performance. However, they sometimes fall short because of the unaccounted defect patte...
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In this paper, we examine the synchronization problem in nonlinearly-coupled multi-weighted complex networks that contain uncertainties and varying delays. The complexities of these networks arise from the presence of...
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Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and ...
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