- 标题
- 摘要
- 关键词
- 实验方案
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Real-time tracking of fluorescent magnetic spore–based microrobots for remote detection of <i>C. diff</i> toxins
摘要： A rapid, direct, and low-cost method for detecting bacterial toxins associated with common gastrointestinal diseases remains a great challenge despite numerous studies and clinical assays. Motion-based detection through tracking the emerging micro- and nanorobots has shown great potential in chemo- and biosensing due to accelerated 'chemistry on the move'. Here, we described the use of fluorescent magnetic spore–based microrobots (FMSMs) as a highly efficient mobile sensing platform for the detection of toxins secreted by Clostridium difficile (C. diff) that were present in patients' stool. These microrobots were synthesized rapidly and inexpensively by the direct deposition of magnetic nanoparticles and the subsequent encapsulation of sensing probes on the porous natural spores. Because of the cooperation effect of natural spore, magnetic Fe3O4 nanoparticles, and functionalized carbon nanodots, selective fluorescence detection of the prepared FMSMs is demonstrated in C. diff bacterial supernatant and even in actual clinical stool samples from infectious patients within tens of minutes, suggesting rapid response and good selectivity and sensitivity of FMSMs toward C. diff toxins.
关键词： biosensing,real-time tracking,C. diff toxins,fluorescent magnetic spore-based microrobots,remote detection
更新于2025-09-23 15:22:29
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A fiducial-less tracking method for radiation therapy of liver tumors by diaphragm disparity analysis part 2: validation study by using clinical data
摘要： Objective Motion management must be considered in treating liver tumors. One effective approach is real-time tumor tracking, which can be performed by the CyberKnife? Robotic Radiosurgery System through the Synchrony? Respiratory Tracking System. It uses a combination of kV images, LED markers, an infrared camera, and surgically implanted fiducial markers to track tumors under the influence of respiration. However, the use of fiducial markers through an invasive procedure can lead to complications. In our previous simulation study, we were able to demonstrate the feasibility of our proposed fiducial-less tracking technique using a digital phantom under regular respiratory motion. The aim of this study is to further validate this innovative method by using a digital phantom data under the irregular respiratory cycles as well as clinical data from patients under the Cyberknife environment. Methods As performed in our previous simulation study, abdominal 4DCT datasets of one breathing cycle, from the digital phantom and from four patients, were previously generated or acquired. Associated with the breathing cycles in the 4DCT datasets, one set of DRR images (+ 45° or ? 45°) was produced for each breathing phase. On each DRR, an outline of the lung-diaphragm border was detected using an edge detection algorithm. The tracked target volume’s gravity center was identified for each phase of the breathing cycle by a MATLAB program, serving as the ground truth for the validation. Using artificial neural networks (ANN), four models for the phantom and six models for the patient data, correlating the diaphragm’s location with the corresponding 3D location of the tracked target volume, were compared. Assessment was performed by using the root-mean-squared error (RMSE) values through the leave-one-out (LOO) validation criterion. Results The averaged RMSE for the phantom data was 1.05 ± 1.14 mm. When using the patient data from the + 45° projection, the averaged RMSE was 2.13 ± 1.79 mm, while from the ? 45° projection, the averaged RMSE was 2.26 ± 2.40 mm. Using the proposed method in both phantom validation and patient data validation, the RMSE is closely related to the 4DCT reconstruction error and to the distance from the lung-diaphragm border to the tracked tumor. Conclusion We proposed and investigated the fiducial-less tracking method to follow tumor motion in the real-time under the influence of respiration. The study shows the feasibility of accurately predicting the tumor’s position with the use of lung-diaphragm border’s information through available kV images without gold fiducial markers. This developed diaphragm disparity-analysis-based approach, verified with clinically accepted errors, has the potential to replace fiducial markers in clinical applications.
关键词： Liver tumor,Real-time tracking,4D XCAT phantom,Diaphragm,Image-guided radiation therapy,4DCT
更新于2025-09-23 15:21:01
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Real-time spatial intersecting seam tracking based on laser vision stereo sensor
摘要： A real-time robotic weld tracking system based on laser vision sensor is designed for intersecting seam. Firstly, the traditional image processing method is used to determine the weld feature point in the first frame when there has no arc noise. Then, an extending and adopting Kalman Filter with Spatio-Temporal Context algorithm was proposed to extract the weld feature point when the laser stripe was blocked by heavy arc and splash noise during welding. Next, in order to control torch to weld automatically, the torch frame was established through novel three points principle and an expanding circle and arc length method. In addition, a segmentation weld method and an intermediate proportional interpolation method for step-size control of mobile torch were proposed to guarantee the accurate of weld. Finally, Experiments and analyses results show that the tracking system is good in real-time, accuracy, stability and flexible, which can meet the weld requirements.
关键词： Laser vision sensor,Kalman filter,Real-time tracking,Spatio-Temporal Context,Intersecting seam,Trajectory planning
更新于2025-09-12 10:27:22
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[IEEE 2018 International Conference on 3D Vision (3DV) - Verona (2018.9.5-2018.9.8)] 2018 International Conference on 3D Vision (3DV) - Multi-scale Direct Sparse Visual Odometry for Large-Scale Natural Environment
摘要： In this paper, we describe a multi-scale monocular direct sparse visual odometry (DSO) system to recover large-scale trajectories in unstructured natural environments in real time, while building a consistent metric map of the visited scenes. In contrast to the current state-of-the-art DSO system, the proposed method allows for more robust motion estimation and more accurate reconstruction in distant scenes by exploiting the characteristics of short- and long-range pixels, respectively. The long-range pixels, which are less sensitive to small camera translations, are used to initialize the camera rotation, so as to boost the tracking robustness in challenging natural environments. A multi-scale reconstruction framework is developed to recover short-range structure over successive frames, as well as the long-range structure over distant frames, hence allowing for a more consistent mapping precision. The reconstruction precision, the tracking accuracy, and the robustness of the proposed system are extensively evaluated with a publicly available vKITTI dataset, as well as the challenging Devon Island dataset, and Symphony Lake dataset. A detailed performance comparison between the proposed method and the state-of-the-art DSO system is presented.
关键词： multi-scale,large-scale natural environment,monocular vision,3D mapping,direct sparse visual odometry,real-time tracking
更新于2025-09-10 09:29:36
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Real-time body tracking in virtual reality using a Vive tracker
摘要： Due to recent improvements in virtual reality (VR) technology, the number of novel applications for entertainment, education, and rehabilitation has increased. The primary goal of these applications is to enhance the sense of belief that the user is “present” in the virtual environment. By tracking the user’s skeleton in real-time, it is possible to synchronize the avatar’s motions with the user’s motions. Although current common devices implement body tracking to a certain degree, most approaches are limited by either high latency or insufficient accuracy. Due to the lack of positional and rotation data, the current VR applications typically do not represent the user’s motions. In this paper, we present an accurate, low-latency body tracking approach for VR-based applications using Vive Trackers. Using a HTC Vive headset and Vive Trackers, we have been able to create an immersive VR experience, by animating the motions of the avatar as smoothly, rapidly and as accurately as possible. An evaluation showed our solution is capable of tracking both joint rotation and position with reasonable accuracy and a very low end-to-latency of 6.71 ± 0.80 ms . Due to this merely imperceptible delay and precise tracking, our solution can show the movements of the user in real-time in order to create deeper immersion.
关键词： Full-body avatar,Real-time tracking,Inverse kinematics,Low-latency,Virtual reality,HTC Vive tracker
更新于2025-09-10 09:29:36
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[IEEE 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) - Nantes, France (2018.9.24-2018.9.27)] 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) - Real-Time Localization and Tracking Using Visible Light Communication
摘要： In this paper an optical indoor positioning system is proposed, which utilizes modulated LED lights as anchor points, deployed in known positions. The sensor, deployed on the tracked object (e.g. autonomous vehicle), is an ordinary camera facing upwards. The system is able to estimate the position and orientation of the moving object in real-time, based on the camera’s image stream, containing images of the anchor points. The paper contains comprehensive analysis on the possible error sources and their effect on the positioning accuracy. Real measurement tests show that the accuracy of the system is in the low centimeter range even if the tracked camera moves with a speed of 1 m/s.
关键词： indoor positioning,heuristic data fusion,Visible Light Communication,fisheye camera,real-time tracking
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Multi-layer CNN Features Aggregation for Real-time Visual Tracking
摘要： In this paper, we propose a novel convolutional neural network (CNN) based tracking framework, which aggregates multiple CNN features from different layers into a robust representation and realizes real-time tracking. We found that some feature maps have interference for effectively representing objects. Instead of using original features, we build an end-to-end feature aggregation network (FAN) which suppresses the noisy feature maps of CNN layers. The feature significantly benefits to represent objects with both coarse semantic information and fine details. The FAN, as a light-weight network, can run at real-time. The highlighted region of feature maps obtained from the FAN is the tracking result. Our method performs at a real-time speed of 24 fps while maintaining a promising accuracy compared with state-of-the-art methods on existing tracking benchmarks.
关键词： real-time tracking,convolutional neural network,feature aggregation,visual tracking
更新于2025-09-04 15:30:14
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Rapid tracking of extrinsic projector parameters in fringe projection using machine learning
摘要： In this work, we propose to enable the angular re-orientation of a projector within a fringe projection system in real-time without the need for re-calibrating the system. The estimation of the extrinsic orientation parameters of the projector is performed using a convolutional neural network and images acquired from the camera in the setup. The convolutional neural network was trained to classify the azimuth and elevation angles of the projector approximated by a point source through shadow images of the measured object. The images used to train the neural network were generated through the use of CAD rendering, by simulating the illumination of the object model from di?erent directions and then rendering an image of its shadow. The accuracy to which the azimuth and elevation angles are estimated is within 1 classi?cation bin, where 1 bin is designated as a ± 10° patch of the illumination dome. To evaluate use of the proposed system in fringe projection, a pyramidal additively manufactured object was measured. The point clouds generated using the proposed method were compared to those obtained by an established fringe projection calibration method. The maximum dimensional error in the point cloud generated when using the convolutional network as compared to the established calibration method for the object measured was found to be 1.05 mm on average.
关键词： real-time tracking,convolutional neural network,fringe projection,machine learning,projector calibration
更新于2025-09-04 15:30:14