443,922 research outputs found
FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events
Traditional visual place recognition (VPR), usually using standard cameras,
is easy to fail due to glare or high-speed motion. By contrast, event cameras
have the advantages of low latency, high temporal resolution, and high dynamic
range, which can deal with the above issues. Nevertheless, event cameras are
prone to failure in weakly textured or motionless scenes, while standard
cameras can still provide appearance information in this case. Thus, exploiting
the complementarity of standard cameras and event cameras can effectively
improve the performance of VPR algorithms. In the paper, we propose
FE-Fusion-VPR, an attention-based multi-scale network architecture for VPR by
fusing frames and events. First, the intensity frame and event volume are fed
into the two-stream feature extraction network for shallow feature fusion.
Next, the three-scale features are obtained through the multi-scale fusion
network and aggregated into three sub-descriptors using the VLAD layer.
Finally, the weight of each sub-descriptor is learned through the descriptor
re-weighting network to obtain the final refined descriptor. Experimental
results show that on the Brisbane-Event-VPR and DDD20 datasets, the Recall@1 of
our FE-Fusion-VPR is 29.26% and 33.59% higher than Event-VPR and
Ensemble-EventVPR, and is 7.00% and 14.15% higher than MultiRes-NetVLAD and
NetVLAD. To our knowledge, this is the first end-to-end network that goes
beyond the existing event-based and frame-based SOTA methods to fuse frame and
events directly for VPR
A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally,
conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002
and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140
Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior
We propose a novel, simple and effective method to integrate lesion prior and
a 3D U-Net for improving brain tumor segmentation. First, we utilize the
ground-truth brain tumor lesions from a group of patients to generate the
heatmaps of different types of lesions. These heatmaps are used to create the
volume-of-interest (VOI) map which contains prior information about brain tumor
lesions. The VOI map is then integrated with the multimodal MR images and input
to a 3D U-Net for segmentation. The proposed method is evaluated on a public
benchmark dataset, and the experimental results show that the proposed feature
fusion method achieves an improvement over the baseline methods. In addition,
our proposed method also achieves a competitive performance compared to
state-of-the-art methods.Comment: 5 pages, 4 figures, 1 table, LNCS forma
[68Ga]-DOTATOC-PET/CT for meningioma IMRT treatment planning
<p>Abstract</p> <p>Purpose</p> <p>The observation that human meningioma cells strongly express somatostatin receptor (SSTR 2) was the rationale to analyze retrospectively in how far DOTATOC PET/CT is helpful to improve target volume delineation for intensity modulated radiotherapy (IMRT).</p> <p>Patients and Methods</p> <p>In 26 consecutive patients with preferentially skull base meningioma, diagnostic magnetic resonance imaging (MRI) and planning-computed tomography (CT) was complemented with data from [<sup>68</sup>Ga]-DOTA-D Phe<sup>1</sup>-Tyr<sup>3</sup>-Octreotide (DOTATOC)-PET/CT. Image fusion of PET/CT, diagnostic computed tomography, MRI and radiotherapy planning CT as well as target volume delineation was performed with OTP-Masterplan<sup>®</sup>. Initial gross tumor volume (GTV) definition was based on MRI data only and was secondarily complemented with DOTATOC-PET information. Irradiation was performed as EUD based IMRT, using the Hyperion Software package.</p> <p>Results</p> <p>The integration of the DOTATOC data led to additional information concerning tumor extension in 17 of 26 patients (65%). There were major changes of the clinical target volume (CTV) which modify the PTV in 14 patients, minor changes were realized in 3 patients. Overall the GTV-MRI/CT was larger than the GTV-PET in 10 patients (38%), smaller in 13 patients (50%) and almost the same in 3 patients (12%). Most of the adaptations were performed in close vicinity to bony skull base structures or after complex surgery. Median GTV based on MRI was 18.1 cc, based on PET 25.3 cc and subsequently the CTV was 37.4 cc. Radiation planning and treatment of the DOTATOC-adapted volumes was feasible.</p> <p>Conclusion</p> <p>DOTATOC-PET/CT information may strongly complement patho-anatomical data from MRI and CT in cases with complex meningioma and is thus helpful for improved target volume delineation especially for skull base manifestations and recurrent disease after surgery.</p
Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction
State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors
usually reduce drift in camera tracking by globally optimizing the estimated
camera poses in real-time without simultaneously updating the reconstructed
surface on pose changes. We propose an efficient on-the-fly surface correction
method for globally consistent dense 3D reconstruction of large-scale scenes.
Our approach uses a dense Visual RGB-D SLAM system that estimates the camera
motion in real-time on a CPU and refines it in a global pose graph
optimization. Consecutive RGB-D frames are locally fused into keyframes, which
are incorporated into a sparse voxel hashed Signed Distance Field (SDF) on the
GPU. On pose graph updates, the SDF volume is corrected on-the-fly using a
novel keyframe re-integration strategy with reduced GPU-host streaming. We
demonstrate in an extensive quantitative evaluation that our method is up to
93% more runtime efficient compared to the state-of-the-art and requires
significantly less memory, with only negligible loss of surface quality.
Overall, our system requires only a single GPU and allows for real-time surface
correction of large environments.Comment: British Machine Vision Conference (BMVC), London, September 201
Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging
Many analyses of neuroimaging data involve studying one or more regions of
interest (ROIs) in a brain image. In order to do so, each ROI must first be
identified. Since every brain is unique, the location, size, and shape of each
ROI varies across subjects. Thus, each ROI in a brain image must either be
manually identified or (semi-) automatically delineated, a task referred to as
segmentation. Automatic segmentation often involves mapping a previously
manually segmented image to a new brain image and propagating the labels to
obtain an estimate of where each ROI is located in the new image. A more recent
approach to this problem is to propagate labels from multiple manually
segmented atlases and combine the results using a process known as label
fusion. To date, most label fusion algorithms either employ voting procedures
or impose prior structure and subsequently find the maximum a posteriori
estimator (i.e., the posterior mode) through optimization. We propose using a
fully Bayesian spatial regression model for label fusion that facilitates
direct incorporation of covariate information while making accessible the
entire posterior distribution. We discuss the implementation of our model via
Markov chain Monte Carlo and illustrate the procedure through both simulation
and application to segmentation of the hippocampus, an anatomical structure
known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure
In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography
Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented
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