2,454 research outputs found
Recommended from our members
The MicroBooNE Search For Anomalous Electron Neutrino Appearance Using Image Based Data Reconstruction
This thesis presents the MicroBooNE search for the MiniBooNE low energy excess using a fully automated image based data reconstruction scheme. A suite of traditional and deep learning computer vision algorithms are developed for identification of charge current quasi-elastic (CCQE) like muon and electron neutrino interactions using the MicroBooNE detector. Given a model of the MiniBooNE low energy excess as due to an enhancement of electron neutrino type events, this analysis predicts a combined statistical and systematic 3.8σ low energy signal in 13.2 × 1020 POT of MicroBooNE data. When interpreted in the context of νμ → νe 3 + 1 sterile neutrino oscillations a best fit point of (∆m241, sin2 2θeμ) = (0.063,0.794) is found with a 90% confidence allowed region consistent with > 0.1 eV2 oscillation
Augmented reality based real-time subcutaneous vein imaging system
A novel 3D reconstruction and fast imaging system for subcutaneous veins by augmented reality is presented. The study was performed to reduce the failure rate and time required in intravenous injection by providing augmented vein structures that back-project superimposed veins on the skin surface of the hand. Images of the subcutaneous vein are captured by two industrial cameras with extra reflective near-infrared lights. The veins are then segmented by a multiple-feature clustering method. Vein structures captured by the two cameras are matched and reconstructed based on the epipolar constraint and homographic property. The skin surface is reconstructed by active structured light with spatial encoding values and fusion displayed with the reconstructed vein. The vein and skin surface are both reconstructed in the 3D space. Results show that the structures can be precisely back-projected to the back of the hand for further augmented display and visualization. The overall system performance is evaluated in terms of vein segmentation, accuracy of vein matching, feature points distance error, duration times, accuracy of skin reconstruction, and augmented display. All experiments are validated with sets of real vein data. The imaging and augmented system produces good imaging and augmented reality results with high speed
Fast 2D/3D object representation with growing neural gas
This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction
Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data
Grain boundaries (GBs) are planar lattice defects that govern the properties
of many types of polycrystalline materials. Hence, their structures have been
investigated in great detail. However, much less is known about their chemical
features, owing to the experimental difficulties to probe these features at the
atomic length scale inside bulk material specimens. Atom probe tomography (APT)
is a tool capable of accomplishing this task, with an ability to quantify
chemical characteristics at near-atomic scale. Using APT data sets, we present
here a machine-learning-based approach for the automated quantification of
chemical features of GBs. We trained a convolutional neural network (CNN) using
twenty thousand synthesized images of grain interiors, GBs, or triple
junctions. Such a trained CNN automatically detects the locations of GBs from
APT data. Those GBs are then subjected to compositional mapping and analysis,
including revealing their in-plane chemical decoration patterns. We applied
this approach to experimentally obtained APT data sets pertaining to three case
studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we
extracted GB-specific segregation features as a function of misorientation and
coincidence site lattice character. Secondly, we revealed interfacial excesses
and in-plane chemical features that could not have been found by standard
compositional analyses. Lastly, we tracked the temporal evolution of chemical
decoration from early-stage solute GB segregation in the dilute limit to
interfacial phase separation, characterized by the evolution of complex
composition patterns. This machine-learning-based approach provides
quantitative, unbiased, and automated access to GB chemical analyses, serving
as an enabling tool for new discoveries related to interface thermodynamics,
kinetics, and the associated chemistry-structure-property relations
3D Reconstruction of Indoor Corridor Models Using Single Imagery and Video Sequences
In recent years, 3D indoor modeling has gained more attention due to its role in decision-making process of maintaining the status and managing the security of building indoor spaces. In this thesis, the problem of continuous indoor corridor space modeling has been tackled through two approaches. The first approach develops a modeling method based on middle-level perceptual organization. The second approach develops a visual Simultaneous Localisation and Mapping (SLAM) system with model-based loop closure.
In the first approach, the image space was searched for a corridor layout that can be converted into a geometrically accurate 3D model. Manhattan rule assumption was adopted, and indoor corridor layout hypotheses were generated through a random rule-based intersection of image physical line segments and virtual rays of orthogonal vanishing points. Volumetric reasoning, correspondences to physical edges, orientation map and geometric context of an image are all considered for scoring layout hypotheses. This approach provides physically plausible solutions while facing objects or occlusions in a corridor scene.
In the second approach, Layout SLAM is introduced. Layout SLAM performs camera localization while maps layout corners and normal point features in 3D space. Here, a new feature matching cost function was proposed considering both local and global context information. In addition, a rotation compensation variable makes Layout SLAM robust against cameras orientation errors accumulations. Moreover, layout model matching of keyframes insures accurate loop closures that prevent miss-association of newly visited landmarks to previously visited scene parts.
The comparison of generated single image-based 3D models to ground truth models showed that average ratio differences in widths, heights and lengths were 1.8%, 3.7% and 19.2% respectively. Moreover, Layout SLAM performed with the maximum absolute trajectory error of 2.4m in position and 8.2 degree in orientation for approximately 318m path on RAWSEEDS data set. Loop closing was strongly performed for Layout SLAM and provided 3D indoor corridor layouts with less than 1.05m displacement errors in length and less than 20cm in width and height for approximately 315m path on York University data set. The proposed methods can successfully generate 3D indoor corridor models compared to their major counterpart
Experimental demonstration of a graph state quantum error-correction code
Scalable quantum computing and communication requires the protection of
quantum information from the detrimental effects of decoherence and noise.
Previous work tackling this problem has relied on the original circuit model
for quantum computing. However, recently a family of entangled resources known
as graph states has emerged as a versatile alternative for protecting quantum
information. Depending on the graph's structure, errors can be detected and
corrected in an efficient way using measurement-based techniques. In this
article we report an experimental demonstration of error correction using a
graph state code. We have used an all-optical setup to encode quantum
information into photons representing a four-qubit graph state. We are able to
reliably detect errors and correct against qubit loss. The graph we have
realized is setup independent, thus it could be employed in other physical
settings. Our results show that graph state codes are a promising approach for
achieving scalable quantum information processing
- …