Investigating Operational Solutions for MBE Assurance
Abstract not provided.
Publications
SEIS AR/VR: Present and Future
Abstract not provided.
Software Product Lines for Immersive Training
Abstract not provided.
Three Critical Considerations for Adopting Immersive Technology
Abstract not provided.
Non-Destructive Materials Identification System using Spectral Computed Tomography
Abstract not provided.
Material Identification and Classification using Machine Learning Techniques with Hyperspectral Computed Tomography
Abstract not provided.
Material Identification and Classification using Machine Learning Techniques with Hyperspectral Computed Tomography
Abstract not provided.
Unsupervised Learning Methods to Perform Material Identification Tasks on Spectral Computed Tomography Data
Abstract not provided.
Supervised Deep Learning Techniques for Material Classification with Spectral Computed Tomography Datasets
Abstract not provided.
Unsupervised Learning Methods to Perform Material Identification Tasks on Spectral Computed Tomography Data
Abstract not provided.
Passenger baggage object database (PBOD)
AIP Conference Proceedings
Detection of anomalies of interest in x-ray images is an ever-evolving problem that requires the rapid development of automatic detection algorithms. Automatic detection algorithms are developed using machine learning techniques, which would require developers to obtain the x-ray machine that was used to create the images being trained on, and compile all associated metadata for those images by hand. The Passenger Baggage Object Database (PBOD) and data acquisition application were designed and developed for acquiring and persisting 2-D and 3-D x-ray image data and associated metadata. PBOD was specifically created to capture simulated airline passenger "stream of commerce" luggage data, but could be applied to other areas of x-ray imaging to utilize machine-learning methods.
Machine Learning for Industrial Material Classification Applications with Color CT Datasets
Abstract not provided.
Unsupervised learning methods to perform material identification tasks on spectral computed tomography data
Proceedings of SPIE - The International Society for Optical Engineering
Sandia National Laboratories has developed a method that applies machine learning methods to high-energy spectral X-ray computed tomography data to identify material composition for every reconstructed voxel in the field-of-view. While initial experiments led by Koundinyan et al. demonstrated that supervised machine learning techniques perform well in identifying a variety of classes of materials, this work presents an unsupervised approach that differentiates isolated materials with highly similar properties, and can be applied on spectral computed tomography data to identify materials more accurately compared to traditional performance. Additionally, if regions of the spectrum for multiple voxels become unusable due to artifacts, this method can still reliably perform material identification. This enhanced capability can tremendously impact fields in security, industry, and medicine that leverage non-destructive evaluation for detection, verification, and validation applications.
Passenger Baggage Object Database
Abstract not provided.
14 Results