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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Exploring Object Detection and Image Classification Tasks for Niche Use Case in Naturalistic Driving Studies

Naturalistic driving studies consist of drivers using their personal vehicles and provide valuable real-world data, but privacy issues must be handled very carefully. Drivers sign a consent form when they elect to participate, but passengers do not for a variety of practical reasons. However, their privacy must still be protected. One large study includes a blurred image of the entire cabin which allows reviewers to find passengers in the vehicle; this protects the privacy but still allows a means of answering questions regarding the impact of passengers on driver behavior. A method for automatically counting the passengers would have scientific value for transportation researchers. We investigated different image analysis methods for automatically locating and counting the non-drivers including simple face detection and fine-tuned methods for image classification and a published object detection method. We also compared the image classification using convolutional neural network and vision transformer backbones. Our studies show the image classification method appears to work the best in terms of absolute performance, although we note the closed nature of our dataset and nature of the imagery makes the application somewhat niche and object detection methods also have advantages. We perform some analysis to support our conclusion.

Peruski, Ryan↗
DOE OSTI2023

Expanded genetic variation (SNP) and phenomics (image based) dataset for Populus trichocarpa

The image dataset consists of 11,791 images representing 1,219 genotypes of Populus trichocarpa undergoing in planta regeneration. Genotypes were imaged with a median of four weekly timepoints and a median of two replicates each. A representative and diverse subset of 249 images was annotated using the IDEAS annotation interface (ideas.eecs.oregonstate.edu) and these annotated images were used to train a deep semantic segmentation model (PSPNet), which was deployed for inference over the entire dataset. Annotated classes include specific stages of regeneration (callus and shoot) in addition to unregenerated plant material and background. Statistics of relative tissue area were extracted and used for downstream genetic association mapping in a genome-wide association study. The SNP dataset consists of over 40 million single-nucleotide polymorphisms across 1,323 wild accessions of Populus trichocarpa

09 BIOMASS FUELS↗
DOE OSTI2021

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test

Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.

Neurosciences & Neurology↗
DOE OSTI2022

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Simulating Reflected Light Exoplanet Spectra of the Promising Direct Imaging Target, υ Andromedae d, with a New, Fast Sampling Method Using the Planetary Spectrum Generator

Simulations of exoplanet albedo profiles are key to planning and interpreting future direct imaging observations. In this paper we demonstrate the use of the Planetary Spectrum Generator (PSG) to produce simulations of reflected light exoplanet spectra. We use PSG to examine multiple issues relevant to all models of directly imaged exoplanet spectra and to produce sample spectra of the bright, nearby exoplanet υ Andromedae d (υ And d), a potential direct imaging target for next-generation facilities. We introduce a new, fast, and accurate subsampling technique that enables calculations of disk-integrated spectra one order of magnitude faster than Chebyshev–Gauss sampling for moderate- to high-resolution sampling. Using this method and a first-principles-derived atmosphere for υ And d, we simulate phase-dependent spectra for a variety of different potential atmospheric configurations. The simulated spectra for υ And d include versions with different haze and cloud properties. Based on our combined analysis of this planet’s orbital parameters, phase- and illumination-appropriate model spectra, and realistic instrument noise parameters, we find that υ And d is a potentially favorable direct imaging and spectroscopy target for the Coronagraph Instrument (CGI) on the Nancy Grace Roman Space Telescope. When a noise model corresponding to the Roman CGI SPC spectroscopy mode is included, PSG predicts the time required to reach a signal-to-noise ratio of 10 of the simulated spectra in both the central wavelength bin of the Roman CGI SPC spectroscopy mode (R = 50 spectrum) and of the Band 1 HLC imaging mode is approximately 400 and less than 40 hr, respectively. We also discuss potential pathways to extricating information about the planet and its atmosphere with future observations and find that Roman observations may be able to bound the interior temperature of the planet.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

L-dwarf Detection from SDSS Images using Improved Faster R-CNN

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey (SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L-dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs, including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i – z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Detecting Anomalous Images in Astronomical Datasets

Abstract Environmental and instrumental conditions can cause anomalies in astronomical images, which can potentially bias all kinds of measurements if not excluded. Detection of the anomalous images is usually done by human eyes, which is slow and sometimes not accurate. This is an important issue in weak lensing studies, particularly in the era of large-scale galaxy surveys, in which image qualities are crucial for the success of galaxy shape measurements. In this work we present two automatic methods for detecting anomalous images in astronomical data sets. The anomalous features can be divided into two types: one is associated with the source images, and the other appears on the background. Our first method, called the entropy method, utilizes the randomness of the orientation distribution of the source shapes and the background gradients to quantify the likelihood of an exposure being anomalous. Our second method involves training a neural network (autoencoder) to detect anomalies. We evaluate the effectiveness of the entropy method on the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS) and Dark Energy Camera Legacy Survey (DECaLS DR3) data. In CFHTLenS, with 1171 exposures, the entropy method outperforms human inspection by detecting 12 of the 13 anomalous exposures found during human inspection and uncovering 10 new ones. In DECaLS DR3, with 17112 exposures, the entropy method detects a significant number of anomalous exposures while keeping a low false-positive rate. We find that although the neural network performs relatively well in detecting source anomalies, its current performance is not as good as the entropy method.

Astronomy & Astrophysics↗
DOE OSTI2025

Origin of the ring ellipticity in the black hole images of M87*

We investigate the origin of the elliptical ring structure observed in the images of the supermassive black hole M87*, aiming to disentangle contributions from gravitational, astrophysical, and imaging effects. Leveraging the enhanced capabilities of the Event Horizon Telescope (EHT)'s 2018 array, including improved (u,v)-coverage from the Greenland Telescope, we measured the ring's ellipticity using five independent imaging methods, obtaining a consistent average value of τ = 0.08−0.02+0.03 with a position angle of ξ = 50.1−7.6+6.2 degrees. To interpret this measurement, we compared it to general relativistic magnetohydrodynamic (GRMHD) simulations spanning a wide range of physical parameters including the thermal or nonthermal electron distribution function, spins, and ion-to-electron temperature ratios in both low- and high-density regions. We find no statistically significant correlation between spin and ellipticity in GRMHD images. Instead, we identify a correlation between ellipticity and the fraction of non-ring emission, particularly in nonthermal models and models with higher jet emission. These results indicate that the ellipticity measured from the M87* emission structure is consistent with that expected from simulations of turbulent accretion flows around black holes, where it is dominated by astrophysical effects rather than gravitational ones. Future high-resolution imaging, including space very long baseline interferometry and long-term monitoring, will be essential to isolate gravitational signatures from astrophysical effects.Key words: accretion, accretion disks / black hole physics / gravitation / galaxies: active** These authors have contributed equally to this work.*** NASA Hubble Fellowship Program, Einstein Fellow.† Deceased.

Dahale, Rohan [Granada U., Theor. Phys. Astrophys.↗
DOE OSTI2025

HAMscope: a snapshot Hyperspectral Autofluorescence Miniscope for real-time molecular imaging

We introduce HAMscope, a compact, snapshot hyperspectral autofluorescence miniscope that enables real-time, label-free molecular imaging in a wide range of biological systems. By integrating a thin polymer diffuser into a widefield miniscope, HAMscope spectrally encodes each frame and employs a probabilistic deep learning framework to reconstruct 30-channel hyperspectral stacks (452-703 nm) or directly infer molecular composition maps from single images. A scalable multi-pass U-Net architecture with transformer-based attention and per pixel uncertainty estimation enables high spatio-spectral fidelity (mean absolute error ∼0.0048) at video rates. While initially demonstrated in plant systems, including lignin, chlorophyll, and suberin imaging in intact poplar and cork tissues, the platform is readily adaptable to other applications such as neural activity mapping, metabolic profiling, and histopathology. We show that the system generalizes to out-of-distribution tissue types and supports direct molecular mapping without the need for spectral unmixing. HAMscope establishes a general framework for compact, uncertainty-aware spectral imaging that combines minimal optics with advanced deep learning, offering broad utility for real-time biochemical imaging across neuroscience, environmental monitoring, and biomedicine.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Method of processing an image

A non-transitory computer-readable medium encoded with a computer-readable program, which when executed by a processor, will cause a computer to execute a computational method, the computational method including collecting an image data, wherein the collecting the image data comprises collecting a first plurality of RGB images and a second plurality of hyperspectral images. The method further includes orthorectifying the image data to produce an RGB based orthophoto and a partially rectified hyperspectral orthophoto. The method further includes selecting tie features from each of the RGB based orthophoto and the partially rectified hyperspectral orthophoto. Lastly, the method includes registering the features of the partially rectified hyperspectral orthophoto into the tie features of the RGB based orthophoto.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

In-situ x-ray scatter imaging of battery electrodes

A method and apparatus for in-situ x-ray scatter imaging of battery electrodes. An apparatus includes an X-ray source, a grid, the grid comprising stainless steel wires with uniform spacing, and a cell, the X-ray source directing a beam of energy through the metal grid and components of the cell, the cell blurring a previously sharp projection of grid wires on an image detector. A method includes providing a Spatial Frequency Heterodyne Imaging system, providing a grid, providing a cell, generating X-rays from the Spatial Frequency Heterodyne Imaging system that pass through components of the cell and the grid, and detecting a scatter image from the X-rays.

Rose-Petruck, Christoph↗
DOE OSTI2021

Image plate calibration system

A calibration system for an image plate used to detect high-energy particles produced by a radioisotope includes a casing and an image plate holder disposed at least partially inside the casing. The image plate is retained by the image plate holder inside the casing. A calibration source generates energy inside the casing to prepare the image plate for detection.

Immel, Darryl R.↗
DOE OSTI2021

Imaging system and method for enhanced visualization of near surface vascular structures

A sub-dermal structure visualization system and method is disclosed. The system may use an illumination module including: a near-infrared (NIR) light source adapted to substantially uniformly illuminate an imaged area including sub-dermal regions thereof with NIR light; and a first optical system including at least one optical element for controlling at least one of spectral and polarization properties of the NIR light prior to illuminating the imaged area. An imaging module rejects unwanted signals from an imaged area while passing desired optical signals that are to be received by an image acquisition module. The desired optical signals can comprise a vein visualization signal to assist in visualizing a vascular structure below a skin layer of a patient.

Demos, Stavros G.↗
DOE OSTI2021

Method and apparatus to obtain limited angle tomographic images from stationary gamma cameras

A nuclear imaging system and method for performing three-dimensional imaging of anatomical structures. The system and method includes two or more gamma ray detectors each used in combination with a variable-slant hole collimator. The detectors are positioned in close proximity to, or in contact with, the structure being imaged. The detectors remain in a stationary position during the data collection process. An imaging or reconstruction method is then used to reconstruct a three-dimensional image from the data derived from the detectors.

Kross, Brian J.↗
DOE OSTI2022

Handheld multi-sensor biometric imaging device and processing pipeline

A handheld biometric imaging device having an array of cameras configured to simultaneously capture face, iris and fingerprint biometrics of a subject. The device includes a plurality of visible-light cameras and a plurality of infrared-light cameras capable of being triggered simultaneously to obtain a plurality of images from which a 3D image of the light field can be constructed. The device includes a plurality of visible-light illuminators and a plurality of infrared-light illuminators that allow images of a subject to be captured under different lighting profiles. The device may include an onboard control system that is capable of reconstructing a face region, an iris region and a fingerprint region from the 3D light-field image, and then extract a corresponding face template, an iris template, and a fingerprint template from the respective reconstructed regions. The extracted face, iris, and fingerprint templates may be used for enrollment and/or comparison against a biometric-template database associated with one or more watchlists.

Bolme, David S.↗
DOE OSTI2023

System and method for identifying objects of interest in images based on likelihood map decluttering

An automatic threat recognition system and method is disclosed for scanning the x-ray CT image of an article to identify the objects of interest (OOIs) contained within the article, which are otherwise not always quickly apparent or discernable to an individual. The system uses a computer to receive information from two-dimensional (2D) image slices from a reconstructed computed tomography (CT) scan image and to produce a plurality of voxels for each slice of the 2D image. The computer analyzes the voxels to create a likelihood map (LM) representing likelihoods that voxels making up the CT image are associated with a material of interest (MOI). The computer further analyzes the LM to construct neighborhoods of voxels within the LM, and classifies each voxel neighborhood based on its features, thereby decluttering the LM to facilitate the process of connecting voxels of a like MOI together to form segments. The computer classifies each candidate segment based on its features, thereby identifying those segments that correspond to objects of interest.

Paglieroni, David W.↗
DOE OSTI2023

Coded-mask-based X-ray phase-contrast and dark-field imaging

Phase contrast and dark-field X-ray imaging enable imaging of objects that absorb or reflect very little X-ray light. Disclosed is a method and systems for performing coded-mask-based multi-contrast imaging (CMMI). The method includes providing radiation to a coded mask that has a known phase and absorption profile according to a pre-determined pattern. The radiation is then impingent upon a sample, and the radiation is detected to perform phase-reconstruction and image processing. The method and associated systems allow for the use of maximum-likelihood and machine learning methods for reconstruction images of the sample from the detected radiation.

Shi, Xianbo↗
DOE OSTI2023

Model-based image change quantification

A system is provided for identifying and quantizing a change in a scene having objects based on comparison of features representing a target object derived from a first image and a second image that includes the target object. The system segments, based on a scene model of the scene, a second image that includes the target object to identify a second area representing the target object within the second image. The system extracts, from the second image, second features for the target object based on the second area. The system determines a second effectiveness of the second features indicating effectiveness of the second features in representing the target object. The system detects a change based on a difference between first features and the second features factoring in a first effectiveness and the second effectiveness.

Roberts, Randy S.↗