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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Addressing Issues with Working Memory in Video Object Segmentation

Contemporary state-of-the-art video object segmentation (VOS) models compare incoming unannotated images to a history of image-mask relations via affinity or cross-attention to predict object masks. We refer to the internal memory state of the initial image-mask pair and past image-masks as a working memory buffer. While the current state of the art models perform very well on clean video data, their reliance on a working memory of previous frames leaves room for error. Affinity-based algorithms include the inductive bias that there is temporal continuity between consecutive frames. To account for inconsistent camera views of the desired object, working memory models need an algorithmic modification that regulates the memory updates and avoid writing irrelevant frames into working memory. A simple algorithmic change is proposed that can be applied to any existing working memory-based VOS model to improve performance on inconsistent views, such as sudden camera cuts, frame interjections, and extreme context changes. The resulting model performances show significant improvement on video data with these frame interjections over the same model without the algorithmic addition. Our contribution is a simple decision function that determines whether working memory should be updated based on the detection of sudden, extreme changes and the assumption that the object is no longer in frame. By implementing algorithmic changes, such as this, we can increase the real-world applicability of current VOS models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Investigation of Three-Dimensional Microstructure of Tricalcium Silicate (C 3 S) by Electron Microscopy

A serial block-face scanning electron microscopy (SBFSEM) system, composed of a scanning electron microscope (SEM) and an ultra-microtome installed within the SEM vacuum chamber, has been used to characterize the three-dimensional (3D) microstructure of tricalcium silicate (C 3 S) grains embedded in epoxy resin. A selection of C 3 S grains were segmented and rendered with 3D-image processing software, which allowed the C 3 S grains to be clearly visualized and enabled statistically quantitative analysis. The results show that about 5% of the C 3 S grains have volumes larger than 1 μm 3 and the average volume of the grains is 25 μm 3 . Pores can also be clearly seen in the biggest C 3 S grain, the volume of which is 3.6 × 10 4 μm 3 , and the mean volume and total volume of all the pores within this grain are 4.8 μm 3 and 3.0 × 10 3 μm 3 , respectively. This work provides a new approach for the characterization of the 3D spatial structure of raw C 3 S materials, and the resulting 3D structure of the raw C 3 S is important for further systematic research on the relationships between the spatial microstructure and the hydration kinetics of C 3 S and other cement minerals.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Red Wine Fermentation Alters Grape Seed Morphology and Internal Porosity

During wine fermentation, grape berry components, including seeds, undergo extensive physical and chemical changes that result in the release of flavonoids, such as tannins, from seeds into wine. Understanding changes in seed morphology during fermentation is crucial for aiding the development of accurate prediction models for flavonoid extraction during winemaking, which enhances fermentation management and ensures consistency in wines from year to year. High-resolution x-ray microcomputed tomography (x-ray μCT) was used to investigate the effect of red wine fermentation on changes in grape seed morphology. Using a PyTorch-based implementation of a fully convolutional network with a Resnet-101 backbone for semantic segmentation of x-ray μCT images, we quantified extensive alteration to grape seed structure during fermentation. Image analyses revealed the development of a pore network breaking apart the seed endosperm by the end of fermentation, leading to an increase in surface area. Fermentation significantly altered grape seed morphology. Such alterations could enable transport of seed flavonoids from inside the endosperm and integument to outside the seed. Further research on the physical processes occurring in seeds during wine fermentation is necessary to build better physiochemical models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

PlantCV v2: Image analysis software for high-throughput plant phenotyping

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here in this paper we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

ANN↗
NASA NTRS1990

Optical neural network system for pose determination of spinning satellites

An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

Lee, Andrew↗
NASA NTRS2003

Validating NASA's Airborne Multikilohertz Microlaser Altimeter (Microaltimeter) by Direct Comparison of Data Taken Over Ocean City, Maryland Against an Existing Digital Elevation Model

NASA's Airborne Multikilohertz Microlaser Altimeter (Microaltimeter) is a scanning, photon-counting laser altimeter, which uses a low energy (less than 10 microJuoles), high repetition rate (approximately 10 kHz) laser, transmitting at 532 nm. A 14 cm diameter telescope images the ground return onto a segmented anode photomultiplier, which provides up to 16 range returns for each fire. Multiple engineering flights were made during 2001 and 2002 over the Maryland and Virginia coastal area, all during daylight hours. Post-processing of the data to geolocate the laser footprint and determine the terrain height requires post- detection Poisson filtering techniques to extract the actual ground returns from the noise. Validation of the instrument's ability to produce accurate terrain heights will be accomplished by direct comparison of data taken over Ocean City, Maryland with a Digital Elevation Model (DEM) of the region produced at Ohio State University (OSU) from other laser altimeter and photographic sources. The techniques employed to produce terrain heights from the Microaltimeter ranges will be shown, along with some preliminary comparisons with the OSU DEM.

Abel, Peter↗
NASA NTRS2003

Toroidal Tank Development for Upper-stages

The advantages, development, and fabrication of toroidal propellant tanks are profiled in this viewgraph presentation. Several images are included of independent research and development (IR&D) of toroidal propellant tanks at Marshall Space Flight Center (MSFC). Other images in the presentation give a brief overview of Thiokol conformal tank technology development. The presentation describes Thiokol's approach to continuous composite toroidal tank fabrication in detail. Images are shown of continuous and segmented toroidal tanks fabricated by Thiokol.

DeLay, Tom↗
NASA NTRS2004

Automated Detection of Solar Loops by the Oriented Connectivity Method

An automated technique to segment solar coronal loops from intensity images of the Sun s corona is introduced. It exploits physical characteristics of the solar magnetic field to enable robust extraction from noisy images. The technique is a constructive curve detection approach, constrained by collections of estimates of the magnetic fields orientation. Its effectiveness is evaluated through experiments on synthetic and real coronal images.

Lee, Jong Kwan↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
NASA NTRS1988

Real-time sensing of optical alignment

The Large Deployable Reflector and other future segmented optical systems may require autonomous, real-time alignment of their optical surfaces. Researchers have developed gratings located directly on a mirror surface to provide interferometric sensing of the location and figure of the mirror. The grating diffracts a small portion of the incident beam to a diffractive focus where the designed diagnostics can be performed. Mirrors with diffraction gratings were fabricated in two separate ways. The formation of a holographic grating over the entire surface of a mirror, thereby forming a Zone Plate Mirror (ZPM) is described. Researchers have also used computer-generated hologram (CGH) patches for alignment and figure sensing of mirrors. When appropriately illuminated, a grid of patches spread over a mirror segment will yield a grid of point images at a wavefront sensor, with the relative location of the points providing information on the figure and location of the mirror. A particular advantage of using the CGH approach is that the holographic patches can be computed, fabricated, and replicated on a mirror segment in a mass production 1-g clean room environment.

Stier, Mark T.↗
NASA NTRS2002

Segmented mirror coarse phasing with white light interferometry: modeling and experiment on NGST's wavefront control testbed

A method of coarse phasing segmented mirrors using white light interferometry (WLI) has been developed for Next Generation Space Telescope (NGST) wavefront sensing and control. Using the broadband point spread function (PSF) of the segmented mirrors taken during a segment piston scan, the WLI can accurately detect small residual piston errors. WLI does not rely on any extra optics and uses only the final imaging camera. With its high sensitivity to small segment piston error WLI can be used as a complementary phasing algorithm to the dispersed fringe sensor (DFS) for NGST. The paper will present the results from modeling and experiment on the NGST's Wavefront Control Testbed (WCT).

Space↗
NASA NTRS2003

Classifying Urban Land Covers Using Local Indices of Spatial Complexity

The skewed statistical distributions of land cover types in complex, heterogeneous urban areas limits the effectiveness of traditional spectrally based maximum-likelihood classifiers. This work examines the utility of fractal dimension and Moran's I index of spatial autocorrelation in segmenting high-resolution panchromatic and lower-resolution multispectral imagery. Tools available in the Image Characterization and Modeling System (ICAMS) were used to analyze multi-temporal and multi-platform imagery of Atlanta, Georgia. In this example, land cover change trajectories from forest or grassland to built up land covers lead to decreased spatial autocorrelation. In lower resolution imagery such as Landsat MSS, the complex details of forested land covers and urbanized areas are smoothed, and texture-based change detection is less effective. Although segmentation of panchromatic images is possible using fractal dimension or Moran's I, widely differing land covers often yield similar values of these indices. Better results are obtained when a surface of local fractal dimension or spatial autocorrelation is combined as an additional layer in a supervised maximum-likelihood multispectral classification.

Arumugam, Mahesh↗
DOE OSTI2013

Needle segmentation using 3D Hough transform in 3D TRUS guided prostate transperineal therapy

Purpose: Prostate adenocarcinoma is the most common noncutaneous malignancy in American men with over 200 000 new cases diagnosed each year. Prostate interventional therapy, such as cryotherapy and brachytherapy, is an effective treatment for prostate cancer. Its success relies on the correct needle implant position. This paper proposes a robust and efficient needle segmentation method, which acts as an aid to localize the needle in three-dimensional (3D) transrectal ultrasound (TRUS) guided prostate therapy. Methods: The procedure of locating the needle in a 3D TRUS image is a three-step process. First, the original 3D ultrasound image containing a needle is cropped; the cropped image is then converted to a binary format based on its histogram. Second, a 3D Hough transform based needle segmentation method is applied to the 3D binary image in order to locate the needle axis. The position of the needle endpoint is finally determined by an optimal threshold based analysis of the intensity probability distribution. The overall efficiency is improved through implementing a coarse-fine searching strategy. The proposed method was validated in tissue-mimicking agar phantoms, chicken breast phantoms, and 3D TRUS patient images from prostate brachytherapy and cryotherapy procedures by comparison to the manual segmentation. The robustness of the proposed approach was tested by means of varying parameters such as needle insertion angle, needle insertion length, binarization threshold level, and cropping size. Results: The validation results indicate that the proposed Hough transform based method is accurate and robust, with an achieved endpoint localization accuracy of 0.5 mm for agar phantom images, 0.7 mm for chicken breast phantom images, and 1 mm for in vivo patient cryotherapy and brachytherapy images. The mean execution time of needle segmentation algorithm was 2 s for a 3D TRUS image with size of 264 Multiplication-Sign 376 Multiplication-Sign 630 voxels. Conclusions: The proposed needle segmentation algorithm is accurate, robust, and suitable for 3D TRUS guided prostate transperineal therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A Co-Registered In-Situ and Ex-Situ Dataset from a Laser Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-10)

This release contains a co-registered in-situ and ex-situ Peregrine dataset from a single Concept Laser M2 Laser Powder Bed Fusion (L-PBF) stainless steel 316L build. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise visible-light in-situ imaging data, the laser scan paths and parameters, in-situ temporal sensor data, X-Ray Computed Tomography (X-CT) scans, pycnometry and tensile test results, etched micrographs from selected locations, and the target part geometries. Additionally, anomaly detections produced by a modified Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C{sub T}) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C{sub T} yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C{sub T} were conducted. The results showed that C{sub T} was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Automated Tracing and Segmentation Tool for Migrating Neurons in 4D Confocal Imagery

Accurate tracing and segmentation of subcellular components of migrating neurons in confocal image sequences are prerequisite steps in many neurobiology studies to understand the biological machinery behind the movement of developing neurons. In this paper, we present an automated tracking, tracing, and segmentation tool for soma, leading, and trailing process of migrating neurons in time-lapse image stacks acquired with a confocal fluorescence microscope. In our approach, we first localize each neuron in the maximum intensity projection of the first frame using manual labeling of the soma and end points of the leading and trailing process. By using each soma position at the first frame, we automatically track the somas in rest of the frames. Then, leading and trailing process are traced in each frame from the soma center to the labeled end tip of the process by using fast marching algorithm. Finally, the soma, leading and trailing processes of each neuron are segmented by using the soma center and traces as seed points, and their boundaries are separated from each other. Based on qualitative results, we demonstrate the capability to automatically track, trace, and segment the soma, leading, and trailing processes of a migrating neuron with minimal user input.

Karakaya, Mahmut↗