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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

On the problem of embedding picture elements in regions

A few new algorithms for region growing in pictures were developed. These algorithms are a step toward finding a satisfactory solution to the image segmentation problem, and in-depth understanding of the problems of nonsemantic image segmentation. The algorithms utilize a sequential decision approach for region boundaries detection. The sequential decisions are supported by a stochastic algorithm that maintains local statistics of the region near the boundaries as the region grows. A few illustrations of the algorithm's performance are included.

Yakimovsky, Y.↗
DOE OSTI2022

Evaluation of a positron-emission-tomography-based SiPM readout for compact segmented neutron imagers

Gamma-ray emission from special nuclear material (SNM) is relatively easy to shield from detection using modest amounts of high-Z material. In contrast, fast-neutrons are much more penetrating and can escape relatively thick high-Z shielding without losing significant energy. Furthermore, fast neutrons provide a clear and unambiguous signature of the presence of SNM with few competing natural background sources. The challenge of detecting fast neutrons is twofold. First, the neutron flux from SNM are only a fraction of the corresponding gamma-ray flux. Second, fast neutrons can be difficult to differentiate from gamma rays. The ability to discriminate gamma rays from neutrons, combined with a neutron imaging capability, can in some situations enable the localization of SNM neutron sources distinct from background. With the developments of pulse-shape-sensitive plastic scintillators that offer excellent gamma-ray/neutron discrimination, and arrays of silicon photomultipliers combined with highly scalable and fast positron-emission-tomography (PET) multi-channel readout systems, field-deployable neutron imagers suitable for SNM detection might now be within reach. In this paper, we present a characterization of the performance of a recently available commercial PET-scanner readout, including its sensitivity to pulse-shape differences between fast neutrons and gamma rays, energy and timing resolution. Here, we find that, while the pulse-shape discrimination is achievable with stilbene, further improvement of the readout is required to achieve it with the PSD-capable available plastic scintillators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2008

RHSEG and Subdue: Background and Preliminary Approach for Combining these Technologies for Enhanced Image Data Analysis, Mining and Knowledge Discovery

Under a project recently selected for funding by NASA's Science Mission Directorate under the Applied Information Systems Research (AISR) program, Tilton and Cook will design and implement the integration of the Subdue graph based knowledge discovery system, developed at the University of Texas Arlington and Washington State University, with image segmentation hierarchies produced by the RHSEG software, developed at NASA GSFC, and perform pilot demonstration studies of data analysis, mining and knowledge discovery on NASA data. Subdue represents a method for discovering substructures in structural databases. Subdue is devised for general-purpose automated discovery, concept learning, and hierarchical clustering, with or without domain knowledge. Subdue was developed by Cook and her colleague, Lawrence B. Holder. For Subdue to be effective in finding patterns in imagery data, the data must be abstracted up from the pixel domain. An appropriate abstraction of imagery data is a segmentation hierarchy: a set of several segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. The RHSEG program, a recursive approximation to a Hierarchical Segmentation approach (HSEG), can produce segmentation hierarchies quickly and effectively for a wide variety of images. RHSEG and HSEG were developed at NASA GSFC by Tilton. In this presentation we provide background on the RHSEG and Subdue technologies and present a preliminary analysis on how RHSEG and Subdue may be combined to enhance image data analysis, mining and knowledge discovery.

Tilton, James C.↗
DOE OSTI2023

Quantifying Pore Morphology in Spray-Formed Tantalum Using X-ray Micro-computed Tomography

In order to establish quantitative process–structure–property relationships in thermal spray coatings, a robust framework for defining (micro)structural characteristics is needed. Here, we present a quantitative characterization of the three-dimensional morphology of porosity in spray-formed tantalum samples based on high-resolution X-ray micro-computed tomography. Using synchrotron facilities, we acquired dozens of high-resolution scans, enabling a statistically meaningful comparison across multiple samples, different regions within samples, and spray processes. We quantify the spatial distribution, size, and topology of porous inclusions, with a significant focus on variability across samples and different spray processes (plasma and cold sprayed), as well as sensitivity to image segmentation and resolution. Based on a typical segmentation, we report porosities ranging from 0.9 to 1.7 pct for all samples tested, with significant sensitivity due to image segmentation resulting in estimates as low as 0.6 pct and as high as 4.8 pct. For the complex pore space morphology observed in these materials, we argue that a conventional analysis based on identifying individual pores is not well suited, and propose an alternative approach based on morphological metrics with a rich history in porous media literature, such as spatial correlations, local pore thickness, and scale-dependent sub-sampling. Spatial correlations indicate anisotropic splat structures, but only mildly anisotropic pores. Various measures of pore size show a wide distribution of sizes, ranging from sub-micron to 10-micron length scales. Scale-dependent variations in porosity suggest that representative volumes of several hundred microns are required for convergence of morphological metrics, with larger volumes for cold-spray materials. This work provides a robust quantitative basis for describing three-dimensional pore structure in thermal spray coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1999

Segmented Mirror Image Degradation Due to Surface Dust, Alignment and Figure

In 1996 an algorithm was developed to include the effects of surface roughness in the calculation of the point spread function of a telescope mirror. This algorithm has been extended to include the effects of alignment errors and figure errors for the individual elements, and an overall contamination by surface dust. The final algorithm builds an array for a guard-banded pupil function of a mirror that may or may not have a central hole, a central reflecting segment, or an outer ring of segments. The central hole, central reflecting segment, and outer ring may be circular or polygonal, and the outer segments may have trimmed comers. The modeled point spread functions show that x-tilt and y-tilt, or the corresponding R-tilt and theta-tilt for a segment in an outer ring, is readily apparent for maximum wavefront errors of 0.1 lambda. A similar sized piston error is also apparent, but integral wavelength piston errors are not. Severe piston error introduces a focus error of the opposite sign, so piston could be adjusted to compensate for segments with varying focal lengths. Dust affects the image principally by decreasing the Strehl ratio, or peak intensity of the image. For an eight-meter telescope a 25% coverage by dust produced a scattered light intensity of 10(exp -9) of the peak intensity, a level well below detectability.

Schreur, Julian J.↗
NASA NTRS1991

Analysis of objects in binary images

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

Leonard, Desiree M.↗
DOE OSTI2021

TomoEncoders: 3D Autoencoders for feature extraction in X-ray tomography

Real-time steering of time-resolved or in-situ X-ray tomography requires capturing changes in morphological descriptors in a sample (e.g., porosity, particle size, and crack width) during continuous data acquisition. Image segmentation (2D or3D) followed by quantitative measurement is the conventional method for tracking changes in these descriptors with respect to a previous time-step or a 3D search in a volume. However, image segmentation is expensive. As a faster and unsupervised alternative, a feature-extraction approach using a convolutional autoencoders was developed, where the latent space of the encoder responds to relative changes in morphology with-out prior knowledge of the morphological descriptors.

TEKAWADE, ANIKET↗
NASA NTRSDate not supplied

Satellite-Derived Imagery and Transfer Learning: A Novel Technique for Land Cover Classification

Land cover classification is a continuing research topic due to its relevance to land use and land cover changes from impacts such as climate change, agriculture, urbanization, and hazardous weather. Simple access to frequently changing land cover classifications could provide knowledge and decision support to various researchers and agencies across the globe for each of above-mentioned and related influences. This research aims to provide a novel technique for land cover classification of remote sensing imagery; harnessing Artificial Neural Networks and transfer learning (TL). Knowledge sharing techniques within machine learning are typically utilized when training datasets are sparse or transitions between data modalities is required. In this case, two data modalities, multi and hyperspectral data, are considered for knowledge transfer. The large number of continuous spectral bands available from hyperspectral sensors typically provide increased sophisticated land classification capability compared to more traditional multispectral imagery with limited discrete spectral bands. However, large-scale, frequent access to hyperspectral imagery is relatively limited. The proposed classification technique would therefore prove useful in regions in which hyperspectral data are not readily available for classification but multispectral data are. Image segmentation models are trained on each multi and hyperspectral datasets. Knowledge sharing techniques are then applied to each model to understand what knowledge, if any, is gained when moving between the data modalities. The datasets utilized for training and testing a U-Net model include the European Space Agency’s multispectral imager Sentinel-2 and the hyperspectral German Aerospace Center’s Earth Sensing Imaging Spectrometer (DESIS). Initially, only two classes, land and water, are classified for simplicity. However, more complex classes can be added if knowledge sharing is successful. The workflow for image classification through supervised image segmentation with a U-Net model will be discussed along with metrics calculated before and after TL for both Sentinel-2 and DESIS data are applied. Additionally, future steps to advance the sophistication of this technique as well as other applicable methodologies will be explored.

Emily Foshee↗
NASA NTRS2011

Object-based Mapping of the Circumpolar Taiga-Tundra Ecotone with MODIS Tree Cover

The circumpolar taiga-tundra ecotone was delineated using an image segmentation based mapping approach with multi-annual MODIS Vegetation Continuous Fields (VCF) tree cover data. Circumpolar tree canopy cover (TCC) throughout the ecotone was derived by averaging MODIS VCF data from 2000 - 2005 and adjusting the averaged values using linear equations relating MODIS TCC to Quickbird-derived tree cover estimates. The adjustment helped mitigate VCF's overestimation of tree cover in lightly forested regions. An image segmentation grouped pixels representing similar tree cover into polygonal features (objects) that form the map of the transition zone. Eachfeature represents an area much larger than the 500m MODIS pixel to characterize thepatterns of sparse forest patches on a regional scale. Comparisons of the adjusted average tree cover data were made with (1) two existing tree line definitions aggregated for each 1deg longitudinal interval in North America and Eurasia and (2) Landsat-derived Canadianproportion of forest cover for Canada. The adjusted TCC from MODIS VCF shows, on average, greater than 12% TCC for all but one regional zone at the intersection with independently delineated tree lines. Adjusted values track closely with Canadian proportion of forest cover data in areas of low tree cover. Those polygons near the boreal/tundra interface with either (1) mean adjusted TCC values between 5-20% , or (2) mean adjusted TCC values <5% but with a standard deviation > 5% were used to identify the ecotone.

Ransom, Kenneth J.↗
NASA NTRS2011

Object-Based Mapping of the Circumpolar Taiga-Tundra Ecotone with MODIS Tree Cover

The circumpolar taiga tundra ecotone was delineated using an image-segmentation-based mapping approach with multi-annual MODIS Vegetation Continuous Fields (VCF) tree cover data. Circumpolar tree canopy cover (TCC) throughout the ecotone was derived by averaging MODIS VCF data from 2000 to 2005 and adjusting the averaged values using linear equations relating MODIS TCC to Quickbird-derived tree cover estimates. The adjustment helped mitigate VCF's overestimation of tree cover in lightly forested regions. An image segmentation procedure was used to group pixels representing similar tree cover into polygonal features (segmentation objects) that form the map of the transition zone. Each polygon represents an area much larger than the 500 m MODIS pixel and characterizes the patterns of sparse forest patches on a regional scale. Those polygons near the boreal/tundra interface with either (1) mean adjusted TCC values from5 to 20%, or (2) mean adjusted TCC values greater than 5% but with a standard deviation less than 5% were used to identify the ecotone. Comparisons of the adjusted average tree cover data were made with (1) two existing tree line definitions aggregated for each 1 degree longitudinal interval in North America and Eurasia, (2) Landsat-derived Canadian proportion of forest cover for Canada, and (3) with canopy cover estimates extracted from airborne profiling lidar data that transected 1238 of the TCC polygons. The adjusted TCC from MODIS VCF shows, on average, less than 12% TCC for all but one regional zone at the intersection with independently delineated tree lines. Adjusted values track closely with Canadian proportion of forest cover data in areas of low tree cover. A comparison of the 1238 TCC polygons with profiling lidar measurements yielded an overall accuracy of 67.7%.

Ranson, K. J.↗
DOE OSTI2021

Reconstruction of concrete microstructure using complementarity of X-ray and neutron tomography

The concrete microstructure was successfully reconstructed using the complementarity of X-ray and neutron computed tomography (CT). Neither tomogram alone was found to be suitable to properly describe the microstructure of concrete under this study. However, by merging the information revealed by the two modalities, and using image segmentation, noise reduction, and image registration techniques we reconstruct the concrete microstructure. Void, aggregate, and cement paste phases are successfully captured down to the images' spatial resolution, even though the aggregate consists of multiple minerals. The coarse-aggregate volume fraction of the reconstructed microstructure was similar to that of the mixing proportions. Furthermore, image-based finite element analysis is performed to demonstrate the effects of microstructure on stress concentration and strain localization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data supporting the manuscript "Nanometer Scale Imaging to Develop Quantitative Descriptors of Bipolar Membrane Junction Structure"

This dataset contains atomic force microscopy images associated with the manuscript "Nanometer Scale Imaging to Develop Quantitative Descriptors of Bipolar Membrane Junction Structure" by Maria Kelly, Emily R. Dunn, Ellis A. Spickermann, Josephine N. Gruber, César A. Lasalde-Ramírez, P. N. Romero Zavala, Éowyn Lucas, Ankur Gupta, Harry A. Atwater, and Wilson A. Smith. The dataset contains both raw images as well as segmented images produced by the image processing workflow described in the manuscript. A readme file and meta data file are included to provide additional details regarding the sample identity, image acquisition parameters, and file naming scheme.

36 MATERIALS SCIENCE↗
NASA NTRS1991

A tool for interactive exploration of a hierarchical segmentation

Hierarchical segmentation is discussed as a form of region growing in which the sequence of merges is controlled by a 'best merge first' principle, and a record of the region merging sequence is often retained for later analysis. In the author's massively parallel implementation of hierarchical segmentation, which he calls iterative parallel region growing (IPRG), a set of directional edge maps are used to store the region merging sequence information. An iteractive tool is described that allows an analyst to fully explore a hierarchical segmentation, potentially producing an image segmentation that is a combination of segmentations produced at several different iterations of the IPRG algorithm. A potential method for automatically producing such segmentations is also discussed.

Tilton, James C.↗
NASA NTRS2014

Incorporating Edge Information into Best Merge Region-Growing Segmentation

We have previously developed a best merge region-growing approach that integrates nonadjacent region object aggregation with the neighboring region merge process usually employed in region growing segmentation approaches. This approach has been named HSeg, because it provides a hierarchical set of image segmentation results. Up to this point, HSeg considered only global region feature information in the region growing decision process. We present here three new versions of HSeg that include local edge information into the region growing decision process at different levels of rigor. We then compare the effectiveness and processing times of these new versions HSeg with each other and with the original version of HSeg.

Image segmentation↗
NASA NTRS2021

Contextual Segmentation of Fire Spotting Regions Through Satellite-Augmented Autonomous Modular Sensor Image

Globally, forest fires remain a significant threat to human and environmental wellbeing. Towards mitigating the impacts of forest fires, it is critical that accurate and updated information regarding not only the fire line, but also nearby human settlements, vegetation, and water sources is reported quickly to emergency services. However, while existing UAS-based fire detection methods are effective, they largely do not report the contextual environmental information necessary to best serve nearby communities in disaster response. Additionally, modern advancements in deep learning offer new approaches for image segmentation which may improve classification accuracy beyond current pixel-wise indices. In this work, we benchmark the performance of these modern segmentation techniques in locating both fire lines and environmental features in historical Autonomous Modular Sensor imagery. Furthermore, we augment these outputs with satellite imagery segmentation towards developing a robust contextual mapping tool for rapid emergency fire response and decision making.

Nikhil Behari↗
NASA NTRS1994

An adaptive technique to maximize lossless image data compression of satellite images

Data compression will pay an increasingly important role in the storage and transmission of image data within NASA science programs as the Earth Observing System comes into operation. It is important that the science data be preserved at the fidelity the instrument and the satellite communication systems were designed to produce. Lossless compression must therefore be applied, at least, to archive the processed instrument data. In this paper, we present an analysis of the performance of lossless compression techniques and develop an adaptive approach which applied image remapping, feature-based image segmentation to determine regions of similar entropy and high-order arithmetic coding to obtain significant improvements over the use of conventional compression techniques alone. Image remapping is used to transform the original image into a lower entropy state. Several techniques were tested on satellite images including differential pulse code modulation, bi-linear interpolation, and block-based linear predictive coding. The results of these experiments are discussed and trade-offs between computation requirements and entropy reductions are used to identify the optimum approach for a variety of satellite images. Further entropy reduction can be achieved by segmenting the image based on local entropy properties then applying a coding technique which maximizes compression for the region. Experimental results are presented showing the effect of different coding techniques for regions of different entropy. A rule-base is developed through which the technique giving the best compression is selected. The paper concludes that maximum compression can be achieved cost effectively and at acceptable performance rates with a combination of techniques which are selected based on image contextual information.

Stewart, Robert J.↗