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Results for “quantitative image analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

ERIM progress report on use of ERTS-1 data: Summary report of work on ten tasks

The author has identified the following significant results. Several of the tasks have produced significant results which are summarized: (1) Absolute water depth can be calculated from a ratio of signals from bands MSS 4 and MSS 5. (2) A 13 category terrain feature classification map of Yellowstone National Park has been produced using supervised pattern recognition techniques. (3) ERTS-1 data has been shown to provide a detection and monitoring capability for a number of water quality problems associated with off-shore ocean dumping sites and inland lakes. (4) A corrected ratio of bands MSS-5 and MSS-7 signals has been formed. (5) A concise format has been devised for storing the ratio signatures of geologic rock and mineral materials determined from laboratory reflectance spectra. (6) Results of work in information extraction demonstrate: signal variability exists among ERTS-1 detectors in any one spectral band that will impact users doing quantitative analysis on successive ERTS-1 images; a newly developed computer-aided procedure for correlating ERTS-1 pixels to ground features; the strong influence of atmospheric effects in ERTS-1 data; and area estimation accuracies are better using the ERIM proportion estimation algorithm than for conventional recognition techniques.

Thomson, F. J.↗
NASA NTRS1996

Spectral Analysis and Experimental Modeling of Ice Accretion Roughness

A self-consistent scheme for relating wind tunnel ice accretion roughness to the resulting enhancement of heat transfer is described. First, a spectral technique of quantitative analysis of early ice roughness images is reviewed. The image processing scheme uses a spectral estimation technique (SET) which extracts physically descriptive parameters by comparing scan lines from the experimentally-obtained accretion images to a prescribed test function. Analysis using this technique for both streamwise and spanwise directions of data from the NASA Lewis Icing Research Tunnel (IRT) are presented. An experimental technique is then presented for constructing physical roughness models suitable for wind tunnel testing that match the SET parameters extracted from the IRT images. The icing castings and modeled roughness are tested for enhancement of boundary layer heat transfer using infrared techniques in a "dry" wind tunnel.

Orr, D. J.↗
NASA NTRS1978

Study of solar cell welds

The thermal imaging technique was evaluated for its capabilities in the nondestructive evaluation of solar cell welds. The temperature and spatial resolution of state of the art instrumentation was sufficient for both qualitative and quantitative determination of the quality of solar cell welds. The addition of color digitized thermography enhanced the aspects of the thermographic display and allowed easily computerized testing procedures. For automated testing systems an accurate correlation of weld quality with temperature profiles of the welds needs to be performed. In comparison, the holographic technique was complementary with the thermal imaging technique, except that the holographic analysis appeared to be more quantitative at the present time. However, the thermal imaging approach is much more versatile in overall capabilities.

Workman, G. L.↗
NASA NTRS2005

Compact FUV camera concept for Space Weather applications

Far ultraviolet (FUV) images of Earth from space have proven invaluable in revealing contextual phenomena associated with space weather in the high latitude auroral regions and, more recently with TIMED and IMAGE in the mid and equatorial regions. Images of this nature can be used to investigate compelling questions associated with the interaction of the ionosphere/mesosphere-magnetosphere-solar wind. Three such questions are: 1. What are the critical time and spatial scales that determine the coupling between the magnetosphere and ionosphere? 2. What are the sources and characteristics of ionospheric variability? 3. How does the configuration of the magnetosphere change as a function of internal boundary conditions established by the mesosphere/ionosphere and the external driving conditions of the solar wind? Observations that lead to quantitative analyses are required to significantly advance the state of knowledge with regard to the affects of space weather and the interaction between and within these regions of Geospace. Current available image data sets are sufficient for qualitative analysis and morphological investigations, and while quantitative analyses are possible, they are difficult and limited to few events at best. In order to qualitatively access the time, spatial, and causal phenomena on global scales, simultaneous images of various FUV emissions with a combination of better spatial, temporal and spectral resolution and sensitivity than currently available is required. We present an instrument concept that is being developed to improve the spatial, temporal and spectral resolution and sensitivity needed to perform the quantitative analysis that enable significant advancement in our understanding of the impact of space weather on Geospace. The approach is to use the self-filtering concept that combines the imaging and filtering functions and thus reduces the size of the 4-mirror off-axis optical system. The optical and filter design will de described and results of preliminary breadboard tests will be provided.

Spann, James↗
DOE OSTI2023

A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging

Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7°C temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1977

Analysis of photographic X-ray images

Some techniques used to extract quantitative data from the information contained in photographic images produced by grazing incidence soft X-ray optical systems are described. The discussion is focussed on the analysis of the data returned by the S-054 X-Ray Spectrographic Telescope Experiment on Skylab. The parameters of the instrument and the procedures used for its calibration are described. The technique used to convert photographic density to focal plane X-ray irradiance is outlined. The deconvolution of the telescope point response function from the image data is discussed. Methods of estimating the temperature, pressure, and number density of coronal plasmas are outlined.

Krieger, A. S.↗
NASA NTRS1992

A comparison of LOWTRAN-7 corrected Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data with ground spectral measurements

Atmospheric correction of imaging spectroscopy data is required for quantitative analysis. Different models were proposed for atmospheric correction of these data. LOWTRAN-7 is a low-resolution model and computer code for predicting atmospheric transmittance and background radiance from 0 to 50,00 cm(sup -1) which was developed by the Air Force Geophysics Laboratory. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data used are radiometrically calibrated and include the 28 Sep. 1989 Providence Fan flight line segment 07, California. It includes a dark gravel surface defined as a calibration site by the Geologic Remote Sensing Field Experiment (GRSFE). Several ground measurements of portable spectrometer DAEDALUS AA440 Spectrafax were taken during the GRSFE, July 1989 field campaign. Comparisons of the LOWTRAN-7 corrected AVIRIS data with the ground spectrometer measurement were made.

Xu, Peng-Yang↗
NASA NTRS1989

Rapid enumeration of viable bacteria by image analysis

A direct viable counting method for enumerating viable bacteria was modified and made compatible with image analysis. A comparison was made between viable cell counts determined by the spread plate method and direct viable counts obtained using epifluorescence microscopy either manually or by automatic image analysis. Cultures of Escherichia coli, Salmonella typhimurium, Vibrio cholerae, Yersinia enterocolitica and Pseudomonas aeruginosa were incubated at 35 degrees C in a dilute nutrient medium containing nalidixic acid. Filtered samples were stained for epifluorescence microscopy and analysed manually as well as by image analysis. Cells enlarged after incubation were considered viable. The viable cell counts determined using image analysis were higher than those obtained by either the direct manual count of viable cells or spread plate methods. The volume of sample filtered or the number of cells in the original sample did not influence the efficiency of the method. However, the optimal concentration of nalidixic acid (2.5-20 micrograms ml-1) and length of incubation (4-8 h) varied with the culture tested. The results of this study showed that under optimal conditions, the modification of the direct viable count method in combination with image analysis microscopy provided an efficient and quantitative technique for counting viable bacteria in a short time.

Non-NASA Center↗
NASA NTRSDate not supplied

Quantitative Phenotypic Analysis of Arabidopsis Thaliana Grown in Microgravity Using Soap, an Applied Artificial Intelligence

Phenotypic analysis is an essential step in studying the gravitropic responses and gravitational stress experienced by plants grown in microgravity. Many of the phenotypic traits analyzed in gravitropism studies, such as root length, leaf area, secondary root count, and number of root hairs, currently rely upon manual measurement methods for quantification. However, new advances in data analysis technology using artificial intelligence offer an opportunity for more efficient phenotypic analysis and a reduction of time spent in the data collection phase. In this project, the ability of a new artificially intelligent data collection software, SOAP (Simple Object Access Protocol), to collect and quantify phenotypic traits of Arabidopsis thaliana will be assessed. This project will test the measurements taken by an initial draft of the software. SOAP will take measurements of shoot length, a key phenotype used to assess A. thaliana stress response when grown in microgravity conditions. Shoot length measurements made by SOAP will be compared against a series of manual shoot length measurements. The comparison between the two methods of data measurement will provide valuable insight into the relative accuracy of SOAP and the margin of human error when conducting lab measurements.

Arabidopsis↗
DOE OSTI2021

Evolution of the structure and chemical composition of the interface between multi-component silicate glasses and yttria-stabilized zirconia after 40,000 h exposure in air at 800 °C

The chemical and structural stability of two commercial multicomponent silicate glasses (SCN and G6) in contact with yttria-stabilized zirconia (YSZ) was investigated after exposure times of up to 40,000 h in air at 800 °C. With exposure time, interfacial layers develop at the SCN-YSZ and G6-YSZ interfaces, which were characterized in detail using both quantitative chemical analysis and atomic-resolution imaging. At the SCN-YSZ interface, a Ca-Ba-Si-O reaction phase was found to grow by diffusion control. In G6-YSZ, Raman spectroscopy and electron microscopy revealed a disorganized interfacial reaction later between G6 and YSZ, and the occurrence of cubic to tetragonal to monoclinic phase transformations in YSZ. Finally, this microstructural evolution is discussed in terms of devitrification resistance of glass and diffusion processes at interfaces.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Depth/diameter relationships of fresh craters within Hesperia Planum, Mars

Meteorite impact craters represent important geological features for revealing the near-surface layers of a planetary surface. In the case of Mars, this characteristic was proposed as a useful method to study spatial variations of such attributes as the distribution of sub-surface volatiles, and heat flow. Using the Planetary Image Cartography System (PICS) software, a quantitative analysis was completed of the geometry of fresh impact craters in the Hesperia Planum region of Mars, where an uniform target material and optimum viewing geometry make possible an analysis of target effects over a large geographic region. Because of the morphologic similarity to the lunar maria, it is likely that Hesperia Planum comprises a series of flood lavas that partially infilled topographic depressions within the Martian highlands. Measurements of partially buried crater rims suggest that the lava flows within Hesperia Planum are between 200-400 m thick.

Mouginis-Mark, P. J.↗
DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Investigation of Carbon Fiber Architecture in Braided Composites Using X-Ray CT Inspection

During the fabrication of braided carbon fiber composite materials, process variations occur which affect the fiber architecture. Quantitative measurements of local and global fiber architecture variations are needed to determine the potential effect of process variations on mechanical properties of the cured composite. Although non-destructive inspection via X-ray CT imaging is a promising approach, difficulties in quantitative analysis of the data arise due to the similar densities of the material constituents. In an effort to gain more quantitative information about features related to fiber architecture, methods have been explored to improve the details that can be captured by X-ray CT imaging. Metal-coated fibers and thin veils are used as inserts to extract detailed information about fiber orientations and inter-ply behavior from X-ray CT images.

braided composites↗
DOE OSTI2021

Deep Learning Analysis of Polaritonic Wave Images

Deep learning (DL) is an emerging analysis tool across the sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nanoscale deeply subdiffractional images of propagating polaritonic waves in complex materials. Utilizing the convolutional neural network (CNN), we developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves. Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and material parameters in a time scale that is at least 3 orders of magnitude faster than common fitting/processing procedures. The CNN-based analysis was validated by examining the experimental near-field images of charge-transfer plasmon polaritons at graphene/α-RuCl3 interfaces. Our work provides a general framework for extracting quantitative information from images generated with a variety of scanning probe methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Visualization techniques to aid in the analysis of multi-spectral astrophysical data sets

The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions, and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists.

Brugel, Edward W.↗
NASA NTRS1993

Visualization techniques to aid in the analysis of multispectral astrophysical data sets

The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists. Twenty-one examples of the use of visualization for astrophysical data are included with this report. Sixteen publications related to efforts performed during or initiated through work on this project are listed at the end of this report.

Brugel, E. W.↗
NASA NTRS1998

Development of High Speed Interferometry Imaging and Analysis Techniques for Compressible Dynamic Stall

The development of a high-speed, phase-locked, realtime, point diffraction interferometry system for quantitative imaging unsteady separated flows is described. The system enables recording of up to 224 interferograms of the dynamic stall flow over an oscillating airfoil using a drum camera at rates of up to 40 KHz controlled by custom designed electronic interlocking circuitry. Several thousand interferograms of the flow have been obtained using this system. A comprehensive image analysis package has been developed for automatic processing of this large number of images. The software has been specifically tuned to address the special characteristics of airfoil flow interferograms. Examples of images obtained using the standard and the high-speed interferometry techniques are presented along with a demonstration of the image processing routine's ability to resolve the fine details present in these images.

Chandrasekhara, M. S.↗
DOE OSTI2024

ESIAS Pseudocode

The Explosive Shock Image Analysis System (ESAIS) is designed to provide a semiautomated quantitative analysis of fast-frame imagery taken during the detonation of high explosive (HE) rate sticks. ESIAS does this by assisting the user in identifying the HE stick geometry on the image (i.e., sides of the undetonated HE stick, detonation front, an axial reference point to assist in the inter-comparison of images); scaling the images based on the known diameter of the undetonated HE stick; identifying the profiles of various surfaces resulting from the detonation process such as the shock wave and product gases or the outer pipe surface; calculating the detonation speed of the HE by tracking the distance it travels between two images with a known time lapse between the images; and determining the slit velocities resulting from the detonation. ESIAS will also import scope traces from oscilloscopes and assist in identifying the time of arrival of significant peaks.

97 MATHEMATICS AND COMPUTING↗