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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Using Heaps in Recursive Hierarchical Segmentation of Data

A modification to increase processing speed has been made in the algorithm and implementing software reported in Modified Recursive Hierarchical Segmentation of Data (GSC-14681-1), NASA Tech Briefs, Vol. 30, No. 6 (June 2006), page 51. That software performs recursive hierarchical segmentation of data having spatial characteristics (e.g., spectral-image data). The segmentation process includes an iterative subprocess, in each iteration of which it is necessary to determine a best pair of regions to merge [merges being justified by one or more measure(s) similarity of pixels in the regions]. In the previously reported version of the algorithm and software, the choice of a best pair of regions to merge involved the use of a fully sorted list of regions. That version was computationally inefficient because a fully sorted list is not needed: what is needed is only the identity of the pair of regions characterized by the smallest measure of dissimilarity. The present modification replaces the use of a fully sorted list with the use of data heaps, which are computationally more efficient for performing the required comparisons among dissimilarity measures. The modification includes the incorporation of standard and modified functions for creating and updating data heaps

Tilton, James C.↗
NASA NTRS2014

Flood Extent Mapping for Namibia Using Change Detection and Thresholding with SAR

A new method for flood detection change detection and thresholding (CDAT) was used with synthetic aperture radar (SAR) imagery to delineate the extent of flooding for the Chobe floodplain in the Caprivi region of Namibia. This region experiences annual seasonal flooding and has seen a recent renewal of severe flooding after a long dry period in the 1990s. Flooding in this area has caused loss of life and livelihoods for the surrounding communities and has caught the attention of disaster relief agencies. There is a need for flood extent mapping techniques that can be used to process images quickly, providing near real-time flooding information to relief agencies. ENVISAT/ASAR and Radarsat-2 images were acquired for several flooding seasons from February 2008 to March 2013. The CDAT method was used to determine flooding from these images and includes the use of image subtraction, decision based classification with threshold values, and segmentation of SAR images. The total extent of flooding determined for 2009, 2011 and 2012 was about 542 km2, 720 km2, and 673 km2 respectively. Pixels determined to be flooded in vegetation were typically <0.5 % of the entire scene, with the exception of 2009 where the detection of flooding in vegetation was much greater (almost one third of the total flooded area). The time to maximum flooding for the 2013 flood season was determined to be about 27 days. Landsat water classification was used to compare the results from the new CDAT with SAR method; the results show good spatial agreement with Landsat scenes.

Flood↗
NASA NTRS1982

Core segment 15008 - Regolith stratigraphy at Apennine Front Station 2 using multispectral imaging

High precision multispectral images for Apennine Front core segment 15008 are presented. These data have a spatial resolution less than approximately 0.5 mm and are analyzed for their compositional information using image analysis techniques. The stratigraphy of the regolith sampled by 15008 is documented here as three distinct zones, the most prominent of which is a feldspathic fragment-rich zone with a chaotic fabric that occurs between 10 and 18 cm depth. It is suggested that this material is the primary rim crest deposit of the local 10 m crater. Above this zone the stratigraphy is more horizontal in nature. Below this zone the soil is observed to be relatively homogeneous with no distinctive structure to 23 cm depth.

Pieters, C. M.↗
NASA NTRS1992

Identification of sea ice types in spaceborne synthetic aperture radar data

This study presents an approach for identification of sea ice types in spaceborne SAR image data. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different ice types measured by a land-based scatterometer. Extensive scatterometer observations and experience accumulated in field campaigns during the last 10 yr were used to construct these look-up tables. The classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics are discussed. Results using both aircraft and simulated ERS-1 SAR data are presented and compared to limited field ice property measurements and coincident passive microwave imagery. The importance of an integrated postlaunch program for the validation and improvement of this approach is discussed.

Kwok, Ronald↗
NASA NTRS1990

Segmentation, modeling and classification of the compact objects in a pile

The problem of interpreting dense range images obtained from the scene of a heap of man-made objects is discussed. A range image interpretation system consisting of segmentation, modeling, verification, and classification procedures is described. First, the range image is segmented into regions and reasoning is done about the physical support of these regions. Second, for each region several possible three-dimensional interpretations are made based on various scenarios of the objects physical support. Finally each interpretation is tested against the data for its consistency. The superquadric model is selected as the three-dimensional shape descriptor, plus tapering deformations along the major axis. Experimental results obtained from some complex range images of mail pieces are reported to demonstrate the soundness and the robustness of our approach.

Gupta, Alok↗
NASA NTRS1997

Colloids and Nucleation

The objectives of the work funded under this grant were to develop a microphotographic technique and use it to monitor the nucleation and growth of crystals of hard colloidal spheres. Special attention is given to the possible need for microgravity studies in future experiments. A number of persons have been involved in this work. A masters student, Keith Davis, began the project and developed a sheet illumination apparatus and an image processing system for detection and analysis. His work on a segmentation program for image processing was sufficient for his master's research and has been published. A post doctoral student Bernie Olivier and a graduate student Yueming He, who originally suggested the sheet illumination, were funded by another source but along with Keith made photographic series of several samples (that had been made by Keith Davis). Data extraction has been done by Keith, Bernie, Yueming and two undergraduates employed on the grant. Results are published in Langmuir. These results describe the sheet lighting technique as one which illuminates not only the Bragg scattering crystal, but all the crystals. Thus, accurate crystal counts can be made for nucleation rate measurements. The strange crystal length scale reduction, observed in small angle light scattering (SALS) studies, following the initial nucleation and growth period, has been observed directly. The Bragg scattering (and dark) crystal size decreases in the crossover region. This could be an effect due to gravitational forces or due to over- compression of the crystal during growth. Direct observations indicate a complex morphology for the resulting hard sphere crystals. The crystal edges are fairly sharp but the crystals have a large degree of internal structure. This structure is a result of (unstable) growth and not aggregation. As yet unpublished work compares growth exponents data with data obtained by SALS. The nucleation rate density is determined over a broad volume fraction range (0.505 to 0.575) and compared to classical nucleation rate calculations. The experimental nucleation rate density varies with volume fraction more slowly than expected theoretically. Gravity may influence nucleation because crystal nuclei are more dense than the surrounding fluid and may be gravitationally convected. The largest observed crystal size was near volume fraction 0.52. Near the freezing point, crystals should grow to large size because nuclei are sparse and there is plenty of room to grow without contacting other crystals. Near melting the opposite is true, with crystals growing into one another shortly after nucleation. The maximum crystal size seen near 0.52 volume fraction suggests that gravitational stresses are limiting crystal growth near freezing.

Ackerson, Bruce↗
NASA NTRS2006

Improvement in Recursive Hierarchical Segmentation of Data

A further modification has been made in the algorithm and implementing software reported in Modified Recursive Hierarchical Segmentation of Data (GSC- 14681-1), NASA Tech Briefs, Vol. 30, No. 6 (June 2006), page 51. That software performs recursive hierarchical segmentation of data having spatial characteristics (e.g., spectral-image data). The output of a prior version of the software contained artifacts, including spurious segmentation-image regions bounded by processing-window edges. The modification for suppressing the artifacts, mentioned in the cited article, was addition of a subroutine that analyzes data in the vicinities of seams to find pairs of regions that tend to lie adjacent to each other on opposite sides of the seams. Within each such pair, pixels in one region that are more similar to pixels in the other region are reassigned to the other region. The present modification provides for a parameter ranging from 0 to 1 for controlling the relative priority of merges between spatially adjacent and spatially non-adjacent regions. At 1, spatially-adjacent-/spatially- non-adjacent-region merges have equal priority. At 0, only spatially-adjacent-region merges (no spectral clustering) are allowed. Between 0 and 1, spatially-adjacent- region merges have priority over spatially- non-adjacent ones.

Tilton, James C.↗
NASA NTRS1987

Constraint-based stereo matching

The major difficulty in stereo vision is the correspondence problem that requires matching features in two stereo images. Researchers describe a constraint-based stereo matching technique using local geometric constraints among edge segments to limit the search space and to resolve matching ambiguity. Edge segments are used as image features for stereo matching. Epipolar constraint and individual edge properties are used to determine possible initial matches between edge segments in a stereo image pair. Local edge geometric attributes such as continuity, junction structure, and edge neighborhood relations are used as constraints to guide the stereo matching process. The result is a locally consistent set of edge segment correspondences between stereo images. These locally consistent matches are used to generate higher-level hypotheses on extended edge segments and junctions to form more global contexts to achieve global consistency.

Kuan, D. T.↗
NASA NTRS1993

Gamma-ray imaging with germanium detectors

Externally segmented germanium detectors promise a breakthrough in gamma-ray imaging capabilities while retaining the superb energy resolution of germanium spectrometers. By combining existing position-sensitive detectors with an appropriate code aperture, two-dimensional imaging with 0.2-deg angular resolution becomes practical for a typical balloon experiment. Much finer resolutions are possible with larger separations between detectors and the coded aperture as would be applicable for space-based or lunar-based observatories. Two coaxial germanium detectors divided into five external segments have been fabricated and have undergone extensive performance evaluation and imaging testing in our laboratory. These tests together with detailed Monte Carlo modeling calculations have demonstrated the great promise of this sensor technology for future gamma-ray missions.

Mahoney, W. A.↗
NASA NTRSDate not supplied

End-to-end Modeling of Lyot Coronagraph for LUVOIR: Sensitivity to Segment-Level Aberrations

Direct imaging and spectroscopic characterization of atmospheres of Earth-like exoplanets require starlight suppression to detect a faint planetary signal at small angular separations. Future space telescopes will require segmentation to survey a statistically significant population of nearby stars systems by increasing aperture size beyond the limits set by traditional monolithic apertures; however, segmentation also represents an additional challenge due to the introduction of temporal pupil discontinuities in terms of both phase and amplitude. For this study, a baseline Lyot coronagraph is used with the Large UV / Optical /Infrared (LUVOIR) mission’s 15-m on-axis, segmented optical telescope assembly (OTA). We present a high-fidelity, end-to-end modeling approach developed as part of the Segmented Coronagraph Design and Analysis (SCDA) group that includes diffractive optical propagations at the 1e-10 contrast level across the entire optical train including the OTA and intermediate re-imaging optics inside the coronagraph instrument. We perform a tolerancing analysis to assess the contrast sensitivity to wavefront perturbations for a set of key telescope terms including segment-level low-order and mid-spatial frequency aberrations. As an input to the end-to-end telescope model, we use two industry-provided segment-level wavefront aberration datasets pertaining: (1) thermal gradients and accelerations (courtesy of Ball-Aerospace and L3-Harris), and (2) telescope dynamics including line-of-sight and segment jitter (courtesy of Lockheed-Martin). Contrast performance outputs of the coronagraph are fed into a mission design reference simulator to assess the scientific yield sensitivity. End-to-end modeling will incorporate performance improvements due to active wavefront sensing and control and/or segment-level metrology to improve system performance.

Optics↗
NASA NTRS1989

Image wavelet decomposition and applications

The general problem of computer vision has been investigated for more that 20 years and is still one of the most challenging fields in artificial intelligence. Indeed, taking a look at the human visual system can give us an idea of the complexity of any solution to the problem of visual recognition. This general task can be decomposed into a whole hierarchy of problems ranging from pixel processing to high level segmentation and complex objects recognition. Contrasting an image at different representations provides useful information such as edges. An example of low level signal and image processing using the theory of wavelets is introduced which provides the basis for multiresolution representation. Like the human brain, we use a multiorientation process which detects features independently in different orientation sectors. So, images of the same orientation but of different resolutions are contrasted to gather information about an image. An interesting image representation using energy zero crossings is developed. This representation is shown to be experimentally complete and leads to some higher level applications such as edge and corner finding, which in turn provides two basic steps to image segmentation. The possibilities of feedback between different levels of processing are also discussed.

Treil, N.↗
NASA NTRS2001

Left ventricular endocardial surface detection based on real-time 3D echocardiographic data

OBJECTIVE: A new computerized semi-automatic method for left ventricular (LV) chamber segmentation is presented. METHODS: The LV is imaged by real-time three-dimensional echocardiography (RT3DE). The surface detection model, based on level set techniques, is applied to RT3DE data for image analysis. The modified level set partial differential equation we use is solved by applying numerical methods for conservation laws. The initial conditions are manually established on some slices of the entire volume. The solution obtained for each slice is a contour line corresponding with the boundary between LV cavity and LV endocardium. RESULTS: The mathematical model has been applied to sequences of frames of human hearts (volume range: 34-109 ml) imaged by 2D and reconstructed off-line and RT3DE data. Volume estimation obtained by this new semi-automatic method shows an excellent correlation with those obtained by manual tracing (r = 0.992). Dynamic change of LV volume during the cardiac cycle is also obtained. CONCLUSION: The volume estimation method is accurate; edge based segmentation, image completion and volume reconstruction can be accomplished. The visualization technique also allows to navigate into the reconstructed volume and to display any section of the volume.

Non-NASA Center↗
NASA NTRS1982

Automatic digital image registration

This paper introduces a general procedure for automatic registration of two images which may have translational, rotational, and scaling differences. This procedure involves (1) segmentation of the images, (2) isolation of dominant objects from the images, (3) determination of corresponding objects in the two images, and (4) estimation of transformation parameters using the center of gravities of objects as control points. An example is given which uses this technique to register two images which have translational, rotational, and scaling differences.

Goshtasby, A.↗
NASA NTRS2018

GEONEX: Progressive Conditional Generative Adversarial Training Using Transfer learning

Obtaining accurate segmentation on large scale images is an open problem in deep learning. The main problem is the amount of labeled data that exists for large scale images. Traditionally, the common solution to this problem is to crop the large images into smaller images to increase the amount of available data and train a Conditional Generative Adversarial Network (CGAN). CGANs are currently the state of the art in image to image translation and provide better accuracy than the traditional method of training a encoder based conv-net architecture to minimize the loss at each pixel. This method can produce noisy and discontinuous images with inaccurate results. We seek to solve this problem by utilizing the concepts of transfer learning and progressive training to create a CGAN that can segment large scale images with a limited amount of labeled data. In transfer learning we recognize that many learned features are applicable to many classes from multiple domains. This introduces the concept of feature reusability, which is the basis for finetuning. Progressive training got its start in training models on the same images at different resolutions. In this work we instead train a GAN on increasing image scales by transferring the weights from the smaller scales to the larger scales. The learned features at the smaller scales are continually reused and applied to larger scales to create a CGAN that can perform accurate segmentation on large scale images. We apply this method to detect building footprints on very high-resolution overhead imagery (e.g Digital Globe and high resolution airborne platforms).

GEONEX↗
NASA NTRS1981

Textural features for radar image analysis

Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

Shanmugan, K. S.↗
NASA NTRS1983

An iterative approach to region growing using associative memories

Region growing, often given as a classical example of the recursive control structures used in image processing which are often awkward to implement in hardware where the intent is the segmentation of an image at raster scan rates, is addressed in light of the postulate that any computation which can be performed recursively can be performed easily and efficiently by iteration coupled with association. Attention is given to an algorithm and hardware structure able to perform region labeling iteratively at scan rates. Every pixel is individually labeled with an identifier which signifies the region to which it belongs. Difficulties otherwise requiring recursion are handled by maintaining an equivalence table in hardware transparent to the computer, which reads the labeled pixels. A simulation of the associative memory has demonstrated its effectiveness.

Snyder, W. E.↗
NASA NTRS1992

The Spectral Image Processing System (SIPS): Software for integrated analysis of AVIRIS data

The Spectral Image Processing System (SIPS) is a software package developed by the Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, in response to a perceived need to provide integrated tools for analysis of imaging spectrometer data both spectrally and spatially. SIPS was specifically designed to deal with data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the High Resolution Imaging Spectrometer (HIRIS), but was tested with other datasets including the Geophysical and Environmental Research Imaging Spectrometer (GERIS), GEOSCAN images, and Landsat TM. SIPS was developed using the 'Interactive Data Language' (IDL). It takes advantage of high speed disk access and fast processors running under the UNIX operating system to provide rapid analysis of entire imaging spectrometer datasets. SIPS allows analysis of single or multiple imaging spectrometer data segments at full spatial and spectral resolution. It also allows visualization and interactive analysis of image cubes derived from quantitative analysis procedures such as absorption band characterization and spectral unmixing. SIPS consists of three modules: SIPS Utilities, SIPS_View, and SIPS Analysis. SIPS version 1.1 is described below.

Kruse, F. A.↗
NASA NTRS2004

Video Image Tracking Engine

A method and system for processing an image including capturing an image and storing the image as image pixel data. Each image pixel datum is stored in a respective memory location having a corresponding address. Threshold pixel data is selected from the image pixel data and linear spot segments are identified from the threshold pixel data selected.. Ihe positions of only a first pixel and a last pixel for each linear segment are saved. Movement of one or more objects are tracked by comparing the positions of fust and last pixels of a linear segment present in the captured image with respective first and last pixel positions in subsequent captured images. Alternatively, additional data for each linear data segment is saved such as sum of pixels and the weighted sum of pixels i.e., each threshold pixel value is multiplied by that pixel's x-location).

Howard, Richard T.↗