Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Image representation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Bandwidth compression of synthetic aperture radar imagery by quantization of raw radar data

A study is made of the effects of quantization of the radar returns transmitted from aircraft or spacecraft employing a synthetic aperture radar system. The study is based on the output images obtained after one-bit, two-bit, and eight-bit quantizations and comparing the results to ground truth. In this way the degradation resulting from data or bandwidth reduction is determined. Quantization is evaluated in terms of crater scene, number of looks, and transmission error rate. It is found that two-bit quantization of raw radar data from homogeneous scenes processed to 32 looks yields nearly all the details of the original. One-bit quantization of raw radar data from homogeneous scenes processed to 32 looks yields a good visual representation of the scene but some fine detail is lost and the absolute reflectivity level is not reliable. Image quality is observed to improve with more looks and video and intermediate frequency quantization are not distinguishable even for one-bit quantizations. Image quality is not influenced by bit error rates less than about 2 to the -7th power.

Lipes, R. G.↗
NASA NTRS1988

A machine vision identification technique from range images

An orientation-independent identification technique from three-dimensional surface maps or range images is developed. Given the range image of an object, it is decomposed into orientation-independent patches using the sign of Gaussian curvature. A relational graph is then set up such that a node represents a patch and an edge represents the adjacency of two patches. The identification of the object is achieved by matching its graph representation to a number of model graphs. The matching is performed by employing the best-first search strategy. Examples of real range images show the merit of the technique.

Kehtarnavaz, N.↗
NASA NTRS1982

Structural analysis techniqes for remote sensing

The structural analysis of remotely sensed imagery is defined and basic techniques for implementing the process are described. Structural analysis uses knowledge of the properties of an entity, its parts and their relationships, and the relationships in which it participates at a higher level to locate and recognize objects in a visual scene. The representation of structural knowledge, the development of algorithms for using the knowledge to help analyze an image, and techniques for storage and retrieval of relational models are addressed.

Shapiro, L. G.↗
NASA NTRS1994

A reactive system for open terrain navigation: Performance and limitations

We describe a core system for autonomous navigation in outdoor natural terrain. The system consists of three parts: a perception module which processes range images to identify untraversable regions of the terrain, a local map management module which maintains a representation of the environment in the vicinity of the vehicle, and a planning module which issues commands to the vehicle controller. Our approach is to use the concept of 'early traversability evaluation', and on the use of reactive planning for generating commands to drive the vehicle. We argue that our approach leads to a robust and efficient navigation system. We illustrate our approach by an experiment in which a vehicle travelled autonomously for one kilometer through unmapped cross-country terrain.

Langer, D.↗
NASA NTRS2000

Wavelet-Based Interpolation and Representation of Non-Uniformly Sampled Spacecraft Mission Data

A well-documented problem in the analysis of data collected by spacecraft instruments is the need for an accurate, efficient representation of the data set. The data may suffer from several problems, including additive noise, data dropouts, an irregularly-spaced sampling grid, and time-delayed sampling. These data irregularities render most traditional signal processing techniques unusable, and thus the data must be interpolated onto an even grid before scientific analysis techniques can be applied. In addition, the extremely large volume of data collected by scientific instrumentation presents many challenging problems in the area of compression, visualization, and analysis. Therefore, a representation of the data is needed which provides a structure which is conducive to these applications. Wavelet representations of data have already been shown to possess excellent characteristics for compression, data analysis, and imaging. The main goal of this project is to develop a new adaptive filtering algorithm for image restoration and compression. The algorithm should have low computational complexity and a fast convergence rate. This will make the algorithm suitable for real-time applications. The algorithm should be able to remove additive noise and reconstruct lost data samples from images.

Bose, Tamal↗
NASA NTRS1985

An algorithm for computing the number of distinct spectral vectors in thematic mapper data

A computationally efficient method was developed to compute the number of distinct spectral vectors and their frequency of occurrence in Landsat-4 Thematic Mapper (TM) data. The algorithm first partitions the image into spectrally disjoint subsets and then computes the frequency distribution of distinct spectral vectors within each subset from a multidimensional histogram. The overall frequency distribution is tabulated by accumulating the results from each subset. The number of distinct spectral vectors could be used as a measure of potential storage compaction of alternate data representations for data compression, or as a measure of information content in the comparison of spectral band combinations and/or spatial resolutions for an image. Results from processing three 512 x 512 pixel Landsat-4 TM images and one Landsat-4 Multispectral Scanner (MSS) image are presented as examples. An algorithm for computing the frequency distribution of distinct spectral vectors in MSS data is given in the Appendix.

Wharton, S. W.↗
DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Exploring Classification of Topological Priors With Machine Learning for Feature Extraction

In many scientific endeavors, increasingly abstract representations of data allow for new interpretive methodologies and conceptualization of phenomena. For example, moving from raw imaged pixels to segmented and reconstructed objects allows researchers new insights and means to direct their studies toward relevant areas. Thus, the development of new and improved methods for segmentation remains an active area of research. With advances in machine learning and neural networks, scientists have been focused on employing deep neural networks such as U-Net to obtain pixel-level segmentations, namely, defining associations between pixels and corresponding/referent objects and gathering those objects afterward. Topological analysis, such as the use of the Morse-Smale complex to encode regions of uniform gradient flow behavior, offers an alternative approach: first, create geometric priors, and then apply machine learning to classify. This approach is empirically motivated since phenomena of interest often appear as subsets of topological priors in many applications. Using topological elements not only reduces the learning space but also introduces the ability to use learnable geometries and connectivity to aid the classification of the segmentation target. Here, in this article, we describe an approach to creating learnable topological elements, explore the application of ML techniques to classification tasks in a number of areas, and demonstrate this approach as a viable alternative to pixel-level classification, with similar accuracy, improved execution time, and requiring marginal training data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1970

Remote sensing for detection of soil limitations in agricultural areas

Automatic analysis of soil limitations studied by an automatic color TV density slicing system was accomplished. This system color codes the density range of the value component of color of an image. Maps of soil limitations or other similar soil groups are produced by photographing the color coded representation of an area. The planimeter feature of the density slicing system measures the area of each soil limitation providing information on the importance of a soil limitation in an area. The results of this study suggest that an automatic color TV density slicing system has great potential not only for identifying and mapping similar soil areas, but also for indicating the percentage composition of an area.

Frazee, C. J.↗
NASA NTRS1974

Multispectral combination and display of ERTS-1 data

Standard NASA color composites combine the most relevant 3 bands from the 4 MSS bands available. An alternate approach is to extract the principal components of the data by a linear transformation of the 4 bands. This approach leads to a low dimensionality representation of ERTS-1 data with the least degradation, in the mean square sense, of the radiometric accuracy. The technique has been applied with success to ERTS-1 MSS data for several geographic areas in California. For all examples considered the mean square representation error is less than one percent. By combining this dimensionality reduction which our previous results on image enhancement for visual display, color composites are obtained which contain and display most of the information provided by the ERTS-1 sensors.

Algazi, V. R.↗
NASA NTRS1995

Program For Editing Graphical Displays Of Schedules

XOPPS is window-based software tool from graphics providing easy and fast "what you see is what you get" (WYSIWYG) on-screen editing capabilities. Provides area, analogous to canvas, displaying full image of schedule being edited. Canvas contains header area (for test) and schedule area (for plotting graphical representations of milestone objects in flexible time line). Written in C language.

Mulnix, Cassie L.↗
NASA NTRS2014

A New Global Core Plasma Model of the Plasmasphere

The Global Core Plasma Model (GCPM) is the first empirical model for thermal inner magnetospheric plasma designed to integrate previous models and observations into a continuous in value and gradient representation of typical total densities. New information about the plasmasphere, in particular, make possible significant improvement. The IMAGE Mission Radio Plasma Imager (RPI) has obtained the first observations of total plasma densities along magnetic field lines in the plasmasphere and polar cap. Dynamics Explorer 1 Retarding Ion Mass Spectrometer (RIMS) has provided densities in temperatures in the plasmasphere for 5 ion species. These and other works enable a new more detailed empirical model of thermal in the inner magnetosphere that will be presented. Specifically shown here are the inner-plasmasphere RIMS measurements, radial fits to densities and temperatures for H(+), He(+), He(++), O(+), and O(+) and the error associated with these initial simple fits. Also shown are more subtle dependencies on the f10.7 P-value (see Richards et al. [1994]).

plasmasphere density↗
NASA NTRS2014

A New Global Core Plasma Model of the Plasmasphere

The Global Core Plasma Model (GCPM) is the first empirical model for thermal inner magnetospheric plasma designed to integrate previous models and observations into a continuous in value and gradient representation of typical total densities. New information about the plasmasphere, in particular, makes possible significant improvement. The IMAGE Mission Radio Plasma Imager (RPI) has obtained the first observations of total plasma densities along magnetic field lines in the plasmasphere and polar cap. Dynamics Explorer 1 Retarding Ion Mass Spectrometer (RIMS) has provided densities in temperatures in the plasmasphere for 5 ion species. These and other works enable a new more detailed empirical model of thermal in the inner magnetosphere that will be presented.

Gallagher, D. L.↗
NASA NTRS2017

From Tomography to Material Properties of Thermal Protection Systems

A NASA Ames Research Center (ARC) effort, under the Entry Systems Modeling (ESM) project, aims at developing micro-tomography (micro-CT) experiments and simulations for studying materials used in hypersonic entry systems. X-ray micro-tomography allows for non-destructive 3D imaging of a materials micro-structure at the sub-micron scale, providing fiber-scale representations of porous thermal protection systems (TPS) materials. The technique has also allowed for In-situ experiments that can resolve response phenomena under realistic environmental conditions such as high temperature, mechanical loads, and oxidizing atmospheres. Simulation tools have been developed at the NASA Ames Research Center to determine material properties and material response from the high-fidelity tomographic representations of the porous materials with the goal of informing macroscopic TPS response models and guiding future TPS design.

Tomography↗
NASA NTRS2024

Modeling Atmospheric Brown Carbon in the GISS ModelE Earth System Model

Brown carbon (BrC) is an absorbing organic aerosol (OA), primarily emitted through biomass burning (BB), which exhibits light absorption unique to both black carbon (BC) and other organic aerosols. Despite many field and laboratory studies seeking to constrain BrC properties, the radiative forcing (RF) of BrC is still highly uncertain. To better understand its climate impact, we introduced BrC to the One-Moment Aerosol (OMA) module of the GISS ModelE Earth system model (ESM). We assessed ModelE sensitivity to primary BrC processed through a novel chemical aging scheme and to secondary BrC formed from biogenic volatile organic compounds (BVOCs). Initial results show that BrC typically contributes a top-of-the-atmosphere (TOA) radiative effect of 0.04 W m −2 . Sensitivity tests indicate that explicitly simulating BrC (separating it from other OAs), including secondary BrC, and simulating chemical bleaching of BrC contribute distinguishable radiative effects and should be accounted for in BrC schemes. This addition of prognostic BrC to ModelE allows greater physical and chemical complexity in OA representation with no apparent trade-off in model performance, as the evaluation of ModelE aerosol optical depth against Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) retrieval data, with and without the BrC scheme, reveals similar skill in both cases. Thus, BrC should be explicitly simulated to allow more physically based chemical composition, which is crucial for more detailed OA studies like comparisons to in situ measurement campaigns. We include a summary of best practices for BrC representation within ModelE at the end of this paper.

brown carbon aerosols↗
NASA NTRS1989

Some methods of encoding simple visual images for use with a sparse distributed memory, with applications to character recognition

To study the problems of encoding visual images for use with a Sparse Distributed Memory (SDM), I consider a specific class of images- those that consist of several pieces, each of which is a line segment or an arc of a circle. This class includes line drawings of characters such as letters of the alphabet. I give a method of representing a segment of an arc by five numbers in a continuous way; that is, similar arcs have similar representations. I also give methods for encoding these numbers as bit strings in an approximately continuous way. The set of possible segments and arcs may be viewed as a five-dimensional manifold M, whose structure is like a Mobious strip. An image, considered to be an unordered set of segments and arcs, is therefore represented by a set of points in M - one for each piece. I then discuss the problem of constructing a preprocessor to find the segments and arcs in these images, although a preprocessor has not been developed. I also describe a possible extension of the representation.

Jaeckel, Louis A.↗
NASA NTRS1981

A model for simulation and processing of radar images

A model for recording, processing, presentation, and analysis of radar images in digital form is presented. The observed image is represented as having two random components, one which models the variation due to the coherent addition of electromagnetic energy scattered from different objects in the illuminated areas. This component is referred to as fading. The other component is a representation of the terrain variation which can be described as the actual signal which the radar is attempting to measure. The combination of these two components provides a description of radar images as being the output of a linear space-variant filter operating on the product of the fading and terrain random processes. In addition, the model is applied to a digital image processing problem using the design and implementation of enhancement scene. Finally, parallel approaches are being employed as possible means of solving other processing problems such as SAR image map-matching, data compression, and pattern recognition.

Stiles, J. A.↗