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Results for “Feature Extraction”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Use of SIR-B multiincidence-angle imagery to study iceberg detectability and offshore ocean feature extraction

The Shuttle Imaging Radar B (SIR-B) iceberg experiment is described and expected results are outlined. The specific objectives are: (1) to study the L-band satellite SAR detectability of iceberg targets as a function of incidence angle and observed conditions; (2) to compare L-, X-, and C-band aircraft imagery, and to use this data as a basis for an appropriate simulation of satellite imagery and to compare with SIR-B images; and (3) to determine the possibility of comparive available surface or near surface currents with estimates of the range component of surface current derived from the SIR-B data (IOS experiment).

Gray, A. L.↗
NASA NTRS1985

Technical support for creating an artificial intelligence system for feature extraction and experimental design

Techniques for classifying objects into groups or clases go under many different names including, most commonly, cluster analysis. Mathematically, the general problem is to find a best mapping of objects into an index set consisting of class identifiers. When an a priori grouping of objects exists, the process of deriving the classification rules from samples of classified objects is known as discrimination. When such rules are applied to objects of unknown class, the process is denoted classification. The specific problem addressed involves the group classification of a set of objects that are each associated with a series of measurements (ratio, interval, ordinal, or nominal levels of measurement). Each measurement produces one variable in a multidimensional variable space. Cluster analysis techniques are reviewed and methods for incuding geographic location, distance measures, and spatial pattern (distribution) as parameters in clustering are examined. For the case of patterning, measures of spatial autocorrelation are discussed in terms of the kind of data (nominal, ordinal, or interval scaled) to which they may be applied.

Glick, B. J.↗
NASA NTRS1988

Advanced feature extraction in remote sensing using artificial intelligence and geographic information systems

Traditional computer-assisted image-analysis techniques in remote sensing lag well behind human abilities in terms of both speed and accuracy. A fundamental limitation of computer-assisted techniques is their inability to assimilate a variety of different data types leading to an interpretation in a manner similar to human image interpretation. Expert systems and computer-vision techniques are proposed as a potential solution to these limitations. Some aspects of human expertise in image analysis may be codified into expert systems. Image understanding and symbolic reasoning provide a means of assimilating spatial information and spatial reasoning into the analysis procedure. Knowledge-based image-analysis systems incorporate many of these concepts and have been implemented for some well defined problem domains. Geographic information systems represent an excellent environment for this type of analysis, providing both analytic tools and contextual information to the analysis procedure.

Estes, John E.↗
NASA NTRS1996

Summary of work on shock wave feature extraction in 3-D datasets

A method for extracting and visualizing shock waves from three dimensional data-sets is discussed. Issues concerning computation time, robustness to numerical perturbations, and noise introduction are considered and compared with other methods. Finally, results using this method are discussed.

Hesselink, Lambertus↗
NASA NTRS1996

3D Feature Extraction for Unstructured Grids

Visualization techniques provide tools that help scientists identify observed phenomena in scientific simulation. To be useful, these tools must allow the user to extract regions, classify and visualize them, abstract them for simplified representations, and track their evolution. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This article explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and those from Finite Element Analysis.

Silver, Deborah↗