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Results for “AUTOMATIC PATTERN RECOGNITION”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

A smart pattern recognition system for the automatic identification of aerospace acoustic sources

An intelligent air-noise recognition system is described that uses pattern recognition techniques to distinguish noise signatures of five different types of acoustic sources, including jet planes, propeller planes, a helicopter, train, and wind turbine. Information for classification is calculated using the power spectral density and autocorrelation taken from the output of a single microphone. Using this system, as many as 90 percent of test recordings were correctly identified, indicating that the linear discriminant functions developed can be used for aerospace source identification.

Cabell, R. H.↗
NASA NTRS1980

Pattern recognition for remote sensing - Progress and prospects

An overview is given of the current state of automatic image pattern recognition as applied to remote sensing of the earth's resources. The framework for the discussion is provided by four important aspects of the remote sensing problem: scene information content, characterization of scene information, information extraction methods, and the net value of extractable information. Outstanding problems are surveyed, as are the prospects for future developments. The effect of increasingly complex data bases and the rapidly evolving digital computer technology are highlighted.

Swain, P. H.↗
NASA NTRS1964

Cloud pattern recognition

Feasibility of perceptron type computer for recognition of vortex patterns in cloud cover photographs

CLOUD COVER↗
NASA NTRS1976

An investigation of potential applications of OP-SAPS: Operational sampled analog processors

The impact of charge-coupled device (CCD) processors on future instrumentation was investigated. The CCD devices studied process sampled analog data and are referred to as OP-SAPS - operational sampled analog processors. Preliminary studies into various architectural configurations for systems composed of OP-SAPS show that they have potential in such diverse applications as pattern recognition and automatic control. It appears probable that OP-SAPS may be used to construct computing structures which can serve as special peripherals to large-scale computer complexes used in real time flight simulation. The research was limited to the following benchmark programs: (1) face recognition, (2) voice command and control, (3) terrain classification, and (4) terrain identification. A small amount of effort was spent on examining a method by which OP-SAPS may be used to decrease the limiting ground sampling distance encountered in remote sensing from satellites.

Parrish, E. A.↗
NASA NTRS1977

Hybrid optical/digital detector

Device for automatic-diffraction pattern recognition is designed around concentric-ring fiber-optic array. Coherent light passes through film-image transparency and lens producing Fourier transform of image. Transform is converted to digital data, the basis of diffraction-pattern recognition program performed by computer.

Shackelford, R. G.↗
NASA NTRS1988

Optical Image Subtraction

Report reviews optical image subtraction techniques developed during years 1975 through 1985. Useful in such disciplines as studies of earth resources, meteorology, automatic surveillance, pattern recognition, studies of urban growth, and compression of bandwidth in communication systems. Describes following real-time techniques: source encoding, polarization modulation, pseudocolor image-difference detection, holographic-shear-lens technique, and nonlinear electro-optics. Reported non-real-time image-subtraction techniques are following, all of which involve intensity subtraction: speckle-diffuser encoding, speckle-pattern encoding, halftone-screen encoding,and polarization-shifted carrier encoding. Offers an attractive alternative to digital electronic image subtraction, is faster and treats all parts of images simultaneously.

Liu, Hua-Kuang↗
NASA NTRS2012

Automatic Estimation of Volcanic Ash Plume Height using WorldView-2 Imagery

We explore the use of machine learning, computer vision, and pattern recognition techniques to automatically identify volcanic ash plumes and plume shadows, in WorldView-2 imagery. Using information of the relative position of the sun and spacecraft and terrain information in the form of a digital elevation map, classification, the height of the ash plume can also be inferred. We present the results from applying this approach to six scenes acquired on two separate days in April and May of 2010 of the Eyjafjallajokull eruption in Iceland. These results show rough agreement with ash plume height estimates from visual and radar based measurements.

pattern recognition↗
NASA NTRS1973

Data handling and analysis for the 1971 corn blight watch experiment

The overall corn blight watch experiment data flow is described and the organization of the LARS/Purdue data center is discussed. Data analysis techniques are discussed in general and the use of statistical multispectral pattern recognition methods for automatic computer analysis of aircraft scanner data is described. Some of the results obtained are discussed and the implications of the experiment on future data communication requirements for earth resource survey systems is discussed.

Anuta, P. E.↗
NASA NTRS1972

Data handling and analysis for the 1971 corn blight watch experiment.

Review of the data handling and analysis methods used in the near-operational test of remote sensing systems provided by the 1971 corn blight watch experiment. The general data analysis techniques and, particularly, the statistical multispectral pattern recognition methods for automatic computer analysis of aircraft scanner data are described. Some of the results obtained are examined, and the implications of the experiment for future data communication requirements of earth resource survey systems are discussed.

Anuta, P. E.↗
NASA NTRS1991

Automated target recognition and tracking using an optical pattern recognition neural network

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Chao, Tien-Hsin↗
NASA NTRS1971

Mapping soil types from multispectral scanner data.

Multispectral remote sensing and computer-implemented pattern recognition techniques were used for automatic ?mapping' of soil types. This approach involves subjective selection of a set of reference samples from a gray-level display of spectral variations which was generated by a computer. Each resolution element is then classified using a maximum likelihood ratio. Output is a computer printout on which the researcher assigns a different symbol to each class. Four soil test areas in Indiana were experimentally examined using this approach, and partially successful results were obtained.

Kristof, S. J.↗