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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 307 records · Page 17

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NASA NTRS1994

Photonics: From target recognition to lesion detection

Since 1989, Martin Marietta has invested in the development of an innovative concept for robust real-time pattern recognition for any two-dimensioanal sensor. This concept has been tested in simulation, and in laboratory and field hardware, for a number of DOD and commercial uses from automatic target recognition to manufacturing inspection. We have now joined Rose Health Care Systems in developing its use for medical diagnostics. The concept is based on determining regions of interest by using optical Fourier bandpassing as a scene segmentation technique, enhancing those regions using wavelet filters, passing the enhanced regions to a neural network for analysis and initial pattern identification, and following this initial identification with confirmation by optical correlation. The optical scene segmentation and pattern confirmation are performed by the same optical module. The neural network is a recursive error minimization network with a small number of connections and nodes that rapidly converges to a global minimum.

Henry, E. Michael↗

NASA NTRS1992

A neural network prototyping package within IRAF

We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do.

Bazell, D.↗

NASA NTRS1987

4-D display of satellite cloud images

A technique has been developed to display GOES satellite cloud images in perspective over a topographical map. Cloud heights are estimated using temperatures from an infrared (IR) satellite image, surface temperature observations, and a climatological model of vertical temperature profiles. Cloud levels are discriminated from each other and from the ground using a pattern recognition algorithm based on the brightness variance technique of Coakley and Bretherton. The cloud regions found by the pattern recognizer are rendered in three-dimensional perspective over a topographical map by an efficient remap of the visible image. The visible shades are mixed with an artificial shade based on the geometry of the cloud-top surface, in order to enhance the texture of the cloud top.

Hibbard, William L.↗

NASA NTRS1970

The application of automatic recognition techniques in the Apollo 9 SO-65 experiment

A synoptic feature analysis is reported on Apollo 9 remote earth surface photographs that uses the methods of statistical pattern recognition to classify density points and clusterings in digital conversion of optical data. A computer derived geological map of a geological test site indicates that geological features of the range are separable, but that specific rock types are not identifiable.

Macdonald, R. B.↗

NASA NTRS1975

Landmark tracking technology

This paper summarizes the justification and technical approach of an experimental plan for landmark recognition and tracking from the Advanced Technology Laboratory (ATL). The primary experimental approach is that of near real-time pattern recognition by means of coherent optical techniques. An experiment development plan would lead to a flight test in 1981.

Welch, J. D.↗

NASA NTRS1991

Collected Notes on the Workshop for Pattern Discovery in Large Databases

These collected notes are a record of material presented at the Workshop. The core data analysis is addressed that have traditionally required statistical or pattern recognition techniques. Some of the core tasks include classification, discrimination, clustering, supervised and unsupervised learning, discovery and diagnosis, i.e., general pattern discovery.

Buntine, Wray↗

NASA NTRS1995

Geophysical phenomena classification by artificial neural networks

Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

Gough, M. P.↗

NASA NTRS1986

A variational assimilation method for satellite and conventional data: Development of basic model for diagnosis of cyclone systems

A three-dimensional diagnostic model for the assimilation of satellite and conventional meteorological data is developed with the variational method of undetermined multipliers. Gridded fields of data from different type, quality, location, and measurement source are weighted according to measurement accuracy and merged using least squares criteria so that the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation are satisfied. The model is used to compare multivariate variational objective analyses with and without satellite data with initial analyses and the observations through criteria that were determined by the dynamical constraints, the observations, and pattern recognition. It is also shown that the diagnoses of local tendencies of the horizontal velocity components are in good comparison with the observed patterns and tendencies calculated with unadjusted data. In addition, it is found that the day-night difference in TOVS biases are statistically different (95% confidence) at most levels. Also developed is a hybrid nonlinear sigma vertical coordinate that eliminates hydrostatic truncation error in the middle and upper troposphere and reduces truncation error in the lower troposphere. Finally, it is found that the technique used to grid the initial data causes boundary effects to intrude into the interior of the analysis a distance equal to the average separation between observations.

Achtemeier, G. L.↗

NASA NTRS1989

Autonomous star referenced attitude determination

A general star pattern recognition algorithm has been used to develop JPL's redundant, microcomputer-equipped ASTROS II CCD star tracker into a full-sky autonomous star tracker (FAST), capable of determining its attitude about all three axes without requiring any a priori attitude knowledge. A large field of view allows the number of guide stars in the all-sky data-base of the tracker to be limited to a manageable number, while high accuracy ensures that the pattern formed by the observed guide stars is unique. The recognition algorithm can also be used for automating the acquisition of celestial targets by astronomy telescopes, for autonomously updating the attitude of gyro-based attitude control systems, and for automating ground-based attitude recognition. Using both Monte Carlo simulations and a quasi-analytical method, it is shown that the general recognition algorithm and a less software-intensive special algorithm can be used to reliably automate the acquisition of celestial targets by astronomy telescopes.

Van Bezooijen, R. W. H.↗

NASA NTRS1989

Automatic voice recognition using traditional and artificial neural network approaches

The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

Botros, Nazeih M.↗

NASA NTRS1993

Prediction-based dynamic load-sharing heuristics

The authors present dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30 percent better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50 percent improvements relative to their nonprediction counterpart.

Goswami, Kumar K.↗

NASA NTRS1988

Application of optical correlation techniques to particle imaging velocimetry

Pulsed laser sheet velocimetry yields nonintrusive measurements of velocity vectors across an extended 2-dimensional region of the flow field. The application of optical correlation techniques to the analysis of multiple exposure laser light sheet photographs can reduce and/or simplify the data reduction time and hardware. Here, Matched Spatial Filters (MSF) are used in a pattern recognition system. Usually MSFs are used to identify the assembly line parts. In this application, the MSFs are used to identify the iso-velocity vector contours in the flow. The patterns to be recognized are the recorded particle images in a pulsed laser light sheet photograph. Measurement of the direction of the partical image displacements between exposures yields the velocity vector. The particle image exposure sequence is designed such that the velocity vector direction is determined unambiguously. A global analysis technique is used in comparison to the more common particle tracking algorithms and Young's fringe analysis technique.

Wernet, Mark P.↗

NASA NTRS1988

Application of optical correlation techniques to particle imaging

Pulsed laser sheet velocimetry yields noninstrusive measurements of velocity vectors across an extended 2-dimensional region of the flow field. The application of optical correlation techniques to the analysis of multiple exposure laser light sheet photographs can reduce and/or simplify the data reduction time and hardware. Here, Matched Spatial Filters (MSF) are used in a pattern recognition system. Usuallay MSFs are used to identify the assembly line parts. In this application, the MSFs are used to identify the iso-velocity vector contours in the flow. The patterns to be recognized are the recorded particle images in a pulsed laser light sheet photograph. Measurement of the direction of the particle image displacements between exposures yields the velocity vector. The particle image exposure sequence is designed such that the velocity vector direction is determined unambiguously. A global analysis technique is used in comparison to the more common particle tracking algorithms and Young's fringe analysis technique.

Wernet, Mark P.↗

NASA NTRS1984

The spatial structure of terrain - A process signal in satellite digital images

Pattern recognition procedures applied to Landsat imagery carry an implicit assumption that the digital data are independently distributed. That assumption is incorrect over virtually any terrain. Deviations from independence occur because slopes follow a systematic pattern of variation arising from the slope-forming processes. That pattern can be identified using the stochastic process methodology of Box and Jenkins.Angles of adjacent slopes are autocorrelated and the bidirectional reflectance function transfers these systematic slope changes to the sensor. Imagery becomes autocorrelated through this transfer. Autocorrelation in the imagery can be removed through direct calculation from a digital elevation model or by use of stochastic process methodology. The latter has the advantage that the residuals are white noise; and it is applicable in any area, even where a D.E.M. is unavailable. The stochastic process signal can be used to study terrain processes.

Craig, R. G.↗

NASA NTRS1989

The development of two-dimensional object identification techniques

This report marks the end of the first year of an anticipated three year effort to study methods for numerically identifying objects according to shape in two dimensions. The method is based upon comparing the unit gradient of an observed object and the unit gradient of a standard object over a specified range of points. The manner in which the gradients are compared forms the basis of a shape recognition scheme, which is then applied to simple closed plane figures. The gradient based method is calibrated by using various distorted objects in comparison with a set of standard reference objects. The use of pattern recognition techniques for computer identification of two-dimensional figures will be investigated during the second and third years of this project.

Lebby, Gary↗

NASA NTRS2004

Segmenting Images for a Better Diagnosis

NASA's Hierarchical Segmentation (HSEG) software has been adapted by Bartron Medical Imaging, LLC, for use in segmentation feature extraction, pattern recognition, and classification of medical images. Bartron acquired licenses from NASA Goddard Space Flight Center for application of the HSEG concept to medical imaging, from the California Institute of Technology/Jet Propulsion Laboratory to incorporate pattern-matching software, and from Kennedy Space Center for data-mining and edge-detection programs. The Med-Seg[TM] united developed by Bartron provides improved diagnoses for a wide range of medical images, including computed tomography scans, positron emission tomography scans, magnetic resonance imaging, ultrasound, digitized Z-ray, digitized mammography, dental X-ray, soft tissue analysis, and moving object analysis. It also can be used in analysis of soft-tissue slides. Bartron's future plans include the application of HSEG technology to drug development. NASA is advancing it's HSEG software to learn more about the Earth's magnetosphere.

Source record↗

NASA NTRS1972

Automated thematic mapping and change detection of ERTS-A images

The author has identified the following significant results. For the exploitation of the ERTS-1 images, both multispectral and spatial pattern recognition techniques are important. A multispectral recognition software program was successfully tested. A technique for the generation of resource boundaries has been successfully combined with terrain recognition software resulting in a regionalized terrain classification system. Digital registration of multispectral images has been achieved. Also, photointerpretation results from one test site are reported and the development of spatial terrain signatures from optical diffraction patterns is described.

Gramenopoulos, N.↗

NASA NTRS1986

A multifunction recognition operator for telerobotic vision

Research on developing an operator capable of performing the various subtasks required of a telerobot's vision sensor is reported. The operator uses a goal-driven matching technique which is an application of a linear programming method that readily adapts to the elastic template matching approach to pattern recognition. Four applications of the operator are discussed: (1) three-space location of an isolated object; (2) shape determination of isolated planar figures; (3) image compression/restoration; and (4) shape decomposition.

Goode, P. W., IV↗