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At least 649 records · Page 36

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NASA NTRS1990

The spectral classification of chromospherically active binary stars with composite spectra

This paper presents and analyzes blue and red-wavelength high-resolution spectra of twelve chromospherically active binary or triple systems with composite spectra. Spectral classifications for the individual stellar components are derived by means of the 'spectrum-synthesis' technique and are compared to stellar evolutionary models and observed masses and/or mass ratios. Also presented is a carefully selected set of MK reference stars of luminosity class III, IV, and V, and spectral type A9-K5, and v sin i less than 10 km/s, to cover the spectral range of the components of chromospherically active binary systems of the RS CVn-type. New values of v sin i are determined for some of the reference and program stars. Two spectroscopic binaries have been discovered.

Strassmeier, K. G.↗

NASA NTRS1990

General method of pattern classification using the two-domain theory

Human beings judge patterns (such as images) by complex mental processes, some of which may not be known, while computing machines extract features. By representing the human judgements with simple measurements and reducing them and the machine extracted features to a common metric space and fitting them by regression, the judgements of human experts rendered on a sample of patterns may be imposed on a pattern population to provide automatic classification.

Rorvig, Mark E.↗

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 NTRS1990

Neural network classification - A Bayesian interpretation

The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical framework.

Wan, Eric A.↗

NASA NTRS1991

Data space volumes and classification optimization of SPOT and Landsat TM data

In order to compare the data space volume of SPOT XS and Landsat TM images, three data sets, i.e., a wetlands/agricultural data set, an agricultural data set, and a forest data set, are examined. The comparisons are made for the same geographic area. The data space volumes for Landsat TM (2, 3, and 4) are found to be 70 to 100 percent larger than the volumes for the SPOT XS images. It is suggested that the additional midinfrared bands contribute to the difference in data space volumes between Landsat TM and SPOT XS. The data space volumes for Landsat TM bands 3, 4, and 5 are more than an order of magnitude greater than the volumes for the three band SPOT XS data sets. The volumes of the six-band Landsat TM images are four orders of magnitude greater than the SPOT XS. The analysis of the data space volumes is used to optimize the computation time and minimize the storage requirements of a maximum-likelihood classification based on a look-up table.

Ahearn, Sean C.↗

NASA NTRS1989

Knowledge acquisition from natural language for expert systems based on classification problem-solving methods

It is shown how certain kinds of domain independent expert systems based on classification problem-solving methods can be constructed directly from natural language descriptions by a human expert. The expert knowledge is not translated into production rules. Rather, it is mapped into conceptual structures which are integrated into long-term memory (LTM). The resulting system is one in which problem-solving, retrieval and memory organization are integrated processes. In other words, the same algorithm and knowledge representation structures are shared by these processes. As a result of this, the system can answer questions, solve problems or reorganize LTM.

Gomez, Fernando↗

NASA NTRS1992

Classification Of Land Cover From Airborne MSS Data

Method for processing images of rural uplands produced by airborne multispectral scanner (MSS), semiautomatically classifies types of land cover, and involves selection of wavelength bands, radiometric calibration, correction for effects of scan angle and atmosphere, training, and assessment of accuracy. Basic version involves classification of each picture element according to spectrum. Augmented with five refinements to increase accuracy: per-field sampling; low-pass filtering; image texture; prior probabilities; and imagery from two dates.

Curran, Paul J.↗

NASA NTRS1992

Neural network classification of questionable EGRET events

High energy gamma rays (greater than 20 MeV) pair producing in the spark chamber of the Energetic Gamma Ray Telescope Experiment (EGRET) give rise to a characteristic but highly variable 3-D locus of spark sites, which must be processed to decide whether the event is to be included in the database. A significant fraction (about 15 percent or 10(exp 4) events/day) of the candidate events cannot be categorized (accept/reject) by an automated rule-based procedure; they are therefore tagged, and must be examined and classified manually by a team of expert analysts. We describe a feedforward, back-propagation neural network approach to the classification of the questionable events. The algorithm computes a set of coefficients using representative exemplars drawn from the preclassified set of questionable events. These coefficients map a given input event into a decision vector that, ideally, describes the correct disposition of the event. The net's accuracy is then tested using a different subset of preclassified events. Preliminary results demonstrate the net's ability to correctly classify a large proportion of the events for some categories of questionables. Current work includes the use of much larger training sets to improve the accuracy of the net.

Meetre, C. A.↗

NASA NTRS1991

Autoclass: An automatic classification system

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.

Stutz, John↗

NASA NTRS1992

Singularity classification as a design tool for multiblock grids

A major stumbling block in interactive design of 3-D multiblock grids is the difficulty of visualizing the design as a whole. One way to make this visualization task easier is to focus, at least in early design stages, on an aspect of the grid which is inherently easy to present graphically, and to conceptualize mentally, namely the nature and location of singularities in the grid. The topological behavior of a multiblock grid design is determined by what happens at its edges and vertices. Only a few of these are in any way exceptional. The exceptional behaviors lie along a singularity graph, which is a 1-D construct embedded in 3-D space. The varieties of singular behavior are limited enough to make useful symbology on a graphics device possible. Furthermore, some forms of block design manipulation that appear appropriate to the early conceptual-modeling phase can be accomplished on this level of abstraction. An overview of a proposed singularity classification scheme and selected examples of corresponding manipulation techniques is presented.

Jones, Alan K.↗

NASA NTRS1990

Myths and legends in learning classification rules

A discussion is presented of machine learning theory on empirically learning classification rules. Six myths are proposed in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, universal learning algorithms, and interactive learning. Some of the problems raised are also addressed from a Bayesian perspective. Questions are suggested that machine learning researchers should be addressing both theoretically and experimentally.

Buntine, Wray↗

NASA NTRS1991

Learning classification trees

Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

Buntine, Wray↗

NASA NTRS1991

Bayesian classification theory

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit or share model parameters though a class hierarchy. We summarize the mathematical foundations of AutoClass.

Hanson, Robin↗

NASA NTRS1990

Tracking and letter classification under dichoptic and binocular viewing conditions

Subjects were required to fly a simulated helicopter path, while also classifying letter pairs presented intermitently at 5 retinal locations. Binocular and Dichoptic conditions were compared by employing color filters. Tracking under dichoptic conditions was strongly influenced by the absence of a common optical axis. Classification performance also deteriorated and was influenced by the conditions of tracking.

Gopher, Daniel↗

NASA NTRS1991

The lower subsidiary diffuse plasma resonances and the classification of radio emissions below the plasma frequency

Using previusly published data and newly scaled ionograms from the Alouette 2 and ISIS 1 experiments, the diffuse ionospheric resonances Dn, stimulated by topside sounders Dn, are classified. The classification also includes the lower subsidiary resonances Dn(-) (n = 1, 2, 3, and 4). It is shown that the Dn(-) frequencies are related to f(Dn) and f(H) by the expression f(Dn)- = sq rt of (f(Dn)-squared - f(H)-squared).

Osherovich, Vladimir A.↗

NASA NTRS1991

Classification-based reasoning

A representation formalism for N-ary relations, quantification, and definition of concepts is described. Three types of conditions are associated with the concepts: (1) necessary and sufficient properties, (2) contingent properties, and (3) necessary properties. Also explained is how complex chains of inferences can be accomplished by representing existentially quantified sentences, and concepts denoted by restrictive relative clauses as classification hierarchies. The representation structures that make possible the inferences are explained first, followed by the reasoning algorithms that draw the inferences from the knowledge structures. All the ideas explained have been implemented and are part of the information retrieval component of a program called Snowy. An appendix contains a brief session with the program.

Gomez, Fernando↗

NASA NTRS1992

Classification of mafic clasts from mesosiderites - Implications for endogenous igneous processes

Results are presented from an analysis of 13 igneous pebbles from the Vaca Muerta, EET87500, and Bondoc mesosiderites, using electron microprobe and instrumental neutron activation techniques. These data, combined with literature data on compositions of 43 mesosiderite clasts were used to compile a classification scheme for the various types of mafic silicate clasts that occur in mesosiderites. These clasts were classified into five principal groups: (1) polygenic and monogenic cumulates (30 percent); (2) polygenic basalts (30 percent); (3) quench-textured rocks, comprising two compositional subgroups (those which resemble basaltic eucrites (5 percent), and those which resemble cumulate eucrites (2 percent)); (4) monogenic basalts (11 percent); and (5) ultramafic rocks, consisting mainly of large crystals of orthopyroxene (9 percent) or olivine (4 percent). The conditions under which these clasts were formed are discussed.

Rubin, Alan E.↗

NASA NTRS1992

High-resolution space-shuttle polarimetry for farm crop classification

A significant advance is reported in imaging the polarimetry of a terrestrial area of earth located along the Mississippi River near New Madrid, Missouri. Color imagery was obtained with twin Hasselblad cameras with mutually perpendicular polarization analyzers. Digitization of the imagery in three colors (red, green, and blue) was accomplished at the Johnson Space Center Video Digital Analysis System Laboratory, Houston, Texas. A ground resolution of 80-90 m was achieved in the high-resolution imagery. Percent polarization was superior to photometry for recognition and characterization of farm crops such as rice, milo, cotton, and soybeans and of fallow areas. Statistical analyses of the percent-polarization data permit a unique classification of crops. Atmospheric effects may be deduced. Space-shuttle window distortion and viewing angle-sun geometry must be taken into account in analyzing the data.

Egan, Walter G.↗