A method of supervised pattern recognition by an adaptive hypersphere decision threshold

In this study the Bayes likelihood detector is combined with an adaptive decision threshold classifier to solve the multicategory pattern recognition problem. It is assumed that the pattern classes can be represented by an n-dimensional vector sample taken from a multivariate gaussian probability distribution. This study presents (1) the derivation of the A̲daptive H̲ypersphere D̲ecision T̲hreshold classifier (AHDT classifier) and shows (2) how the AHDT classifier minimizes the probability of error using the learning patterns. Finally the AHDT classifier is applied to the solution of a physical problem through computer simulation --Abstract, page ii

Program planning, chapter 5, part D

Recommendations for future activities necessary to support satellite microwave sensing are reported. Areas covered include component development, data processing, calibration, design and fabrication of multifrequency systems, and experimental test programs to establish interactions of electromagnetic waves and sensed parameters

Investigations in adaptive processing of multispectral data

Adaptive data processing procedures are applied to the problem of classifying objects in a scene scanned by multispectral sensor. These procedures show a performance improvement over standard nonadaptive techniques. Some sources of error in classification are identified and those correctable by adaptive processing are discussed. Experiments in adaptation of signature means by decision-directed methods are described. Some of these methods assume correlation between the trajectories of different signature means; for others this assumption is not made

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 391)

This bibliography lists 75 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System during Aug. 1994. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Learning probability spaces for classification and recognition of patterns with or without supervision

Learning probability spaces for classification and recognition of patterns with or without supervisio

On pattern classification algorithms - Introduction and survey

Pattern recognition algorithms, and mathematical techniques of estimation, decision making, and optimization theor

A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

Investigation related to multispectral imaging systems

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community

Introduction to computer image processing

Theoretical backgrounds and digital techniques for a class of image processing problems are presented. Image formation in the context of linear system theory, image evaluation, noise characteristics, mathematical operations on image and their implementation are discussed. Various techniques for image restoration and image enhancement are presented. Methods for object extraction and the problem of pictorial pattern recognition and classification are discussed

A comparative study of nonparametric methods for pattern recognition

The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is shown graphically for differences in modes and/or means of the probability density functions for four, eight and sixteen samples. The K-class classifier performed very well with respect to the other classifiers used. Since the K-class classifier is a nonparametric technique, it usually performed better than the Bayes classifier which assumes the data to be Gaussian even though it may not be. The K-class classifier has the advantage over the Bayes in that it works well with non-Gaussian data without having to determine the probability density function of the data. It should be noted that the data in this experiment was always unimodal