Showing papers on "Feature vector published in 1979"

A composite classifier system design: Concepts and methodology

Abstract: This study explores the scope for achieving enhanced recognition system performance through deployment of a composite classifier system consisting of two or more component classifiers which belong to different categories. The domains of deployment of these individual components (classifiers) are determined by optimal partitioning of the problem space. The criterion for such optimal partitioning is determined in each case by the characteristics of the classifier components. An example, in terms of partitioning the feature space for optimal deployment of a composite system consisting of the linear and nearest neighbor (NN) classifiers as its components, is presented to illustrate the concepts, the associated methodology, and the possible benefits one could expect through such composite classifier system design. Here, the optimality of the partitioning is dictated by the linear class separability limitation of the linear classifier and the computational demand characteristics of the NN classifier. Accordingly, the criterion for the optimal feature space partitioning is set to be the minimization of the domain of application of the NN classifier, subject to the constraint that the linear classifier is to be deployed only in regions satisfying the underlying assumption of linear separability of classes. While many alternatives are available for the solution of the resulting constrained optimization problem, a specific technique-Sequential Weight Increasing Factor Technique (SWIFT)- was employed here for convenience in view of previous successful experience with this technique in other application areas. Numerical results derived using the well-known IRIS data set are furnished to demonstrate the effectiveness of the new concepts and methodology.

Thresholding Using the Isodata Clustering Algorithm

Abstract: : A recently proposed iterative thresholding scheme turns out to be essentially the well-known ISODATA clustering algorithm, applied to a one- dimensional feature space (the sole feature of a pixel is its gray level). We prove that in one dimension, ISODATA always converges. We also apply it to requantize images into specified numbers of gray levels.

Device for recognizing an input pattern with approximate patterns used for reference patterns on mapping

Abstract: In a speech recognition system which time-normalizes (i.e. aligns by time-warping) each pre-stored reference pattern of feature vectors b j before comparison with the input signal pattern vectors a i , an improved time-alignment technique requiring less calculation, by selecting "standard" vectors v m (j) which approximate the reference vectors thereby simplifying the derivation of the time-warp mapping function j=j(i).

Pattern recognition with a warping function decided for each reference pattern by the use of feature vector components of a few channels

Abstract: In a pattern recognition device according to pattern matching, one or more specific dimensions of vector components are memorized for each reference pattern feature vector sequence in a reference pattern memory for the reference pattern feature vector sequences. A warping function for time-normalizing input pattern feature vectors of a sequence and the vectors of each reference pattern feature vector sequence is determined so as to minimize the difference between a pattern represented by the specific vector components of the specific dimension or dimensions and another pattern represented by the vector components corresponding in the input pattern feature vector sequence to the specific reference pattern feature vector components as regards the dimensions of a space in which each input or reference pattern feature vector is defined. The input pattern feature vector sequence and each reference pattern feature vector sequence are subjected to nonlinear pattern matching with reference to the warping function. The pattern matching may be between the vector components of all dimensions or those of several dimensions including the specific dimension or dimensions. Preferably, one or more dimensions are specified as the specific one or ones by selecting each dimension for which a variation with time of a pattern represented by the reference pattern feature vector components is a maximum of similar variations of patterns represented by the vector components of other dimensions.

Spike Recognition and On-Line Classification by Unsupervised Learning System

Abstract: An on-line spike recognition system allows separation of multiple spikes present on a single channel, in up to six different classes. The learning phase is unsupervised, and uses the data samples of the waveform as coordinates in a multidimensional feature space. Additional signal characteristics may improve the system performance in special cases. Using the well known nearest neighbor technique, all possible cluster configurations are determined. From this analysis, the investigator selects the physiologically best suited duster layout, primary based on a curve showing the number of clusters versus the maximum distance of two neighboring spikes in the same cluster. This procedure is supported by visual examination of the spikes of each cluster. Statistics are calculated for inter-and intracluster distances, yielding confidence limits for the cluster bounds, and estimates for the quality of separation. During the classification phase, a separate graphic display processor permits continuous control without delay. Each classified spike is projected over its cluster, identifying mean waveform.

A Syntactic-Semantic Approach to Image Understanding and Creation

Abstract: A syntactic-semantic approach to information extraction from images is described. The methodology involves the injection of semantic considerations into context-free grammars. The semantic considerations include feature vectors, selection restrictions, feature transfer functions, semantic well-formedness, etc. With such injection, we can make a description scheme which carries the numerical, the structural, and the a prior real world knowledge about the pattern we want to extract. From the description we can construct an analytical mechanism, the creation machine, which wil find the desired pattern amid a chaos of noisy primitives.

Method and apparatus for character reading

Abstract: A method for character reading requiring no character segmentation, which method effects required character recognition by the steps of subjecting a given string of character patterns to continuous scanning to produce either a local feature vector at each of the intersections of rows and columns of character patterns or a global feature vector for each of the columns formed in consequence of the scanning, linearly consolidating either or both of the feature vectors to obtain a lower dimensional vector in new feature axes and continuously matching the vectors with the standard ones set in advance.

Recent Advances in Syntactic Pattern Recognition

Abstract: The many different mathematical techniques used to solve pattern recognition problems may be grouped into two general approaches [1,2]. They are the decision-theoretic (or discriminant) approach and the syntactic (or structural) approach [3]. In the decision-theoretic approach, a set of characteristic measurements, called features, are extracted from the patterns. Each pattern is represented by a feature vector, and the recognition of each pattern is usually made by partitioning the feature space. On the other hand, in the syntactic approach, each pattern is expressed as a composition of its components, called sub-patterns and pattern primitives. This approach draws an analogy between the structure of patterns and the syntax of a language. The recognition of each pattern is usually made by parsing the pattern structure according to a given set of syntax rules. In this paper, we briefly review the recent progress in syntactic pattern recognition and some of its applications.

Multiclass Bayes error estimation by a feature space sampling technique

Abstract: A general Gaussian M-class N-feature classification problem is defined. An algorithm is developed that requires the class statistics as its only input and computes the minimum probability of error through use of a combined analytical and numerical integration over a sequence simplifying transformations of the feature space. The results are compared with those obtained by conventional techniques applied to a 2-class 4-feature discrimination problem with results previously reported and 4-class 4-feature multispectral scanner Landsat data classified by training and testing of the available data.

Application of a Frequency Domain Prony Method to Wide Bandwidth Radar Signature Classification

Abstract: : A frequency domain Prony approach is presented for extracting features of return signals from targets illuminated by wide bandwidth (short pulse) radar Theoretical details pertaining to this approach are described in a separate paper The features mentioned above consist of the relative delays and reflection coefficients pertaining to scattering centers on the target representing differently shaped regions on the target surface The dimensionality of the feature vectors thus constructed is very low (less than ten) Moreover, when used in the classification of targets by a nearest neighbor classification strategy, such feature vectors permit accurate discrimination between targets that do not differ much in shape, and also they are in a large measure insensitive to noise The above results were corroborated by computer simulations performed on the data base created by the coherent X-band short pulse (05 nanosecond) radar at the Fort Worth Operation Radar Range of General Dynamics Convair Aerospace Division The three objects on which extensive simulations were carried out were an AGENA space vehicle with rectangular cross-sectional first stage, an AGENA space vehicle with cylindrical cross-sectional first stage, and an AGENA space vehicle payload The results of these simulations, which were rather successful, are discussed in detail