Showing papers on "Feature vector published in 1974"

Fuzzy Notions in Nonlinear System Classification

Abstract: Pattern Recognition methods like the A-nearest neighbor technique and clustering have been successfully used to classify nonlinear systems to a predetermined set of well separated classes, demonstrating similarities in their input-output behavior. The cross-correlation function has been used as a feature vector which yielded a classification with a relatively small misclassification error. Further categorization of the nonlinearity within a class is not possible through the above approach. However, a membership factor in a fuzzy sense can be assigned to each nonlinear system within a certain class. This factor is dependent on the coefficients of the polynomial expansion of the nonlinearity involved and may be obtained through the cross-correlation vector. This way, every class of nonlinear systems may be defined as a fuzzy set. The investigation of the properties of such a set as well as its contribution to the further classification of nonlinear systems is discussed in this paper.

A Binary Feature Extraction Technique

Abstract: Numerous schemes are available for feature selection in a pattern recognition problem, but the feature extraction process is largely intuitive. A sequential feature extraction scheme is proposed for binary features. A decision function, which is linear and near optimal, is developed concurrently with each feature. Performance bounds are developed for several design strategies. Experimental results are given to illustrate the use of the scheme and the effectiveness of the performance bounds.

Computer Recognition of Facial Profiles

Abstract: : A system for the recognition of human faces from full profile silhouettes is described. The system is adaptively trained using a novel stack- oriented training procedure which is shown to be quite effective in identifying those feature vectors which are of most importance in the recognition process. Thus the training procedure generally produces authority files having a small number of entries. The feature vectors used are generated from a normalized autocorrelation function expressed in polar coordinates. These feature vectors are shown to be more effective in the recognition process than are the moment invariant features, at least for this problem. Experiments are described in which the system attains a recognition accuracy of 90% in a 10 class problem using 12-dimensional circular autocorrelation feature vectors. It is shown, by further experiments, that these results are no worse than a human observer's accuracy.

Learning Texture Information from Singular Photographs and its Application in Digital Image Classification

Abstract: An area which has rapidly gained interest in the last few years is that of remote sensing by imagery.1, 2 Certainly the successful mission of the ERTS has contributed to an expanding interest and increased potential.3 The potential uses of such remotely sensed data cover a variety of applications, including land use studies, crop quality, accurate map making, and evaluating natural resources. In utilizing this data in a particular application, a significant problem is the amount of data that must be processed to obtain specific information or features pertinent to the application. This is inherent in image or visual information; when this is multiplied by a large number of the available images, the problem of obtaining details or small features from such images is significant. This is particularly true when some set of features is to be used in a parametric pattern recognition scheme. However, there are many instances when one is interested in identifying an area or region which may encompass many details, but is small compared with an overall image and represents a defined entity. Then, if one is interested in identifying or classifying such a region, its gross characteristics must be represented while ignoring small details that may vary significantly from one sample observation to another.