Showing papers on "Feature vector published in 1973"

An integrated non-parametric sequential approach to multi-class pattern classification

Abstract: A now method of pattern classification, evolved by integrating in a sequential mode a non-parametric feature selection criterion with an explicit learning scheme, is presented. This feature selection criterion is based on the well-known concept of inter-class and intra-class Euclidean distances as a measure of the separability of the pattern classes in a given feature space. An ‘ effective figure of merit’ is denned and the feature subset in which this figure of merit attains the maximum value is construed as the best feature subset. Usually in most of the existing techniques, a single feature subset is chosen as the hest for the multi-class problem as a whole. A distinctive departure from this practice has been made here in that an individual best feature subset is determined for each of the pattern classes. The values of the effective figure of merit for the best feature subsets of the different pattern classes are sorted to determine the best separable pattern class. The learning scheme developed here ...

Automated thematic mapping and change detection of ERTS-1 images

Abstract: The author has identified the following significant results. For the recognition of terrain types, spatial signatures are developed from the diffraction patterns of small areas of ERTS-1 images. This knowledge is exploited for the measurements of a small number of meaningful spatial features from the digital Fourier transforms of ERTS-1 image cells containing 32 x 32 picture elements. Using these spatial features and a heuristic algorithm, the terrain types in the vicinity of Phoenix, Arizona were recognized by the computer with a high accuracy. Then, the spatial features were combined with spectral features and using the maximum likelihood criterion the recognition accuracy of terrain types increased substantially. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. Nonlinear transformations of the feature vectors are required so that the terrain class statistics become approximately Gaussian. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month but vary substantially between seasons.