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Showing papers on "Feature vector published in 1977"


Journal ArticleDOI
01 May 1977
TL;DR: The application of pattern recognition to the classification of normal and four pathological gaits is described and potential clinical applications of the results are discussed.
Abstract: The application of pattern recognition to the classification of normal and four pathological gaits is described. The classification is based on construction of a pattern feature vector whose elements are obtained by processing EMG signals obtained from 6 muscles responsible for movement of the foot at the ankle. The paper describes the basic actions of these muscles and the resulting gait patterns. Data were obtained from 30 patients at Rancho Los Amigos Hospital. 11 sets of data were used as training patterns for the synthesis of linear dicriminant functions needed in the classification algorithm. The resulting algorithm was used to proces 19 patient records. The correct gait was identified in 15 of these 19 sets. Potential clinical applications of the results are discussed.

54 citations


Journal ArticleDOI
TL;DR: The correspondence discusses the relationship of the discriminant vector method of feature selection and the method of Kittler and Young, showing that the latter method is, from the point of view of dimensionality reduction, more powerful and also computationally more efficient.
Abstract: The correspondence discusses the relationship of the discriminant vector method of feature selection [1] and the method of Kittler and Young [5]. Although both methods determine the feature space coordinate axes by maximizing the generalized Fisher criterion of discriminatory power, with the exception of two class case the resulting feature spaces are considerably different because of the difference in the constraints imposed on the axes by individual methods. It is shown that the latter method is, from the point of view of dimensionality reduction, more powerful and also computationally more efficient.

22 citations


Proceedings ArticleDOI
09 May 1977
TL;DR: In this paper a parametric technique for speech segmentation into voiced, unvoiced and silence intervals is described, based on the statistical properties of parameters which define a four dimensional pattern space.
Abstract: In this paper a parametric technique for speech segmentation into voiced, unvoiced and silence intervals is described. This technique is based on the statistical properties of parameters which define a four dimensional pattern space. This space is spanned by the magnitude and the zero-crossing rate of the speech waveform and the normalized magnitude and the zero-crossing rate of the speech difference signal. A method is presented for the evaluation of the discriminating power of each parameter and the selection of the features for the decision criterion. The partition of the feature space is performed by discriminant functions parallel to the axes with a probability of misclassification less than 5%. The performance of this pattern classifier is evaluated and compared to that obtained by a reference criterion essentially based on the properties of the autocorrelation function.

9 citations


01 May 1977
TL;DR: This paper examines several image segmentation algorithms which have been explored in the development of the VISIONS system and shows that the interaction between these two representations of data provides a view that is lacking in either.
Abstract: : This paper examines several image segmentation algorithms which have been explored in the development of the VISIONS system. Each of these algorithms can be viewed as a variation on a basic theme: the clustering of activity in feature space via histogram analysis, mapping these clusters back onto the image, and then isolating regions by analysis of the spatial relationships of the cluster labels. It is shown that the interaction between these two representations of data (global feature information and spatial information) provides a view that is lacking in either. The scene segmentation algorithms contain the following stages: (1) PLAN: reduce the amount of detail in the scene to a bare minimum by performing a fast simple segmentation into primary areas using spatial and/or quantization compression. (2) REFINE: resegment the scene with careful attention directed to the textural complexities of each region. The primitive transformations which are used include histogram clustering, region growing, data reduction by narrowing the quantization range, and/or data reduction by spatially collapsing the data while extracting features. These algorithms have been implemented using a parallel, hierarchical computational structure. Comparisons of performance on several images are given. (Author)

7 citations


Journal Article
TL;DR: The paper discusses the problem of representation of multivariate cell data sets in two dimensions such that the essence of the situation as represented by the multivariate feature space is preserved.
Abstract: The paper discusses the problem of representation of multivariate cell data sets in two dimensions such that the essence of the situation as represented by the multivariate feature space is preserved. A corresponding projectional technique has been developed and illustrated on a set of five different cell types of ectocervical cells, including normal and carcinoma cells.

6 citations