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A novel approach to action recognition in video based on the analysis of optical flow is presented. Properties of optical flow useful for action recognition are captured using only the empirical covariance matrix of a bag of features such as flow velocity, gradient, and divergence. The feature covariance matrix is a low-dimensional representation of video dynamics that belongs to a Riemannian manifold...
This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the boosting framework. Sigma set encodes second order statistics of an image region implicitly in the form of a point set. Compared with the covariance matrix, the traditional second order statistics based region descriptor, which requires computationally demanding operations based...
An approach is proposed to extend bilateral filtering to the vector case so as to simultaneously take spectral and spatial information into account by using spectral distances and multivariate Gaussian functions. To simplify the determination of the parameters of the corresponding covariance matrix, the data vectors are transformed to eigenspace through principal component analysis (PCA). By locally...
This paper presents a novel approach of feature selection based on analysis of covariance matrix of training patterns, a correlation-based feature selection method is put forward. An objective measure is proposed and defined. It is shown that for a given set of features, a subset of features that has the highest sum of the correlation coefficients has the tendency to be reduced, if it meets the requirement...
This paper presents a new face recognition method based on Two-Dimensional Principal Component Analysis (2DPCA) and Gabor filters. In the method, an original image is convolved with 40 Gabor filters corresponding to various orientations and scales to give its Gabor representation. Then, the Gabor representation is analyzed by the 2DPCA in which the eigenvectors are computed using the Gabor image covariance...
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