Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A method for facial recognition encode analysis comprises providing a training set of Gabor encoded arrays of face images from a database; and, for each encode array in the training set, evaluating the Gabor data to determine the accuracy of the fiducial points on which the encode array is based. The method also comprises training an outlier detection algorithm based on the evaluation of the encode arrays to obtain a decision function for a strength of accuracy of fiducial points in the encode arrays; and outputting the decision function for application to an encode array to be tested.

Abstract: A method for optimizing network design includes identifying a set of terminal-to-terminal shortest paths in a network, wherein a terminal-to-terminal shortest path is a best connection between two terminals, evaluating a terminal betweenness for each non-terminal vertex in the network, wherein the terminal betweenness of a vertex is a fraction of the total number of terminal-to-terminal shortest paths that include said vertex, calculating an average terminal betweenness for each terminal-to-terminal shortest path based on the terminal betweenness of the vertices in the path, iteratively adding the terminal-to-terminal shortest paths to an output graph in order of decreasing average terminal betweenness until all terminals are represented on the output graph, and using the output graph to design or adjust a network. The method may also include displaying the output graph to a user. A computer program product and computer system corresponding to the method are also disclosed.

Abstract: A method for optimizing network design includes identifying a set of terminal-to-terminal shortest paths in a network, wherein a terminal-to-terminal shortest path is a best connection between two terminals, evaluating a terminal betweenness for each non-terminal vertex in the network, wherein the terminal betweenness of a vertex is a fraction of the total number of terminal-to-terminal shortest paths that include said vertex, calculating an average terminal betweenness for each terminal-to-terminal shortest path based on the terminal betweenness of the vertices in the path, iteratively adding the terminal-to-terminal shortest paths to an output graph in order of decreasing average terminal betweenness until all terminals are represented on the output graph, and using the output graph to design or adjust a network. The method may also include displaying the output graph to a user. A computer program product and computer system corresponding to the method are also disclosed.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A method for facial recognition encode analysis comprises providing a training set of Gabor encoded arrays of face images from a database; and, for each encode array in the training set, evaluating the Gabor data to determine the accuracy of the fiducial points on which the encode array is based. The method also comprises training an outlier detection algorithm based on the evaluation of the encode arrays to obtain a decision function for a strength of accuracy of fiducial points in the encode arrays; and outputting the decision function for application to an encode array to be tested.

Abstract: A method for facial recognition encode analysis comprises providing a training set of Gabor encoded arrays of face images from a database; and, for each encode array in the training set, evaluating the Gabor data to determine the accuracy of the fiducial points on which the encode array is based. The method also comprises training an outlier detection algorithm based on the evaluation of the encode arrays to obtain a decision function for a strength of accuracy of fiducial points in the encode arrays; and outputting the decision function for application to an encode array to be tested.

Abstract: A method for facial recognition encode analysis comprises providing a training set of Gabor encoded arrays of face images from a database; and, for each encode array in the training set, evaluating the Gabor data to determine the accuracy of the fiducial points on which the encode array is based. The method also comprises training an outlier detection algorithm based on the evaluation of the encode arrays to obtain a decision function for a strength of accuracy of fiducial points in the encode arrays; and outputting the decision function for application to an encode array to be tested.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A computer-implemented method is provided for classifying an input signal against a set of pre-classified signals. A computer system may calculate, for each of one or more signals of the set of pre-classified signals, a parallelism value indicating a level of the parallelism between that signal and the input signal. The computer system may calculate, for a first subset of the set of pre-classified signals, a sparse vector, wherein each element of the sparse vector serves as a coefficient for a corresponding signal of the first subset. The computer system may determine, for each of the signals in the set of pre-classified signals, a similarity value indicating a level of similarity between that signal and the input signal.

Abstract: A method for facial recognition encode analysis comprises providing a training set of Gabor encoded arrays of face images from a database; and, for each encode array in the training set, evaluating the Gabor data to determine the accuracy of the fiducial points on which the encode array is based. The method also comprises training an outlier detection algorithm based on the evaluation of the encode arrays to obtain a decision function for a strength of accuracy of fiducial points in the encode arrays; and outputting the decision function for application to an encode array to be tested.

Abstract: A method for facial recognition encode analysis comprises providing a training set of Gabor encoded arrays of face images from a database; and, for each encode array in the training set, evaluating the Gabor data to determine the accuracy of the fiducial points on which the encode array is based. The method also comprises training an outlier detection algorithm based on the evaluation of the encode arrays to obtain a decision function for a strength of accuracy of fiducial points in the encode arrays; and outputting the decision function for application to an encode array to be tested.

Abstract: A computer-implemented method is provided for classifying an input signal against a set of pre-classified signals. A computer system may calculate, for each of one or more signals of the set of pre-classified signals, a parallelism value indicating a level of the parallelism between that signal and the input signal. The computer system may calculate, for a first subset of the set of pre-classified signals, a sparse vector, wherein each element of the sparse vector serves as a coefficient for a corresponding signal of the first subset. The computer system may determine, for each of the signals in the set of pre-classified signals, a similarity value indicating a level of similarity between that signal and the input signal.

Abstract: An approach for crowd congestion detection, the approach determines one or more selected locations. The approach determines a frequency spectrum history of one or more users within the one or more selected locations. The approach determines a location of the one or more users within the one or more selected locations. The approach determines a frequency spectrum of the one or more users within the one or more selected locations. The approach determines a crowding measure for the one or more selected locations based, at least in part, on the frequency spectrum history and the frequency spectrum of the one or more users within the one or more selected locations. The approach ranks the one or more selected locations based, at least in part, on the crowding measure.

Abstract: An approach for crowd congestion detection, the approach determines one or more selected locations. The approach determines a frequency spectrum history of one or more users within the one or more selected locations. The approach determines a location of the one or more users within the one or more selected locations. The approach determines a frequency spectrum of the one or more users within the one or more selected locations. The approach determines a crowding measure for the one or more selected locations based, at least in part, on the frequency spectrum history and the frequency spectrum of the one or more users within the one or more selected locations. The approach ranks the one or more selected locations based, at least in part, on the crowding measure.