Abstract: A system to recognize text or symbols contained in a captured image using machine learning models leverages context information about the image to improve accuracy. Contextual information is determined for the entire image, or spatial regions of the images, and is provided to a machine learning model when a determination is made as to whether a region does or does not contain text or symbols. Associating features related to the larger context with features extracted from regions potentially containing text or symbolic content provides an incremental improvement of results obtained using machine learning techniques.

Abstract: A method for feature transformation of a data set includes: receiving a data set including original feature samples with corresponding class labels; splitting the data set into a direction optimization set and a training set; using the direction optimization set to calculate an optimum transformation vector that maximizes inter-class separability and minimizes intra-class variance of the feature samples with respect to corresponding class labels; using the optimum transformation vector to transform the rest of the original feature samples of the data set to new feature samples with enhanced discriminative characteristics; and training a classifier using the new feature samples, wherein the method is performed by one or more processors.

Abstract: A system to recognize text, objects, or symbols in a captured image using machine learning models reduces computational overhead by generating a plurality of thumbnail versions of the image at different downscaled resolutions and aspect ratios, and then processing the downscaled images instead of the entire image, or sections of the entire image. The downscaled images are processed to produce a combine feature vector characterizing the overall image. The combined feature vector is processed using the machine learning model.

Abstract: A process for training and optimizing a system to select video frames for optical character recognition (OCR) based on feature metrics associated with blur and sharpness. A set of image frames are subjectively labelled based on a comparison of each frame before and after binarization to determine to what degree text is recognizable in the binary image. A plurality of different sharpness feature metrics are generated based on the original frame. A classifier is then trained using the feature metrics and the subjective labels. The feature metrics are then tested for accuracy and/or correlation with subjective labelling data. The set of feature metrics may be refined based on which metrics produce the best results.

Abstract: An approach to computation of kernel descriptors is accelerated using precomputed tables. In one aspect, a fast algorithm for kernel descriptor computation that takes O(1) operations per pixel in each patch, based on pre-computed kernel values. This speeds up the kernel descriptor features under consideration, to levels that are comparable with D-SIFT and color SIFT, and two orders of magnitude faster than STIP and HoG3D. In some examples, kernel descriptors are applied to extract gradient, flow and texture based features for video analysis. In tests of the approach on a large database of internet videos used in the TRECVID MED 2011 evaluations, the flow based kernel descriptors are up to two orders of magnitude faster than STIP and HoG3D, and also produce significant performance improvements. Further, using features from multiple color planes produces small but consistent gains.

Abstract: A system that removes underlines in text appearing in captured images in multiple stages. The improved system rejects most text regions that do not require underline removal quickly and performs detailed underline detection and removal on a small number of regions.

Abstract: A system estimates text orientation in images captured using a handheld camera prior detecting text in the image. Text orientation is estimated based on edges detected within the image, and the image is rotated based on the estimated orientation. Text detection and processing is then performed on the rotated image. Non-text features along a periphery of the image may be sampled to assure that clutter will not undermine the estimation of orientation.

Abstract: Potential paths between a source and destination of a network are identified based on trace-route information, then filtered to eliminate paths or links that are not supported by ancillary information associated with the network so as to identify feasible/actual paths between the source and destination. The ancillary information includes, for example, routing tables and ARP tables. If a feasible path cannot be identified based on the ancillary information, supplemental information regarding nodes further along the potential path is assessed to provide a basis for inferring the nodes that may provide a feasible path. The determined feasible paths are displayed for review, and provided to serve as filters for subsequent path-analysis tools.

Abstract: A method for feature transformation of a data set includes: receiving a data set including original feature samples with corresponding class labels; splitting the data set into a direction optimization set and a training set; using the direction optimization set to calculate an optimum transformation vector that maximizes inter-class separability and minimizes intra-class variance of the feature samples with respect to corresponding class labels; using the optimum transformation vector to transform the rest of the original feature samples of the data set to new feature samples with enhanced discriminative characteristics; and training a classifier using the new feature samples, wherein the method is performed by one or more processors.

Abstract: A network analysis system invokes an application specific, or source-destination specific, path discovery process. The application specific path discovery process determines the path(s) used by the application, collects performance data from the nodes along the path, and communicates this performance data to the network analysis system for subsequent performance analysis. The system may also maintain a database of prior network configurations to facilitate the identification of nodes that are off the path that may affect the current performance of the application. The system may also be specifically controlled so as to identify the path between any pair of specified nodes, and to optionally collect performance data associated with the path.

Abstract: A computationally efficient approach to determining inner products between feature vectors is provided that eliminates or reduces the need for multiplication, and more specifically, provides an efficient and accurate basis selection for techniques such as Orthogonal Matching Pursuit.

Abstract: Distributional information for a set of ? vectors is determined using a sparse basis selection approach to representing an input image or video. In some examples, this distributional information is used for a classification task.

Abstract: A network analysis system invokes an application specific, or source-destination specific, path discovery process. The application specific path discovery process determines the path(s) used by the application, collects performance data from the nodes along the path, and communicates this performance data to the network analysis system for subsequent performance analysis. The system may also maintain a database of prior network configurations to facilitate the identification of nodes that are off the path that may affect the current performance of the application. The system may also be specifically controlled so as to identify the path between any pair of specified nodes, and to optionally collect performance data associated with the path.

Abstract: Potential paths between a source and destination of a network are identified based on trace-route information, then filtered to eliminate paths or links that are not supported by ancillary information associated with the network so as to identify feasible/actual paths between the source and destination. The ancillary information includes, for example, routing tables and ARP tables. If a feasible path cannot be identified based on the ancillary information, supplemental information regarding nodes further along the potential path is assessed to provide a basis for inferring the nodes that may provide a feasible path. The determined feasible paths are displayed for review, and provided to serve as filters for subsequent path-analysis tools.

Abstract: Potential paths between a source and destination of a network are identified based on trace-route information, then filtered to eliminate paths or links that are not supported by ancillary information associated with the network so as to identify feasible/actual paths between the source and destination. The ancillary information includes, for example, routing tables and ARP tables. If a feasible path cannot be identified based on the ancillary information, supplemental information regarding nodes further along the potential path is assessed to provide a basis for inferring the nodes that may provide a feasible path. The determined feasible paths are displayed for review, and provided to serve as filters for subsequent path-analysis tools.

Abstract: A computationally efficient approach to determining inner products between feature vectors is provided that eliminates or reduces the need for multiplication, and more specifically, provides an efficient and accurate basis selection for techniques such as Orthogonal Matching Pursuit.

Abstract: Distributional information for a set of ? vectors is determined using a sparse basis selection approach to representing an input image or video. In some examples, this distributional information is used for a classification task.

Abstract: An approach to computation of kernel descriptors is accelerated using precomputed tables. In one aspect, a fast algorithm for kernel descriptor computation that takes O(1) operations per pixel in each patch, based on pre-computed kernel values. This speeds up the kernel descriptor features under consideration, to levels that are comparable with D-SIFT and color SIFT, and two orders of magnitude faster than STIP and HoG3D. In some examples, kernel descriptors are applied to extract gradient, flow and texture based features for video analysis. In tests of the approach on a large database of internet videos used in the TRECVID MED 2011 evaluations, the flow based kernel descriptors are up to two orders of magnitude faster than STIP and HoG3D, and also produce significant performance improvements. Further, using features from multiple color planes produces small but consistent gains.

Abstract: Potential paths between a source and destination of a network are identified based on trace-route information, then filtered to eliminate paths or links that are not supported by ancillary information associated with the network so as to identify feasible/actual paths between the source and destination. The ancillary information includes, for example, routing tables and ARP tables. If a feasible path cannot be identified based on the ancillary information, supplemental information regarding nodes further along the potential path is assessed to provide a basis for inferring the nodes that may provide a feasible path. The determined feasible paths are displayed for review, and provided to serve as filters for subsequent path-analysis tools.

Abstract: A network analysis system invokes an application specific, or source-destination specific, path discovery process. The application specific path discovery process determines the path(s) used by the application, collects performance data from the nodes along the path, and communicates this performance data to the network analysis system for subsequent performance analysis. The system may also maintain a database of prior network configurations to facilitate the identification of nodes that are off the path that may affect the current performance of the application. The system may also be specifically controlled so as to identify the path between any pair of specified nodes, and to optionally collect performance data associated with the path.