Abstract: Described is a system for early detection of events via social media mining. The system receives, as input, social media blog posts comprising textual data. The system processes the social media blog posts through a cascade of filters. The cascade of filters comprises an event term detection filter, a location term detection filter following the event term detection filter, and a future date detection filter following the location term detection filter. A plurality of candidate social media blog posts describing an event of interest on a future date is output to a user for further analysis.

Abstract: Described is a system for analyzing network activities. Each pair of node interactions between nodes in the network is represented with a tensor. For each pair of node interactions, a mesostructure is inferred using tensor decomposition of the tensor, resulting in inferred mesostructures. A temporal network structure representing each pair of node interactions is determined using a set of parameters generated from the tensor decomposition, resulting in temporal network structures. A future data cascade in the network is predicted using the temporal network structures.

Abstract: Described is a system for finding open space for robot exploration. During operation, the system designates a straight forward direction as a default direction. It is determined if a filtered 3D point cloud has a sufficient number of cloud points. If not, the system determines if an obstacle is too close in a previous time. If so, a backward direction is set as a target direction and if not, the straight forward direction is set as the target direction. Alternatively, if there are a sufficient number of points, then calculate a distance of each point to a sensor center and determine if a number of times that the distance is smaller than a distance threshold is greater than a fixed number. The system either sets the backward direction as the target direction or estimates an openness of each candidate direction until a candidate direction is set as the target direction.

Abstract: Described is a system for frontal and side doorway detection. Salient line segments are extracted from an image frame captured of an indoor environment. Existence of a vanishing point in the image frame is determined. If a vanishing point is detected with a confidence score that meets or exceeds a predetermined confidence score, then the system performs side doorway detection via a side doorway detection module. If a vanishing point is detected with a confidence score below the predetermined confidence score, then the system performs frontal doorway detection via a frontal doorway detection module. A description of detected doorways is output and used by a mobile robot (Unmanned Aerial Vehicle) to autonomously navigate the indoor environment.

Abstract: Described is a system for real world event summarization with microblog data. The system collects microblog image posts related to a real world event based on an event tag and generates a heterogeneous network (having nodes and edges) based on the microblog image posts. The nodes are ranked and images are then extracted from top-ranked nodes for display to the user as a real world event summarization.

Abstract: A method for localizing and estimating a pose of a known object in a field of view of a vision system is described, and includes developing a processor-based model of the known object, capturing a bitmap image file including an image of the field of view including the known object, extracting features from the bitmap image file, matching the extracted features with features associated with the model of the known object, localizing an object in the bitmap image file based upon the extracted features, clustering the extracted features of the localized object, merging the clustered extracted features, detecting the known object in the field of view based upon a comparison of the merged clustered extracted features and the processor-based model of the known object, and estimating a pose of the detected known object in the field of view based upon the detecting of the known object.

Abstract: Described is a system for identifying emerging trends in a consumer product from heterogeneous online data sources. Data extracted from heterogeneous data sources is fused, and consumer product data is identified from the fused data. A baseline distribution for consumer issues related to consumer products is generated from the set of consumer product data. A deviation value from the baseline distribution is determined for a specific consumer product. Indicators for future consumer issues regarding the specific consumer product are identified based on the deviation value. The indicators are reported to a system analyst.

Abstract: Described is a system for discovering user interests through online social media, and more specifically, to a way of doing so by means of a bi-directional graph model. During operation, the system generates a confidence matrix F based on user interactions and co-occurring tags on a social media platform. The confidence matrix F indicates a likelihood of the users in the social media platform as being interested in a particular topic. Based on such likelihoods, an action can be initiated regarding a particular topic for those users whose likelihood of being interested in the particular topic exceeds a predetermined threshold. For example, the system generates and presents an online advertisement to users regarding a particular topic to those users whose likelihood of being interested in the particular topic exceeds a predetermined threshold.

Abstract: Described is a system for filtering, segmenting and recognizing objects. The system receives a three-dimensional (3D) point cloud having a plurality of data points in 3D space and down-samples the 3D point cloud to generate a down-sampled 3D point cloud with reduced data points in the 3D space. A ground plane is then identified and removed, leaving above-ground data points in the down-sampled 3D point cloud. The above-ground data points are clustered to generate a plurality of 3D blobs, each of the 3D blobs having a cluster size. The 3D blobs are filtered based on cluster size to generate a set of 3D candidate blobs. Features are extracted from each 3D candidate blob. Finally, at least one of the 3D candidate blobs is classified as a pre-defined object class based on the extracted features.

Abstract: A method for localizing and estimating a pose of a known object in a field of view of a vision system is described, and includes developing a processor-based model of the known object, capturing a bitmap image file including an image of the field of view including the known object, extracting features from the bitmap image file, matching the extracted features with features associated with the model of the known object, localizing an object in the bitmap image file based upon the extracted features, clustering the extracted features of the localized object, merging the clustered extracted features, detecting the known object in the field of view based upon a comparison of the merged clustered extracted features and the processor-based model of the known object, and estimating a pose of the detected known object in the field of view based upon the detecting of the known object.

Abstract: Described is a system for open doorway detection for autonomous robot exploration, the system includes an onboard range sensor that is operable for constructing a three-dimensional (3D) point cloud of a scene. One or more processors that receive the 3D point cloud from the range sensor. The 3D point cloud is then filtered and downsampled to remove cloud points outside of a predefined range and reduce a size of the point cloud and, in doing so, generate a filtered and downsampled 3D point cloud. Vertical planes are extracted from the filtered and downsampled 3D point cloud. Finally, open doorways are identified from each extracted vertical plane.

Abstract: A method for detecting one or more target objects is provided including obtaining 2-dimensional imaging information and 3-dimensional point cloud information of a target zone. The method also includes determining a ground plane in the point cloud information and removing the ground plane to generate modified 3-dimensional information. Also, the method includes identifying a set of 2-dimensional candidate objects from the 2-dimensional imaging information, and identifying a set of 3-dimensional candidate objects from the modified 3-dimensional information. The method also includes determining, for each of at least some of the 2-dimensional candidate objects, a corresponding 3-dimensional candidate object from the set of 3-dimensional candidate objects.

Abstract: A method for detecting one or more target objects is provided including obtaining 2-dimensional imaging information and 3-dimensional point cloud information of a target zone. The method also includes determining a ground plane in the point cloud information and removing the ground plane to generate modified 3-dimensional information. Also, the method includes identifying a set of 2-dimensional candidate objects from the 2-dimensional imaging information, and identifying a set of 3-dimensional candidate objects from the modified 3-dimensional information. The method also includes determining, for each of at least some of the 2-dimensional candidate objects, a corresponding 3-dimensional candidate object from the set of 3-dimensional candidate objects.

Abstract: The present invention relates to an object detection and behavior recognition system using three-dimensional motion data. The system receives three-dimensional (3D) motion data of a scene from at least one sensor, such as a LIDAR sensor. An object is identified in the 3D motion data. Thereafter, an object track is extracted, the object track being indicative of object motion in the scene over time. Through Dynamic Time Warping (DTW) or other comparison techniques, the object track is compared to a database to identify the behavior of the object based on its object track.