Abstract: A computer-implemented method comprising collecting data from a plurality of information sources, identifying a geographic location associated with the data and forming a corresponding event according to the geographic location, correlating the data and the event with one or more topics based at least partly on the identified geographic location and storing the correlated data and event and inferring the associated geographic location if the data does not comprise explicit location information, including matching the data against a database of geo-referenced data.

Abstract: A method and system for analyzing at least one food item on a food plate is disclosed. A plurality of images of the food plate is received by an image capturing device. A description of the at least one food item on the food plate is received by a recognition device. The description is at least one of a voice description and a text description. At least one processor extracts a list of food items from the description; classifies and segments the at least one food item from the list using color and texture features derived from the plurality of images; and estimates the volume of the classified and segmented at least one food item. The processor is also configured to estimate the caloric content of the at least one food item.

Abstract: A computer implemented method for automatically detecting and classifying acoustic signatures across a set of recording conditions is disclosed. A first acoustic signature is received. The first acoustic signature is projected into a space of a minimal set of exemplars of acoustic signature types derived from a larger set of exemplars using a wrapper method. At least one vector distance is calculated between the projected acoustic signature and each exemplar of the minimal set of exemplars. An exemplar is selected from the minimal set of exemplars having the smallest vector distance to the projected acoustic signature as a class corresponding to and classifying the first acoustic signature. The first acoustic signature and the plurality of acoustic signatures may correspond to one of gunshots, musical instruments, songs, and speech. The minimal set of exemplars may correspond to a hierarchy of acoustic signature types.

Abstract: A computer-implemented method for estimating a volume of at least one food item on a food plate is disclosed. A first and second plurality of images are received from different positions above a food plate, wherein angular spacing between the positions of the first plurality of images is greater than angular spacing between the positions of the second plurality of images. A first set of poses of each of the first plurality of images is estimated. A second set of poses of each of the second plurality of images is estimated based on at least the first set of poses. A pair of images taken from each of the first and second plurality of images is rectified based on at least the first and second set of poses. A 3D point cloud is reconstructed based on at least the rectified pair of images. At least one surface of the at least one food item above the food plate is estimated based on at least the reconstructed 3D point cloud. The volume of the at least one food item is estimated based on the at least one surface.

Abstract: A computer-implemented method for for matching objects is disclosed. At least two images where one of the at least two images has a first target object and a second of the at least two images has a second target object are received. At least one first patch from the first target object and at least one second patch from the second target object are extracted. A distance-based part encoding between each of the at least one first patch and the at least one second patch based upon a corresponding codebook of image parts including at least one of part type and pose is constructed. A viewpoint of one of the at least one first patch is warped to a viewpoint of the at least one second patch. A parts level similarity measure based on the view-invariant distance measure for each of the at least one first patch and the at least one second patch is applied to determine whether the first target object and the second target object are the same or different objects.

Abstract: A method and system for coordinated tracking of objects is disclosed. A plurality of images is received from a plurality of nodes, each node comprising at least one image capturing device. At least one target in the plurality of images is identified to produce at least one local track corresponding to each of the plurality of nodes having the at least one target in its field of view. The at least one local track corresponding to each of the plurality of nodes is fused according to a multi-hypothesis tracking method to produce at least one fused track corresponding to the at least one target. At least one of the plurality of nodes is assigned to track the at least one target based on minimizing at least one cost function comprising a cost matrix using the k-best algorithm for tracking at least one target for each of the plurality of nodes. The at least one fused track is sent to the at least one of the plurality of nodes assigned to track the at least one target based on the at least one fused track.

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.

Abstract: A method segments a video. Audio frames of the video are classified with labels. Dominant labels are assigned to successive time intervals of consecutive labels. A semantic description is constructed for sliding time windows of the successive time intervals, in which the sliding time windows overlap in time, and the semantic description for each time window is a transition matrix determined from the dominant labels of the time intervals. A marker is determined from the transition matrices, in which a frequency of occurrence of the marker is between a low frequency threshold and a high frequency threshold. Then, the video is segmented at the locations of the markers.

Abstract: A method and system denoises a mixed signal. A constrained non-negative matrix factorization (NMF) is applied to the mixed signal. The NMF is constrained by a denoising model, in which the denoising model includes training basis matrices of a training acoustic signal and a training noise signal, and statistics of weights of the training basis matrices. The applying produces weight of a basis matrix of the acoustic signal of the mixed signal. A product of the weights of the basis matrix of the acoustic signal and the training basis matrices of the training acoustic signal and the training noise signal is taken to reconstruct the acoustic signal. The mixed signal can be speech and noise.

Abstract: A computer-implemented method for estimating a volume of at least one food item on a food plate is disclosed. A first and second plurality of images are received from different positions above a food plate, wherein angular spacing between the positions of the first plurality of images is greater than angular spacing between the positions of the second plurality of images. A first set of poses of each of the first plurality of images is estimated. A second set of poses of each of the second plurality of images is estimated based on at least the first set of poses. A pair of images taken from each of the first and second plurality of images is rectified based on at least the first and second set of poses. A 3D point cloud is reconstructed based on at least the rectified pair of images. At least one surface of the at least one food item above the food plate is estimated based on at least the reconstructed 3D point cloud. The volume of the at least one food item is estimated based on the at least one surface.

Abstract: A computer implemented method for unattended detection of a current terrain to be traversed by a mobile device is disclosed. Visual input of the current terrain is received for a plurality of positions. Audio input corresponding to the current terrain is received for the plurality of positions. The video input is fused with the audio input using a classifier. The type of the current terrain is classified with the classifier. The classifier may also be employed to predict the type of terrain proximal to the current terrain. The classifier is constructed using an expectation-maximization (EM) method.

Abstract: A computer-implemented method for matching objects is disclosed. At least two images where one of the at least two images has a first target object and a second of the at least two images has a second target object are received. At least one first patch from the first target object and at least one second patch from the second target object are extracted. A distance-based part encoding between each of the at least one first patch and the at least one second patch based upon a corresponding codebook of image parts including at least one of part type and pose is constructed. A viewpoint of one of the at least one first patch is warped to a viewpoint of the at least one second patch. A parts level similarity measure based on the view-invariant distance measure for each of the at least one first patch and the at least one second patch is applied to determine whether the first target object and the second target object are the same or different objects.

Abstract: A system determines real-time locations of railcars in a railroad environment. Railcars are equipped with at least four RFID tags. A RFID reader at a fixed location at every track branch in the environment reads the RFID tags. Railcar locations are updated for the railcars by determining the branches on which the railcars are located.

Abstract: A computer implemented method for automatically detecting and classifying acoustic signatures across a set of recording conditions is disclosed. A first acoustic signature is received. The first acoustic signature is projected into a space of a minimal set of exemplars of acoustic signature types derived from a larger set of exemplars using a wrapper method. At least one vector distance is calculated between the projected acoustic signature and each exemplar of the minimal set of exemplars. An exemplar is selected from the minimal set of exemplars having the smallest vector distance to the projected acoustic signature as a class corresponding to and classifying the first acoustic signature. The first acoustic signature and the plurality of acoustic signatures may correspond to one of gunshots, musical instruments, songs, and speech. The minimal set of exemplars may correspond to a hierarchy of acoustic signature types.

Abstract: A method and system for coordinated tracking of objects is disclosed. A plurality of images is received from a plurality of nodes, each node comprising at least one image capturing device. At least one target in the plurality of images is identified to produce at least one local track corresponding to each of the plurality of nodes having the at least one target in its field of view. The at least one local track corresponding to each of the plurality of nodes is fused according to a multi-hypothesis tracking method to produce at least one fused track corresponding to the at least one target. At least one of the plurality of nodes is assigned to track the at least one target based on minimizing at least one cost function comprising a cost matrix using the k-best algorithm for tracking at least one target for each of the plurality of nodes. The at least one fused track is sent to the at least one of the plurality of nodes assigned to track the at least one target based on the at least one fused track.

Abstract: A computer implemented method detects scene boundaries in videos by first extracting feature vectors from videos of different genres. The feature vectors are then classified as scene boundaries using a support vector machine. The support vector machine is trained to be independent of the different genres of the videos.

Abstract: A method and system for analyzing at least one food item on a food plate is disclosed. A plurality of images of the food plate is received by an image capturing device. A description of the at least one food item on the food plate is received by a recognition device. The description is at least one of a voice description and a text description. At least one processor extracts a list of food items from the description; classifies and segments the at least one food item from the list using color and texture features derived from the plurality of images; and estimates the volume of the classified and segmented at least one food item. The processor is also configured to estimate the caloric content of the at least one food item.

Abstract: A method classifies segments of a video using an audio signal of the video and a set of classes. Selected classes of the set are combined as a subset of important classes, the subset of important classes being important for a specific highlighting task, the remaining classes of the set are combined as a subset of other classes. The subset of important classes and classes are trained with training audio data to form a task specific classifier. Then, the audio signal can be classified using the task specific classifier as either important or other to identify highlights in the video corresponding to the specific highlighting task. The classified audio signal can be used to segment and summarize the video.

Abstract: A method generates a summary of a video. Faces are detected in a plurality of frames of the video. The frames are classified according to a number of faces detected in each frame and the video is partitioned into segments according to the classifications to produce a summary of the video. For each frame classified as having a single detected face, one or more characteristics of the face is determined. The frames are labeled according to the characteristics to produce labeled clusters and the segments are partitioned into sub-segments according to the labeled clusters.

Abstract: A method and system denoises a mixed signal. A constrained non-negative matrix factorization (NMF) is applied to the mixed signal. The NMF is constrained by a denoising model, in which the denoising model includes training basis matrices of a training acoustic signal and a training noise signal and statistics of weights of the training basis matrices. The applying produces weight of a basis matrix of the acoustic signal, of the mixed signal. A product of the weights of the basis matrix of the acoustic signal and the training basis matrices of the training acoustic signal and the training noise signal is taken to reconstruct the acoustic signal. The mixed signal can be speech and noise.