Abstract: A safety monitoring system for a workspace area. The workspace area related to a region having automated moveable equipment. A plurality of vision-based imaging devices capturing time-synchronized image data of the workspace area. Each vision-based imaging device repeatedly capturing a time synchronized image of the workspace area from a respective viewpoint that is substantially different from the other respective vision-based imaging devices. A visual processing unit for analyzing the time-synchronized image data. The visual processing unit processes the captured image data for identifying a human from a non-human object within the workspace area. The visual processing unit further determining potential interactions between a human and the automated moveable equipment.

Abstract: Separating mixed signals includes receiving the mixed signals from signal sources transmitting from a number of cells. A signal source is operable to transmit a source signal, and a mixed signal comprises at least a subset of the source signals. A complex mixing matrix is established from the mixed signals. The complex mixing matrix describes mixing the source signals to yield the mixed signals. The number of cells is estimated from the mixed signals. The mixed signals are separated using the complex mixing matrix and the estimated number of cells.

Abstract: Described is a signal processing system. The system comprises a signal processing module having signal processing parameters and being configured to receive a plurality of signals. The signal processing module uses the signal processing parameters to output a processed signal, as either a fused signal or a plurality of separate signals. A classification module is included to recognize information encoded in the processed signal to classify the information encoded in the process signal, with the classification having a confidence level. An optimization module is configured, in a feedback loop, to utilize the information encoded in the processed signal to adjust the signal processing parameters to optimize the confidence level of the classification, thereby optimizing an output of the signal processing module.

Abstract: A method, computer program product, and system for processing imagery is presented. The imagery is processed by receiving data regarding a scene (such as from a sensor monitoring a scene). The scene includes an object having a dimension. Flow vectors are computed from the data, while a flow histogram space is generated from the flow vectors. A line segment (with a length) is found within the flow histogram space. An object in the scene is associated with the length segment, and the dimensions of the object are estimated based on the length of the line segment.

Abstract: A vision-based system for automatically detecting the type of object within a specified area, such as the type of occupant within a vehicle is presented. The type of occupant can then be used to determine whether an airbag deployment system should be enabled or not. The system extracts different features, including wavelet features and/or a disparity map from images captured by image sensors. These features are then processed by classification algorithms to produce class confidences for various occupant types. The occupant class confidences are fused and processed to determine occupant type. In a preferred embodiment, image features from image edges, wavelet features, and disparity are used. Various classification algorithms may be implemented to classify the object. Use of the disparity map and/or wavelet features provides greater computational efficiency.

Abstract: In one embodiment, a method for specific emitter identification includes receiving a signal from an emitter indicative of a hardware characteristic of the emitter. A computer-readable representation of the received signal is generated. A plurality of gradients for each partition of a plurality of partitions of the computer-readable representation is computed. Each gradient is indicative of at least the angular orientation of a respective portion of the computer-readable representation. A histogram is computed for each partition by assigning each computed gradient to a bin based at least in part on the magnitude of the computed gradient. One or more Histogram of Oriented Gradient (HOG) features are extracted from a concatenation of the bins of all of the computed histograms. The one or more HOG features are compared to one or more corresponding HOG features stored on a computer-readable medium.

Abstract: An object recognition system is described that incorporates swarming classifiers. The swarming classifiers comprise a plurality of software agents configured to operate as a cooperative swarm to classify an object group in a domain. Each node N represents an object in the group having K object attributes. Each agent is assigned an initial velocity vector to explore a KN-dimensional solution space for solutions matching the agent's graph. Further, each agent is configured to search the solution space for an optimum solution. The agents keep track of their coordinates in the KN-dimensional solution space that are associated with an observed best solution (pbest) and a global best solution (gbest). The gbest is used to store the best solution among all agents which corresponds to a best graph among all agents. Each velocity vector thereafter changes towards pbest and gbest, allowing the cooperative swarm to classify of the object group.

Abstract: The present invention relates to a system, method, and computer program product for recognition objects in a domain which combines feature-based object classification with efficient search mechanisms based on swarm intelligence. The present invention utilizes a particle swarm optimization (PSO) algorithm and a possibilistic particle swarm optimization algorithm (PPSO), which are effective for optimization of a wide range of functions. PSO searches a multi-dimensional solution space using a population of “software agents” in which each software agent has its own velocity vector. PPSO allows different groups of software agents (i.e., particles) to work together with different temporary search goals that change in different phases of the algorithm. Each agent is a self-contained classifier that interacts and cooperates with other classifier agents to optimize the classifier confidence level.

Abstract: A computer vision system includes distorting optics producing distorted or warped input images. The system includes an integral feature object classifier trained using an undistorted image space. Undistorted integral feature values are calculated directly from distorted input images without undistorting or dewarping the distorted input image.

Abstract: An object recognition system is described that incorporates swarming classifiers with attention mechanisms. The object recognition system includes a cognitive map having a one-to-one relationship with an input image domain. The cognitive map records information that software agents utilize to focus a cooperative swarm's attention on regions likely to contain objects of interest. Multiple agents operate as a cooperative swarm to classify an object in the domain. Each agent is a classifier and is assigned a velocity vector to explore a solution space for object solutions. Each agent records its coordinates in multi-dimensional space that are an observed best solution that the agent has identified, and a global best solution that is used to store the best location among all agents. Each velocity vector thereafter changes to allow the swarm to concentrate on the vicinity of the object and classify the object when a classification level exceeds a preset threshold.

Abstract: Described is an active learning system for fingerprinting an object identified in an image frame. The active learning system comprises a flow-based object segmentation module for segmenting a potential object candidate from a video sequence, a fixed-basis function decomposition module using Haar wavelets to extract a relevant feature set from the potential object candidate, a static classifier for initial classification of the potential object candidate, an incremental learning module for predicting a general class of the potential object candidate, an oriented localized filter module to extract features from the potential object candidate, and a learning-feature graph-fingerprinting module configured to receive the features and build a fingerprint of the object for tracking the object.

Abstract: A method, an apparatus, and a computer program product for three-dimensional shape estimation using constrained disparity propagation are presented. An act of receiving a stereoscopic pair of images of an area occupied by at least one object is performed. Next, pattern regions and non-pattern regions are detected in the images. An initial estimate of ?patial disparities between the pattern regions in the images is generated. The initial estimate is used to generate a subsequent estimate of the spatial disparities between the non-pattern regions. The subsequent estimate is used to generate further subsequent estimates of the spatial disparities using the disparity constraints until there is no change between the results of subsequent iterations, generating a final estimate of the spatial disparities. A disparity map of the area occupied by at least one object is generated from the final estimate of the three-dimensional shape.

Abstract: An object recognition system is described that incorporates swarming classifiers. The swarming classifiers comprise a plurality of software agents configured to operate as a cooperative swarm to classify an object in a domain as seen from multiple view points. Each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions. Each agent is configured to perform an iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents. Each velocity vector changes towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object.

Abstract: In one embodiment, a system for identifying a communication device includes a signal generator coupled to a transmit horn and a computing system coupled to a receive horn through a receiver. The signal generator is operable to generate an excitation waveform from the transmit horn such that the communication device passively reflects a response waveform. The computing system is operable to receive the response waveform from the communication device and compare the response waveform to a plurality of reference waveforms to determine the identity of the communication device.

Abstract: A method for adaptive driver workload estimation. A subjective assessment of a driver workload is received from a vehicle driver. A stream of sensor input data is collected from one or more sensors in response to receiving the subjective assessment. A machine learning algorithm is applied to a driver workload estimate model based on the stream of sensor input data and the subjective assessment. The result of the applying is an updated driver workload estimate model.

Abstract: Described is a signal processing system. The system comprises a signal processing module having signal processing parameters and being configured to receive a plurality of signals. The signal processing module uses the signal processing parameters to output a processed signal, as either a fused signal or a plurality of separate signals. A classification module is included to recognize information encoded in the processed signal to classify the information encoded in the process signal, with the classification having a confidence level. An optimization module is configured, in a feedback loop, to utilize the information encoded in the processed signal to adjust the signal processing parameters to optimize the confidence level of the classification, thereby optimizing an output of the signal processing module.

Abstract: Described is a behavior recognition system for detecting the behavior of objects in a scene. The system comprises a semantic object stream module for receiving a video stream having at least two frames and detecting objects in the video stream. Also included is a group organization module for utilizing the detected objects from the video stream to detect a behavior of the detected objects. The group organization module further comprises an object group stream module for spatially organizing the detected objects to have relative spatial relationships. The group organization module also comprises a group action stream module for modeling a temporal structure of the detected objects. The temporal structure is an action of the detected objects between the two frames, whereby through detecting, organizing and modeling actions of objects, a user can detect the behavior of the objects.

Abstract: Separating mixed signals includes receiving the mixed signals from signal sources transmitting from a number of cells. A signal source is operable to transmit a source signal, and a mixed signal comprises at least a subset of the source signals. A complex mixing matrix is established from the mixed signals. The complex mixing matrix describes mixing the source signals to yield the mixed signals. The number of cells is estimated from the mixed signals. The mixed signals are separated using the complex mixing matrix and the estimated number of cells.

Abstract: An object recognition system is described that incorporates swarming classifiers. The swarming classifiers comprise a plurality of software agents configured to operate as a cooperative swarm to classify an object in a domain as seen from multiple view points. Each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions. Each agent is configured to perform an iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents. Each velocity vector changes towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object.

Abstract: An object recognition system is described that incorporates swarming classifiers. The swarming classifiers comprise a plurality of software agents configured to operate as a cooperative swarm to classify an object group in a domain. Each node N represents an object in the group having K object attributes. Each agent is assigned an initial velocity vector to explore a KN-dimensional solution space for solutions matching the agent's graph. Further, each agent is configured to search the solution space for an optimum solution. The agents keep track of their coordinates in the KN-dimensional solution space that are associated with an observed best solution (pbest) and a global best solution (gbest). The gbest is used to store the best solution among all agents which corresponds to a best graph among all agents. Each velocity vector thereafter changes towards pbest and gbest, allowing the cooperative swarm to classify of the object group.