Abstract: A system includes a wearable device including a camera. The system further includes at least one processor that can identify objects from video data generated by the camera and monitor how an individual wearing the wearable device manipulates the objects according to predetermined micro-activities of interest to infer an action by the individual.

Abstract: Embodiments of a lightweight unobtrusive wearable device which is operable to continually monitor an instantaneous hand pose are disclosed. In some embodiments, the device measures the position of the wrist relative to one's body and the configuration of the hand. The device may infer hand pose in real-time and, as such, can be combined with actuators or displays to provide instantaneous feedback to the user. The device may be worn on the wrist and all processing can be performed within the device, thus addressing privacy.

Abstract: A computer-implemented system and associated methods are disclosed including a device for personalized activity monitoring using the hands The device is worn about a wrist and captures images along the wrist including movement of the hands to monitor predetermined hand movements relative to an object of interest.

Abstract: This disclosure includes various embodiments of apparatuses, systems, and methods for adaptive batch mode active learning for evolving a classifier. A corpus of unlabeled data elements to be classified is received, a batch size is determined based on a score function, a batch of unlabeled data elements having the determined batch size is selected from the corpus and labeled using a labeling agent or oracle, a classifier is retrained with the labeled data elements, these steps are repeated until a stop criterion has been met, for example, the classifier obtains a desired performance on unlabeled data elements in the corpus. The batch size determination and selection of a batch unlabeled data elements may be based on a single score function. The data elements may be video, image, audio, web text, and/or other data elements.

Abstract: A method of producing a reconfigurable circuit device for running a computer program of moderate complexity such as multimedia processing. Code for the application is compiled into Control Flow Graphs representing distinct parts of the application to be run. From those Control Flow Graphs are extracted basic blocks. The basic blocks are converted to Data Flow Graphs by a compiler utility. From two or more Data Flow Graphs, a largest common subgraph is determined. The largest common subgraph is ASAP scheduled and substituted back into the Data Flow Graphs which also have been scheduled. The separate Data Flow Graphs containing the scheduled largest common subgraph are converted to data paths that are then combined to form code for operating the application. The largest common subgraph is effected in hardware that is shared among the parts of the application from which the Data Flow Graphs were developed.

Abstract: A method of producing a reconfigurable circuit device for running a computer program of moderate complexity such as multimedia processing. Code for the application is compiled into Control Flow Graphs representing distinct parts of the application to be run. From those Control Flow Graphs are extracted basic blocks. The basic blocks are converted to Data Flow Graphs by a compiler utility. From two or more Data Flow Graphs, a largest common subgraph is determined. The largest common subgraph is ASAP scheduled and substituted back into the Data Flow Graphs which also have been scheduled. The separate Data Flow Graphs containing the scheduled largest common subgraph are converted to data paths that are then combined to form code for operating the application. The largest common subgraph is effected in hardware that is shared among the parts of the application from which the Data Flow Graphs were developed.

Abstract: An image classification system uses curvature-based multi-scale morphology to classify an image by its most distinguishing features. The image is recorded in digital form. Curvature features associated with the image are determined. A structuring element is modulated based on the curvature features. The shape of the structuring element is controlled by making it a function of both the scaling factor and the principal curvatures of the intensity surface of the face image. The structuring element modulated with the curvature features is superimposed on the image to determine a feature vector of the image using mathematical morphology. When this Curvature-based Multi-scale Morphology (CMM) technique is applied to face images, a high-dimensional feature vector is obtained. The dimensionality of this feature vector is reduced by using the PCA technique, and the low-dimensional feature vectors are analyzed using an Enhanced FLD Model (EFM) for superior classification performance.

Abstract: Methods and apparatus for gathering image information from nanostructures includes a composite waveguide of conductive nanoparticles in a dielectric medium. The waveguide is irradiated with preferably coherent blue light to form a slow surface wave. The evanescent wave that is the “tail” of the surface wave exists outside the waveguide contiguous to its surface. The nanostructures are located to encounter the evanescent wave. The slowing of the wave that occurs in the waveguide reduces the wave's speed and wavelength sufficiently such that nanostructures can be imaged. Upon encountering the evanescent wave, the nanostructures radiate. This radiation causes a backward scattering from the structures and a forward perturbation of the wavefront of the surface wave. From the scattering and perturbation information about the physical characteristics of the nanostructures sufficient to form an image is derived.

Abstract: A method for tracking one or multiple objects from an input video sequence allows a user to select one or more regions that contain the object(s) of interest in the first and the last frame of their choice. An initialization component selects the current and the search frame and divides the selected region into equal sized macroblocks. An edge detection component computes the gradient of the current frame for each macroblock and a threshold component decides then which of the macroblocks contain sufficient information for tracking the desired object. A motion estimation component computes for each macroblock in the current frame its position in the search frame. The motion estimation component utilizes a search component that executes a novel search algorithm to find the best match. The mean absolute difference between two macroblocks is used as the matching criterion. The motion estimation component returns the estimated displacement vector for each block.

Abstract: An image classification system uses curvature-based multi-scale morphology to classify an image by its most distinguishing features. The image is recorded in digital form. Curvature features associated with the image are determined. A structuring element is modulated based on the curvature features. The shape of the structuring element is controlled by making it a function of both the scaling factor and the principal curvatures of the intensity surface of the face image. The structuring element modulated with the curvature features is superimposed on the image to determine a feature vector of the image using mathematical morphology. When this Curvature-based Multi-scale Morphology (CMM) technique is applied to face images, a high-dimensional feature vector is obtained. The dimensionality of this feature vector is reduced by using the PCA technique, and the low-dimensional feature vectors are analyzed using an Enhanced FLD Model (EFM) for superior classification performance.