Abstract: A method and system for identifying a feature of an object based on an AR device is provided. The AR device is capable of superimposing a virtual reality scene on a real reality scene. The method includes: an identification obtaining step of obtaining identification information corresponding to an object to be inspected; a tag selecting step of checking the object and using the AR device to select at least one tag based on a visual feature of the object; and a tag associating step of using the AR device to associate the at least one selected tag with the identification information of the object.

Abstract: A method and system for identifying a feature of an object based on an AR device is provided. The AR device is capable of superimposing a virtual reality scene on a real reality scene. The method includes: an identification obtaining step of obtaining identification information corresponding to an object to be inspected; a tag selecting step of checking the object and using the AR device to select at least one tag based on a visual feature of the object; and a tag associating step of using the AR device to associate the at least one selected tag with the identification information of the object.

Abstract: Systems and methods for inspecting a device include arranging the device in a known position relative to a plurality of movable cameras mounted on a controllable actuator controlled by a computing device and pointed at the device by controlling the controllable actuator to position a camera with a user interface on the computing device with a display, and the display displays a virtual image from the camera into CAD model of the device and an image of the device generated by the camera in a side-by-side or in an overlay fashion or both.

Abstract: A rotating rotor in a turbine has blades attached. At least one blade is a reference blade of which a reference position is determined by a Once-Per-Rotation sensor. A camera positioned at a viewing port takes a sequence of images of all the blades during a rotation at a trigger moment determined by a controller. The controller receives a signal indicating that the reference blade passes its reference point. The controller is provided with data about the number of blades, the position of the camera and a desired dwell of an image related to the blade. The controller calculates the trigger moments and generates trigger signals to the camera. The trigger moment is adjusted for changing rotor speed by extracting a blade feature from blade images. The camera records images that are labeled and stored as image data on a storage device.

Abstract: A method includes accessing, at a computing device, data descriptive of a graph representing a program. The graph includes multiple nodes representing execution steps of the program and includes multiple edges representing data transfer steps. The method also includes determining at least two heterogeneous hardware resources of the computing device that are available to execute code represented by one or more of the nodes, and determining one or more paths from a source node to a sink node based on a topology of the graph. The method further includes scheduling execution of code at the at least two heterogeneous hardware resources. The code is represented by at least one of the multiple nodes, and the execution of the code is scheduled based on the one or more paths.

Abstract: A method includes capturing an image of a scene that includes a diagram. The method further includes applying functional block recognition rules to image data of the image to recognize functional blocks of the diagram. The functional blocks include at least a first functional block associated with a first computer operation. The method further includes determining whether the functional blocks comply with functional block syntax rules. A functional graph is computer-generated based on the functional blocks complying with the functional block syntax rules. The functional graph corresponds to the diagram, and the functional graph includes the functional blocks.

Abstract: Systems and methods for inspecting a device are disclosed. The method includes arranging the device in a known position relative to a plurality of movable cameras. The plurality of movable cameras is mounted on a controllable actuator. The plurality of cameras is pointed at the device by controlling the controllable actuator to position the camera with a user interface. An image of the device generated by the camera is displayed on a mobile and wireless display. The computing device also causes a rendered virtual image of the device to be displayed on the mobile and wireless display. A stream of interest and a region of interest is selected at the mobile and wireless display from the images generated by the cameras.

Abstract: A method includes accessing, at a computing device, data descriptive of a graph representing a program. The graph includes multiple nodes representing execution steps of the program and includes multiple edges representing data transfer steps. The method also includes determining at least two heterogeneous hardware resources of the computing device that are available to execute code represented by one or more of the nodes, and determining one or more paths from a source node to a sink node based on a topology of the graph. The method further includes scheduling execution of code at the at least two heterogeneous hardware resources. The code is represented by at least one of the multiple nodes, and the execution of the code is scheduled based on the one or more paths.

Abstract: A method includes capturing an image of a scene that includes a diagram. The method further includes applying functional block recognition rules to image data of the image to recognize functional blocks of the diagram. The functional blocks include at least a first functional block associated with a first computer operation. The method further includes determining whether the functional blocks comply with functional block syntax rules. A functional graph is computer-generated based on the functional blocks complying with the functional block syntax rules. The functional graph corresponds to the diagram, and the functional graph includes the functional blocks.

Abstract: A rotating rotor in a turbine has blades attached. At least one blade is a reference blade of which a reference position is determined by a Once-Per-Rotation sensor. A camera positioned at a viewing port takes a sequence of images of all the blades during a rotation at a trigger moment determined by a controller. The controller receives a signal indicating that the reference blade passes its reference point. The controller is provided with data about the number of blades, the position of the camera and a desired dwell of an image related to the blade. The controller calculates the trigger moments and generates trigger signals to the camera. The trigger moment is adjusted for changing rotor speed by extracting a blade feature from blade images. The camera records images that are labeled and stored as image data on a storage device.

Abstract: Systems and methods for inspecting a device are disclosed. The method includes arranging the device in a known position relative to a plurality of movable cameras. The plurality of movable cameras is mounted on a controllable actuator. The plurality of cameras is pointed at the device by controlling the controllable actuator to position the camera with a user interface. An image of the device generated by the camera is displayed on a mobile and wireless display. The computing device also causes a rendered virtual image of the device to be displayed on the mobile and wireless display. A stream of interest and a region of interest is selected at the mobile and wireless display from the images generated by the cameras.

Abstract: Systems and methods for inspecting a device are disclosed. The method includes arranging the device in a known position relative to a plurality of movable cameras. The plurality of movable cameras are mounted on a controllable actuator. The plurality of cameras are pointed at the device by controlling the controllable actuator to position the camera with a user interface. A computing device renders a virtual image from a CAD model of the device with a computing device. An image of the device generated by the camera is displayed on a display. The computing device also causes the rendered virtual image of the device to be displayed on the display. The camera images and the rendered images can be displayed side-by-side, in an overlay fashion or both.

Abstract: A method of detecting a feature of a vehicle in an image of a vehicle includes providing a digitized image of a vehicle, providing a first filter mask over a first subdomain of the image, where the filter mask is placed to detect a feature in the image, calculating a function of a gradient of the image inside the first masked subdomain, and detecting the presence or absence of a vehicle feature within the first masked subdomain based on the value of the gradient function.

Abstract: A system and method for object detection are provided where the system includes a component detection unit for detecting components in an image, a component fusion unit in signal communication with the component detection unit for fusing the components into an object, and a CPU in signal communication with the detection and fusion units for comparing the fused components with a statistical model; and the method includes receiving observation data for a plurality of training images, forming at least one statistical model from the plurality of training images, receiving an input image having a plurality of pixels, detecting a plurality of components in the input image, determining a fusion of the detected components, comparing the fusion with the statistical model, and detecting an object in accordance with the comparison.

Abstract: A system and method for detecting an occupant and head pose using stereo detectors is disclosed. In the training stage, pairs of images taken simultaneously from a pair of stereo cameras are received and components in each pair of images are identified. Features are associated with each component and a value is associated with each feature. Among them, a subset of features with best discriminative capabilities is selected and forms a strong classifier for a given component in a pair of images. In the detection stage, the strong classifiers are used in the incoming image pairs to detect components. Identified components are used to detect occupants and head pose of the occupants. Stereo detection is combined with stereo matching in a systematic way to improve occupant detection and localization.

Abstract: A method and system of real-time obstacle detection from a moving vehicle is provided. The method and system use a calibrated image capturing device. The method and system use a motion estimation technique to pick points with reliable image motion flows, and performs very fast sparse matching between the image motion flows and true motion flows calculated from the ego-motion of the image capturing device. Any mismatch between the image motion flows and the true motion flows are verified over time to achieve robust obstacle detection.

Abstract: A system and method for detecting an occupant and head pose using stereo detectors is disclosed. In the training stage, pairs of images taken simultaneously from a pair of stereo cameras are received and components in each pair of images are identified. Features are associated with each component and a value is associated with each feature. Among them, a subset of features with best discriminative capabilities is selected and forms a strong classifier for a given component in a pair of images. In the detection stage, the strong classifiers are used in the incoming image pairs to detect components. Identified components are used to detect occupants and head pose of the occupants. Stereo detection is combined with stereo matching in a systematic way to improve occupant detection and localization.

Abstract: A method of detecting a feature of a vehicle in an image of a vehicle includes providing a digitized image of a vehicle, providing a first filter mask over a first subdomain of the image, where the filter mask is placed to detect a feature in the image, calculating a function of a gradient of the image inside the first masked subdomain, and detecting the presence or absence of a vehicle feature within the first masked subdomain based on the value of the gradient function.

Abstract: A system and method for object detection with sudden illumination changes provides a system including a camera model unit for computing a difference map between a first input image and a second input image, an illumination model unit in signal communication with the camera model unit for comparing the sign of the difference between two pixels in a first input image against the sign of the difference between two pixels in a second input image, and a CPU in signal communication with the camera model unit and the illumination model unit for detecting a changed object in accordance with said comparison when said sign of the difference is changed; and a corresponding method including providing a first image having a number of pixels, receiving a second image subsequent to the first image, calculating a difference map between the first and second images, selecting a pair of pixel locations from the first or second images in accordance with the difference map, statistically modeling a camera noise level for the firs

Abstract: A system and method for object detection are provided where the system includes a component detection unit for detecting components in an image, a component fusion unit in signal communication with the component detection unit for fusing the components into an object, and a CPU in signal communication with the detection and fusion units for comparing the fused components with a statistical model; and the method includes receiving observation data for a plurality of training images, forming at least one statistical model from the plurality of training images, receiving an input image having a plurality of pixels, detecting a plurality of components in the input image, determining a fusion of the detected components, comparing the fusion with the statistical model, and detecting an object in accordance with the comparison.