Abstract: A quick data structuring (QDS) system is provided to obtain and store structured data in constrained computing environment, such as a content editor application. The QDS system includes a presentation layer for receiving user inputted structured data, a logic layer, and a database for storing the structured data. The presentation layer is displayable in a web browser, which is also displayable within the user interface of the content editor application. The logic layer obtains the structured data from the database and outputs the same to a report file, which is the visible portion of a document file, and that is editable in the user interface of the content editor application. The report file and the presentation layer can be simultaneously displayed in the content editor application.

Abstract: A system for and method of antimicrobial susceptibility testing includes detecting a resonance peak of a sensor provided with live microbes on a surface thereof; applying a substance to the live microbes; detecting a resonance peak of said sensor after application of said substance; determining a width of a top of each of said resonance peaks before and after application of the substance from one of: (1) a phase angle versus frequency plot where the phase angle is the phase angle of the electrical impedance of said sensor. (2) a real part of a plot of an electrical impedance versus frequency of said sensor.

Abstract: Hyperlapse results are generated from wide-angled, panoramic video. A set of wide-angled, panoramic video data is obtained. Video stabilization is performed on the obtained set of wide-angled, panoramic video data. Without user intervention, a smoothed camera path is automatically determined using at least one region of interest that is determined using saliency detection and semantically segmented frames of stabilized video data resulting from the video stabilization. A set of frames is determined to vary the velocity of wide-angled, panoramic rendered display of the hyperlapse results.

Abstract: Examples of the present disclosure relate to generating optimal scanning trajectories for 3D scenes. In an example, a moveable camera may gather information about a scene. During an initial pass, an initial trajectory may be used to gather an initial dataset. In order to generate an optimal trajectory, a reconstruction of the scene may be generated based on the initial data set. Surface points and a camera position graph may be generated based on the reconstruction. A subgradient may be determined, wherein the subgradient provides an additive approximation for the marginal reward associated with each camera position node in the camera position graph. The subgradient may be used to generate an optimal trajectory based on the marginal reward of each camera position node. The optimal trajectory may then be used by to gather additional data, which may be iteratively analyzed and used to further refine and optimize subsequent trajectories.

Abstract: Examples of the present disclosure describe systems and methods for scene reconstruction from bursts of image data. In an example, an image capture device may gather information from multiple positions within the scene. At each position, a burst of image data may be captured, such that other images within the burst may be used to identify common image features, anchor points, and geometry, in order to generate a scene reconstruction as observed from the position. Thus, as a result of capturing bursts from multiple positions in a scene, multiple burst reconstructions may be generated. Each burst may be oriented within the scene by identifying a key frame for each burst and using common image features and anchor points among the key frames to determine a camera position for each key frame. The burst reconstructions may then be combined into a unified reconstruction, thereby generating a high-quality reconstruction of the scene.

Abstract: The mobile image viewing technique described herein provides a hands-free interface for viewing large imagery (e.g., 360 degree panoramas, parallax image sequences, and long multi-perspective panoramas) on mobile devices. The technique controls the imagery displayed on a display of a mobile device by movement of the mobile device. The technique uses sensors to track the mobile device's orientation and position, and front facing camera to track the user's viewing distance and viewing angle. The technique adjusts the view of a rendered imagery on the mobile device's display according to the tracked data. In one embodiment the technique can employ a sensor fusion methodology that combines viewer tracking using a front facing camera with gyroscope data from the mobile device to produce a robust signal that defines the viewer's 3D position relative to the display.

Abstract: Examples of the present disclosure relate to generating optimal scanning trajectories for 3D scenes. In an example, a moveable camera may gather information about a scene. During an initial pass, an initial trajectory may be used to gather an initial dataset. In order to generate an optimal trajectory, a reconstruction of the scene may be generated based on the initial data set. Surface points and a camera position graph may be generated based on the reconstruction. A subgradient may be determined, wherein the subgradient provides an additive approximation for the marginal reward associated with each camera position node in the camera position graph. The subgradient may be used to generate an optimal trajectory based on the marginal reward of each camera position node. The optimal trajectory may then be used by to gather additional data, which may be iteratively analyzed and used to further refine and optimize subsequent trajectories.

Abstract: A system for and method of antimicrobial susceptibility testing includes detecting a resonance peak of a sensor provided with live microbes on a surface thereof; applying a substance to the live microbes; detecting a resonance peak of said sensor after application of said substance; determining a width of a top of each of said resonance peaks before and after application of the substance from one of: (1) a phase angle versus frequency plot where the phase angle is the phase angle of the electrical impedance of said sensor, (2) a real part of a plot of an electrical impedance versus frequency of said sensor, (3) a plot of a magnitude of electrical impedance versus frequency of said sensor, and (4) a phase angle versus frequency plot where the phase angle is the phase angle between an output voltage and an input voltage of said sensor, and comparing the determined widths of tops of said resonance peaks or standard deviations of the frequency of said resonance peaks to determine antimicrobial susceptibility incl

Abstract: Examples of the present disclosure describe systems and methods for scene reconstruction from bursts of image data. In an example, an image capture device may gather information from multiple positions within the scene. At each position, a burst of image data may be captured, such that other images within the burst may be used to identify common image features, anchor points, and geometry, in order to generate a scene reconstruction as observed from the position. Thus, as a result of capturing bursts from multiple positions in a scene, multiple burst reconstructions may be generated. Each burst may be oriented within the scene by identifying a key frame for each burst and using common image features and anchor points among the key frames to determine a camera position for each key frame. The burst reconstructions may then be combined into a unified reconstruction, thereby generating a high-quality reconstruction of the scene.

Abstract: A “Food Logger” provides various approaches for learning or training one or more image-based models (referred to herein as “meal models”) of nutritional content of meals. This training is based on one or more datasets of images of meals in combination with “meal features” that describe various parameters of the meal. Examples of meal features include, but are not limited to, food type, meal contents, portion size, nutritional content (e.g., calories, vitamins, minerals, carbohydrates, protein, salt, etc.), food source (e.g., specific restaurants or restaurant chains, grocery stores, particular pre-packaged foods, school meals, meals prepared at home, etc.). Given the trained models, the Food Logger automatically provides estimates of nutritional information based on automated recognition of new images of meals provided by (or for) the user. This nutritional information is then used to enable a wide range of user-centric interactions relating to food consumed by individual users.

Abstract: Various technologies described herein pertain to creation of an output hyper-lapse video from an input video. Values indicative of overlaps between pairs of frames in the input video are computed. A value indicative of an overlap between a pair of frames can be computed based on a sparse set of points from each of the frames in the pair. Moreover, a subset of the frames from the input video are selected based on the values of the overlaps between the pairs of the frames in the input video and a target frame speed-up rate. Further, the output hyper-lapse video is generated based on the subset of the frames. The output hyper-lapse video can be generated without a remainder of the frames of the input video other than the subset of the frames.

Abstract: A “Quality Predictor” applies a machine-learned quality model to predict subjective quality of an output video of an image sequence processing algorithm without actually running that algorithm on a temporal sequence of image frames (referred to as “candidate sets”). Candidate sets having sufficiently high predicted quality scores are processed by the image sequence processing algorithm to produce an output video. Therefore, the Quality Predictor reduces computational overhead by eliminating unnecessary processing of candidate sets when the image sequence processing algorithm is not expected to produce acceptable results. The quality model is trained on a combination of human quality scores of output videos generated by the image sequence processing algorithm and image features extracted from frames of image sequences used to generate those output videos.

Abstract: Aspects of the present disclosure provide a method to select a plurality of leader stations (STAs) that may respond with an ACK message to a plurality of multicast frames broadcasted by the transmitting device (e.g., Access Point (AP) or another STA). For each multicast frame broadcasted, a different leader STA may be scheduled to respond with an ACK message. The leader STAs may be selected based on the measured signal quality and signal strength. Specifically, the transmitting device may select STAs that experience the poorest signal by comparison to other STAs on the network as the leader STAs to respond with an ACK message on a rotational basis.

Abstract: Hyperlapse results are generated from wide-angled, panoramic video. A set of wide-angled, panoramic video data is obtained. Video stabilization is performed on the obtained set of wide-angled, panoramic video data. Without user intervention, a smoothed camera path is automatically determined using at least one region of interest that is determined using saliency detection and semantically segmented frames of stabilized video data resulting from the video stabilization. A set of frames is determined to vary the velocity of wide-angled, panoramic rendered display of the hyperlapse results.

Abstract: Various technologies described herein pertain to generating a video loop. An input video can be received, where the input video includes values at pixels over a time range. An optimization can be performed to determine a respective input time interval within the time range of the input video for each pixel from the pixels in the input video. The respective input time interval for a particular pixel can include a per-pixel loop period and a per-pixel start time of a loop at the particular pixel within the time range from the input video. Moreover, an output video can be created based upon the values at the pixels over the respective input time intervals for the pixels in the input video.

Abstract: Described are various technologies that pertain to automatically generating looped videos or cinemagraphs by selecting objects to animate from an input video. In one implementation a group of semantically labeled objects from an input video is received. Candidate objects from the input video that can appear as a moving object in an output cinemagraph or looped video are identified. Candidate video loops are generated using the selected candidate objects. One or more of these candidate video loops are then selected to create a final cinemagraph. The selection of candidate video loops used to create the final cinemagraph can be made by a user or by a predictive model trained to evaluate the subjective attractiveness of the candidate video loops.

Abstract: Various technologies described herein pertain to creation of an output hyper-lapse video from an input video. Values indicative of overlaps between pairs of frames in the input video are computed. A value indicative of an overlap between a pair of frames can be computed based on a sparse set of points from each of the frames in the pair. Moreover, a subset of the frames from the input video are selected based on the values of the overlaps between the pairs of the frames in the input video and a target frame speed-up rate. Further, the output hyper-lapse video is generated based on the subset of the frames. The output hyper-lapse video can be generated without a remainder of the frames of the input video other than the subset of the frames.

Abstract: A “Food Logger” provides various approaches for learning or training one or more image-based models (referred to herein as “meal models”) of nutritional content of meals. This training is based on one or more datasets of images of meals in combination with “meal features” that describe various parameters of the meal. Examples of meal features include, but are not limited to, food type, meal contents, portion size, nutritional content (e.g., calories, vitamins, minerals, carbohydrates, protein, salt, etc.), food source (e.g., specific restaurants or restaurant chains, grocery stores, particular pre-packaged foods, school meals, meals prepared at home, etc.). Given the trained models, the Food Logger automatically provides estimates of nutritional information based on automated recognition of new images of meals provided by (or for) the user. This nutritional information is then used to enable a wide range of user-centric interactions relating to food consumed by individual users.

Abstract: A cinemagraph is generated that includes one or more video loops. A cinemagraph generator receives an input video, and semantically segments the frames to identify regions that correspond to semantic objects and the semantic object depicted in each identified region. Input time intervals are then computed for the pixels of the frames of the input video. An input time interval for a particular pixel includes a per-pixel loop period and a per-pixel start time of a loop at the particular pixel. In addition, the input time interval of a pixel is based, in part, on one or more semantic terms which keep pixels associated with the same semantic object in the same video loop. A cinemagraph is then created using the input time intervals computed for the pixels of the frames of the input video.

Abstract: Various technologies described herein pertain to creation of an output hyper-lapse video from an input video. Values indicative of overlaps between pairs of frames in the input video are computed. A value indicative of an overlap between a pair of frames can be computed based on a sparse set of points from each of the frames in the pair. Moreover, a subset of the frames from the input video are selected based on the values of the overlaps between the pairs of the frames in the input video and a target frame speed-up rate. Further, the output hyper-lapse video is generated based on the subset of the frames. The output hyper-lapse video can be generated without a remainder of the frames of the input video other than the subset of the frames.