Abstract: A method of providing an accurate three-dimensional scan of a dental arch area is disclosed. The arch area has two segments and a connecting area between the two segments. The connecting area has homogeneous features. A connecting-geometry tool with at least one definable feature is affixed to the arch area. The definable feature overlays at least part of the connecting area. The arch area is scanned to produce a scanned dataset of the arch area. The definable feature of the connecting-geometry tool on the connection area is determined based on the scanned dataset. The dimensions of the arch area are determined based on the data relating to the definable features from the scanned dataset.

Abstract: The disclosure provides human-tracking methods, systems, and storage media. The method includes: acquiring a plurality of human point clouds of a current frame from a plurality of cameras; generating a total point cloud of the current frame by integrating the plurality of human point clouds of the current frame; acquiring a plurality of human point clouds of a next frame from the plurality of cameras; acquiring a total point cloud of the next frame by integrating the plurality of human point clouds of the next frame; and performing human tracking based on the total point cloud of the current frame and the total point cloud of the next frame.

Abstract: Methods, systems, and devices for graphics processing are described. A device may receive an image including a set of pixels. The device may render a first subset of pixels in each bin of a set of bins during a first rendering pass, and defer rendering a second subset of pixels and a third subset of pixels in each bin of the set of bins during the first rendering pass. The second subset of pixels may include edge pixels and the third subset of pixels may be between the first subset of pixels and the second subset of pixels. The device may render the second subset of pixels and the third subset of pixels in each bin of the set of bins during a second rendering pass based on rendering the first subset of pixels. The device may then output the image based on the first and second rendering pass.

Abstract: Techniques for automatically detecting objects left behind. An example method includes receiving video frames of a scene from a three-dimensional (3D) camera and establishing, based on 3D depths associated with the video frames, a background of the scene. The method also includes detecting, in the frames, a foreground blob in the scene based on the foreground blob having a 3D depth that is different from the background. The method further includes detecting that the foreground blob has separated into a blob corresponding to a person and a second blob corresponding to an object, based on the person having a 3D depth that is different from the object. The method additionally includes determining that the person has been separated from the object for a threshold, and responsive to determining that the person has been separated from the object for the threshold, generating an alert indicating that the object is left behind.

Abstract: This disclosure provides methods and systems for predicting missing links and previously unknown numerals in a knowledge graph. A jointly trained multi-task machine learning model is disclosed for integrating a symbolic pipeline for predicting missing links and a regression numerical pipeline for predicting numerals with prediction uncertainty. The two prediction pipelines share a jointly trained embedding space of entities and relationships of the knowledge graph. The numerical pipeline additionally includes a second-layer multi-task regression neural network containing multiple regression neural networks for parallel numerical prediction tasks with a cross stich network allowing for information/model parameter sharing between the various parallel numerical prediction tasks.

Abstract: Methods and systems for detecting visual relations in a video are disclosed.

Abstract: A high efficiency method of processing images to provide perceptual high-contrast output. Pixel intensities are calculated by a weighted combination of a fixed number of static bounding rectangle sizes. This is more performant than incrementally growing the bounding rectangle size and performing expensive analysis on resultant histograms. To mitigate image artifacts and noise, blurring and down-sampling are applied to the image prior to processing.

Abstract: The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data acquired continuously by a plurality of types of sensors deployed on the vehicle are first received, where the plurality of types of sensor data provide information about surrounding of the vehicle. Based on at least one model, one or more items are tracked from a first of the plurality of types of sensor data acquired by one or more of a first type of the plurality of types of sensors, wherein the one or more items appear in the surrounding of the vehicle. At least some of the one or more items are then automatically labeled on-the-fly via either cross modality validation or cross temporal validation of the one or more items and are used to locally adapt, on-the-fly, the at least one model in the vehicle.

Abstract: In one embodiment, an image processing system includes a memory and processing circuitry. The memory is configured to store a predetermined program. The processing circuitry is configured, by executing the predetermined program, to perform processing on an input image by exploiting a neural network having an input layer, an output layer, and an intermediate layer provided between the input layer and the output layer, the input image being inputted to the input layer, and adjust an internal parameter based on data related to the input image, while performing the processing on the input image after training of the neural network, the internal parameter being at least one internal parameter of at least one node included in the intermediate layer, and the input parameter having been calculated by the training of the neural network.

Abstract: A system includes a sensor and a client. The client receives a set of frames of top-view depth images generated by the sensor. The client identifies a frame of the received frames in which a first contour associated with a first object is merged with a second contour associated with a second object. The client determines, at a first depth in the identified frame, a merged-contour region which is associated with the merged contours. The client detects a third contour at a second depth that is less than the first depth and determines a first region associated with the third contour. The client detects a fourth contour at the second depth and determines a second region associated with the fourth contour. If criteria are satisfied, the client associates the first region with a position of the first object and associates the second region with a position of the second object.

Abstract: To improve calculation efficiency by properly reducing the number of regression regions based on a size of an object to be detected, a processing apparatus has at least one processor or circuit configured to function as: a size obtain unit configured to obtain a size of a predetermined object in the image; a set unit configured to divide the image into a plurality of regions based on the size of the predetermined object obtained by the size obtain unit, and to set regression regions for estimating a number of the predetermined object, wherein the set unit is configured to inhibit to set the regression regions smaller than a predetermined minimum size corresponding to the predetermined object, and an estimate unit configured to estimate the number of the predetermined object by performing a regression process on the regression regions set by the set unit.

Abstract: Apparatus and methods for identification of a coded pattern visible to a computerized imaging apparatus while invisible or inconspicuous to human eyes. A pattern and/or marking may serve to indicate identity of an object, and/or the relative position of the pattern to a viewer. While some solutions exist for identifying patterns (for example, QR codes), they may be visually obtrusive to a human observer due to visual clutter. In exemplary implementations, apparatus and methods are capable of generating patterns with sufficient structure to be used for either discrimination or some aspect of localization, while incorporating spectral properties that are more aesthetically acceptable such as being: a) imperceptible or subtle to the human observer and/or b) aligned to an existing acceptable visual form, such as a logo. In one variant, a viewer comprises an imaging system comprised as a processor and laser scanner, or camera, or moving photodiode.

Abstract: A computer-based system and method for taking clarity measurements of a gem, and a computer-readable medium having computer-executable instructions, are provided and include receiving a pixilated image of a gem and identifying pixels representing an inclusion. The method and medium further include determining characteristics of the inclusion as a function of the pixels representing the inclusion, and providing a clarity grade based upon the determined characteristics. Also provided is a method for mapping a gem, and a computer-readable medium having computer-executable instructions, which include receiving a pixilated image of a gem having facet edges, and identifying pixels representing the facet edges. The method and medium further include generating a diagram of the gem, such that the diagram is a function of the pixels representing the facet edges, and superimposing the diagram onto the pixilated image.

Abstract: An object tracking system that includes a sensor that is configured to capture frames of at least a portion of a global plane for a space. The system is configured to receive a first frame from the sensor, to identify a pixel location within the first frame, and to determine an estimated sensor location for the sensor by applying a homography to the pixel location. The homography includes coefficients that translate between pixel locations in a frame from the sensor and (x,y) coordinates in the global plane. The system is further configured to determine an actual sensor location for the sensor and to determine a location difference between the estimated sensor location and the actual sensor location. The system is further configured to compare the location difference to a difference threshold level and to recompute the homography in response to determining that the location difference exceeds the difference threshold level.

Abstract: Present embodiments pertain to systems, apparatuses, and methods for analyzing and reporting movements characterized by torsional vibration in mechanical structures, machines, and machine components, through the use of an acquired video recording representing a plurality of cycles of motion, by measuring intensity values of a subset of pixels contained in a region of interest within the video recording in a plurality of frames of the video recording, wherein an application of one or more numerical algorithms to the repeating patterns in the intensity waveform enables the torsional vibration characteristics of the component to be determined.

Abstract: A method for detecting and recognizing surface defects of an automated fiber placement composite based on an image converted from point clouds, including: acquiring a surface point cloud of the automated fiber placement composite; fitting a plane to surface point data; calculating a distance from each point of the surface point cloud to a fitted plane; enveloping the surface point cloud by OBB, and generating a grayscale image according to the OBB and the distance; constructing a pre-trained semantic segmentation network for defect of fiber placement, and inputting the grayscale image to segment and recognize defect areas thereon; mapping a segmentation result output by the semantic segmentation network to the point cloud followed by defect evaluation and visualization.

Abstract: The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are received, which are acquired by a plurality of types of sensors deployed on the vehicle to provide information about surrounding of the vehicle. Based on at least one model, one or more surrounding items are tracked from a first of the plurality of types of sensor data acquired by a first type sensors. At least some of the tracked items are automatically labeled via cross validation and are used to locally adapt, on-the-fly, the at least one model. Model update information is received which from a model update center, which is derived based on the labeled at least some items. The at least one model is updated using the model update information.

Abstract: An object tracking system that includes a sensor that is configured to capture frames of at least a portion of a global plane for a space. The system is configured to receive a first frame from the sensor and to identify a first pixel location and a second pixel location within the first frame. The system is further configured to determine (x,y) coordinates by applying a homography to the first pixel location and the second pixel location. The system is further configured to determine an estimated distance between the (x,y) coordinates, to determine an actual distance, and to determine a distance difference between the estimated distance and the actual distance. The system is further configured to compare the distance difference to a difference threshold level and to recompute the homography in response to determining that the distance difference exceeds the difference threshold level.

Abstract: In the image processing device, the method and the recording medium according to the present invention, the characteristic information extractor extracts the number of times the same subject appears in the still images. The target object detector detects the target subject which appears the number of times not less than the threshold value. The image analysis condition determiner sets the image analysis condition for the portion of the moving image where the target subject is absent to be rougher than that where the target subject is present. The frame image analyzer extracts frame images from the moving image in accordance with the image analysis condition. The frame image output section calculates the evaluation value of the frame images based on the result of the image analysis and output the frame image having the evaluation value not less than the threshold value.

Abstract: The invention relates to a method for determining errors in at least one parameter of the object derived from a digital representation of an object, wherein the digital representation comprises a large number of pixels arranged on a grid. At least one item of image information that quantifies a material-specific value of the object at the position of the pixel is assigned to a pixel. The image information results from a metrological mapping of the object, and is overlaid with statistical noise. As a result of the metrological mapping of the object, the image information of a first pixel is correlated to the image information of pixels within a surroundings of the first pixel defined by a correlation length of the image.