Abstract: A method, system and computer program product for segmenting generic foreground objects in images and videos. For segmenting generic foreground objects in videos, an appearance stream of an image in a video frame is processed using a first deep neural network. Furthermore, a motion stream of an optical flow image in the video frame is processed using a second deep neural network. The appearance and motion streams are then joined to combine complementary appearance and motion information to perform segmentation of generic objects in the video frame. Generic foreground objects are segmented in images by training a convolutional deep neural network to estimate a likelihood that a pixel in an image belongs to a foreground object. After receiving the image, the likelihood that the pixel in the image is part of the foreground object as opposed to background is then determined using the trained convolutional deep neural network.

Abstract: A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a component co-replacement recommendation method (200).

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a critical edge detection neural network and a geometric model to determine camera parameters from a single digital image. In particular, in one or more embodiments, the disclosed systems can train and utilize a critical edge detection neural network to generate a vanishing edge map indicating vanishing lines from the digital image. The system can then utilize the vanishing edge map to more accurately and efficiently determine camera parameters by applying a geometric model to the vanishing edge map. Further, the system can generate ground truth vanishing line data from a set of training digital images for training the critical edge detection neural network.

Abstract: A method and an apparatus for controlling a video frame image in a live classroom, and a computer readable storage medium and an electronic device are provided. The method includes: acquiring image information of a target person in the video frame image; determining a plurality of detection points according to the image information; determining distribution information of the plurality of detection points based on relationship between the plurality of detection points and a preset first area; determining adjustment information of a camera based on the distribution information and camera parameters; and adjusting the camera according to the adjustment information, so that at least a part of the image information of the target person in the video frame image is located in a preset second area, wherein the second area is located in the first area.

Abstract: Systems and methods for trimming dental aligners include a cut line system for identifying a first point and a second point based on a line around tooth (LAT) for a tooth of a model representative of a dentition of a user, where the first point is disposed on a tooth-gingiva interface of the tooth. The cut line system defines a cut plane at a distance from the first point toward the second point, where the cut plane intersects a line between the first point and the second point, identifies a plurality of points on the LAT based on the cut plane, defines a cut line based on the plurality of points on the LAT and the first point, and controls a cutting system to cut the dental aligner along the cut line.

Abstract: A method of analyzing images of a biological specimen using a computational model is described, the method including processing a cell image of the biological specimen and a phase contrast image of the biological specimen using the computational model to generate an output data. The cell image is a composite of a first brightfield image of the biological specimen at a first focal plane and a second brightfield image of the biological specimen at a second focal plane. The method also includes performing a comparison of the output data and a reference data and refining the computational model based on the comparison of the output data and the reference data. The method also includes thereafter processing additional image pairs according to the computational model to further refine the computational model based on comparisons of additional output data generated by the computational model to additional reference data.

Abstract: A spinal surgery training process includes the steps of capturing a plurality of 2D images for each of a plurality of spines, generating a curve of each spine from the respective 2D images based on locations of select vertebrae in each of the spines, grouping the spines into one of a number of groups based on similarity to produce groups of spines having similarities, performing the capturing, generating, determining and grouping steps at least once prior to surgery and at least once after surgery to produce pre-operative groups and their resultant post-operative groups, and assigning surgical methods and a probability to each of the post-operative groups indicating the probability that a spinal shape of the post-operative group can be achieved using the surgical methods. An outcome prediction process for determining surgical methods can be implemented once the training process is complete.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for generating and/or adapting automated assistant content according to a distance of a user relative to an automated assistant interface that renders the automated assistant content. For instance, the automated assistant can provide data for a client device to render. The client device can request additional data when the user relocates closer to, or further from, the client device. In some implementations, a request for additional data can identify a distance between the user and the client device. In this way, the additional data can be generated or selected according to the distance in the request. Other implementations can allow an automated assistant to determine an active user from a group of users in an environment, and determine a distance between the active user and the client device in order that any rendered content can be tailored for the active user.

Abstract: Systems and methods are disclosed for using an integrated computing platform to view and transfer digital pathology slides using artificial intelligence, the method including receiving at least one whole slide image in a cloud computing environment located in a first geographic region, the whole slide image depicting a medical sample associated with a patient, the patient being located in the first geographic region; storing the received whole slide image in a first encrypted bucket; applying artificial intelligence to perform a classification of the at least one whole slide image, the classification comprising steps to determine whether portions of the medical sample depicted in the whole slide image are healthy or diseased; based on the classification of the at least one whole slide image, generating metadata associated with the whole slide image; and storing the metadata in a second encrypted bucket.

Abstract: A line of coherent radiation is projected on a bed on which one or more particles is located, the one or more particles flowing produced as a result of a downhole operation in the borehole. An image of the bed is captured wherein one or more particles on the bed deflect the line of coherent radiation. One or more image edges is detected based on the captured image. A subset of the one or more image edges is identified as corresponding to edges of the one or more particles, based in part on changes in intensity of the captured image. Information about the one or more particles, including information about of size, shape and volume, can be determined from the one or more image edges corresponding to the edges of the one or more particles.

Abstract: Disclosed is a method for calculating fractional flow reserve, comprising: collecting a pressure at the coronary artery inlet of heart by a blood pressure sensor in real-time, and storing a pressure value in a data linked table; obtaining an angiographic time according to the angiographic image, finding out the corresponding data from data queues based on time index using the angiographic time as an index value, screening out stable pressure waveforms during multiple cycles, and obtaining an average pressure Pa; and obtaining a length of the segment of blood vessel from angiographic images of the two body positions, and obtaining a blood flow velocity V; calculating a pressure drop ?P for the segment of the blood vessel using the blood flow velocity V at the coronary artery inlet, and calculating a pressure Pd at the distal end of the blood vessel, and further calculating the angiographic fractional flow reserve.

Abstract: A character recognition method, a character recognition apparatus, an electronic device and a computer readable storage medium are disclosed. The character recognition method includes: determining semantic information and first position information of each individual character recognized from an image; constructing a graph network according to the semantic information and the first position information of each individual character; and determining a character recognition result of the image according to a feature of each individual character calculated by the graph network.

Abstract: Provided is a method and apparatus for providing information needed for the diagnosis of lymph node metastasis of a thyroid cancer, and the method includes the steps of: acquiring medical images produced correspondingly to the continuous volumes of a body region including the neck; detecting at least one or more lymph nodes from the medical images through a first network function learned, the lymph nodes including at least one or more lymph nodes having higher lymph node metastasis risks than a given reference value; dividing the neck tissue around the thyroid into a plurality of compartments on the medical images through a second network function learned, based on the anatomical characteristics of the neck tissue; and matching diagnostic information including the information of the detected lymph nodes and the plurality of compartments with the medical images and displaying the diagnostic information on the medical images.

Abstract: A method for visual question answering, a computer device implementing the method and a medium for storing instructions on performing the method are provided. The method includes: acquiring an input image and an input question; constructing a visual graph based on the input image, wherein the visual graph comprises a first node feature and a first edge feature; constructing a question graph based on the input question, wherein the question graph comprises a second node feature and a second edge feature; performing a multimodal fusion on the visual graph and the question graph to obtain an updated visual graph and an updated question graph; determining a question feature based on the input question; determining a fusion feature based on the updated visual graph, the updated question graph and the question feature; and generating a predicted answer for the input image and the input question.

Abstract: A method is disclosed for generating pixel risk maps for diagnostic image reconstruction. The method produces uncertainty/variance maps by feeding into a trained encoder a short-scan image acquired from a medical imaging scan to generate latent code statistics including the mean ?y and variance ?y; selecting random samples based on the latent code statistics, z˜N(?y,?y2); feeding the random samples into a trained decoder to generate a pool of reconstructed images; and calculating, for each pixel of the pool of reconstructed images, the pixel mean and variance statistics across the pool of reconstructed images. The risk of each pixel may be calculated using the Stein's Unbiased Risk Estimator on the input density compensated data, that involves calculating the end-to-end divergence of the deep neural network.

Abstract: The present disclosure describes a computer-implemented method for image landmark detection. The method includes receiving an input image for the image landmark detection, generating a feature map for the input image via a convolutional neural network, initializing an initial graph based on the generated feature map, the initial graph representing initial landmarks of the input image, performing a global graph convolution of the initial graph to generate a global graph, where landmarks in the global graph move closer to target locations associated with the input image, and iteratively performing a local graph convolution of the global graph to generate a series of local graphs, where landmarks in the series of local graphs iteratively move further towards the target locations associated with the input image.

Abstract: Systems and techniques are provided for determining and applying corrected poses in digital content experiences. An example method can include receiving, from one or more sensors associated with an apparatus, inertial measurements and one or more frames of a scene; based on the one or more frames and the inertial measurements, determining, via a first filter, an angular and linear motion of the apparatus and a gravity vector indicating a direction of gravitational force interacting with the apparatus; when a motion of the apparatus is below a threshold, determining, via a second filter, an updated gravity vector indicating a direction of gravitational force interacting with the apparatus; determining, based on the updated gravity vector, parameters for aligning an axis of the scene with a gravity direction in a real-world spatial frame; and aligning, using the parameters, the axis of the scene with the gravity direction in the real-world spatial frame.

Abstract: Systems and methods for processing point cloud data are disclosed. The methods include receiving a 3D image including point cloud data, displaying a 2D image of the 3D image, and generating a 2D bounding box that envelops an object of interest in the 2D image. The methods further include generating a projected image frame comprising a projected plurality of points by projecting a plurality of points in a first direction. The methods may then include displaying an image frame that includes the 2D image and the 2D bounding box superimposed by the projected image frame, receiving a user input that includes an identification of a set of points in the projected plurality of points that correspond to the object of interest, identifying a label for the object of interest, and storing the set of points that correspond to the object of interest in association with the label.

Abstract: Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used to in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior.

Abstract: According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.