Abstract: A method for capturing digital data for fabricating a dental splint involves displaying a GUI on a display of a smartphone that provides an alignment feature for a user to align a camera of the smartphone to a first position that captures teeth of a person, receiving digital video of the teeth, overlaying the alignment feature on the digital video of the teeth on the display, moving the alignment feature on the screen in a manner that causes the user to move the smartphone relative to the teeth to maintain alignment with the alignment feature, capturing digital image information of the teeth as the alignment feature is moved, the captured digital image information including depth information, and transmitting the captured digital image information, including the depth information, from the smartphone for use in fabricating a dental splint.

Abstract: A hydroponic system is managed by obtaining hydroponic data using a plurality of sensors in a hydroponic cultivator. Data from the plurality of sensors is communicated to a data aggregator, which renders the sensed data from the plurality of sensors in a predetermined data format, as aggregator output data. The aggregator output data is communicated to a fog computing unit, which, in turn, communicates with outside computing routed over the Internet backbone or “cloud”. The fog computing unit is used to control operation of the hydroponic cultivator based on direct control commands executed through the fog control unit and data inputs obtained by communications with outside computing routed over the Internet backbone or “cloud”.

Abstract: Techniques for determining a classification probability of an object in an environment are discussed herein. Techniques may include analyzing sensor data associated with an environment from a perspective, such as a top-down perspective, using multi-channel data. From this perspective, techniques may determine channels of multi-channel input data and additional feature data. Channels corresponding to spatial features may be included in the multi-channel input data and data corresponding to non-spatial features may be included in the additional feature data. The multi-channel input data may be input to a first portion of a machine-learned (ML) model, and the additional feature data may be concatenated with intermediate output data from the first portion of the ML model, and input into a second portion of the ML model for subsequent processing and to determine the classification probabilities. Additionally, techniques may be performed on a multi-resolution voxel space representing the environment.

Abstract: A system for facilitating image finding analysis includes one or more processors and one or more hardware storage devices storing instructions that are executable by the one or more processors to configure the system to perform acts such as (i) presenting an image on a user interface, the image being one of a plurality of images provided on the user interface in a navigable format, (ii) obtaining a voice annotation for the image, the voice annotation being based on a voice signal of a user, and (iii) binding the voice annotation to at least one aspect of the image, wherein the binding modifies metadata of the image based on the voice annotation.

Abstract: Systems and methods are disclosed for generating a specialized machine learning model by receiving a generalized machine learning model generated by processing a plurality of first training images to predict at least one cancer characteristic, receiving a plurality of second training images, the first training images and the second training images include images of tissue specimens and/or images algorithmically generated to replicate tissue specimens, receiving a plurality of target specialized attributes related to a respective second training image of the plurality of second training images, generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of second training images and the target specialized attributes, receiving a target image corresponding to a target specimen, applying the specialized machine learning model to the target image to determine at least one characteristic of the target image, and outputting the characteristic of the tar

Abstract: A method of image processing in a structured light imaging system is provided that includes receiving a captured image of a scene, wherein the captured image is captured by a camera of a projector-camera pair, and wherein the captured image includes a binary pattern projected into the scene by the projector, applying a filter to the rectified captured image to generate a local threshold image, wherein the local threshold image includes a local threshold value for each pixel in the rectified captured image, and extracting a binary image from the rectified captured image wherein a value of each location in the binary image is determined based on a comparison of a value of a pixel in a corresponding location in the rectified captured image to a local threshold value in a corresponding location in the local threshold image.

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.