Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for detecting a pose of a user. The program and method include operations comprising receiving a monocular image that includes a depiction of a body of a user; detecting a plurality of skeletal joints of the body based on the monocular image; accessing a video feed comprising a plurality of monocular images received prior to the monocular image; filtering, using the video feed, the plurality of skeletal joints of the body detected based on the monocular image; and determining a pose represented by the body depicted in the monocular image based on the filtered plurality of skeletal joints of the body.

A modeling engine generates a prediction model that quantifies and predicts secondary dynamics associated with the face of a performer enacting a performance. The modeling engine generates a set of geometric representations that represents the face of the performer enacting different facial expressions under a range of loading conditions. For a given facial expression and specific loading condition, the modeling engine trains a Machine Learning model to predict how soft tissue regions of the face of the performer change in response to external forces applied to the performer during the performance. The modeling engine combines different expression models associated with different facial expressions to generate a prediction model. The prediction model can be used to predict and remove secondary dynamics from a given geometric representation of a performance or to generate and add secondary dynamics to a given geometric representation of a performance.

A processor derives a first composition image representing a first composition included in a subject including three or more compositions from at least one radiation image acquired by imaging the subject, derives at least one removal radiation image obtained by removing the first composition from the at least one radiation image by using the first composition image, derives a plurality of other composition images representing a plurality of other compositions different from the first composition included in the subject by using the at least one removal radiation image, and derives a composite image obtained by synthesizing the first composition image and the plurality of other composition images at a predetermined ratio.

A method for automatic selection of display settings for a medical image is provided. The method includes receiving a medical image, mapping the medical image to an appearance classification cell of an appearance classification matrix using a trained deep neural network, selecting a first WW and a first WC for the medical image based on the appearance classification and a target appearance classification, adjusting the first WW and the first WC based on user preferences to produce a second WW and a second WC, and displaying the medical image with the second WW and the second WC via a display device.

Example dental scanning methods for analyzing jaws of a patient involve taking multiple scans of the jaws, and/or models thereof, and then shifting the image of one scan to match that of another. In some examples, fiducial markers are attached to the patient's jaws beforehand to accurately identify and track the relative position of the jaws. The methods provide a way for creating a precise image of an upper jaw and a lower jaw in their proper bite registration, even though the resulting image may show an insufficient number of teeth to readily do so. The final, properly shifted image serves as a virtual 3D jaw model that can be manipulated and analyzed to aid in various orthodontic and other dental treatments.

A method for remotely assessing oral health of a user of a mobile device by obtaining, using the mobile device (40), at least one digital image (1) of said user's (30) oral cavity (31) and additional non-image data (2) comprising anamnestic information about the user (30). The digital image (1) is processed both using a statistical object detection algorithm (20) to extract at least one local visual feature (3) corresponding to a medical finding related to a sub-region of said user's oral cavity (31); and also using a statistical image recognition algorithm (21) to extract at least one global classification label (4) corresponding to a medical finding related to said user's oral cavity (31) as a whole. An assessment (10) of the oral health of said user (30) is determined based on the local visual feature(s) (3), the global classification label(s) (4) and the non-image data (2).

A method for analysis of spine anatomy and stenosis is disclosed herein. The method includes preprocessing (3203) of images and then running segmentation models for each area of interest such as the foramen, disc, canal, vertebra (3206). In image post processing (3207), the system runs various heuristics to ensure accuracy (3208), computes areas (3209), and then runs a comparison model (3210). The report template produces HTML that is then converted to a PDF file of the final report (3214).

A method for controlling a smart device is provided. The method comprises identifying a user-furniture interaction activity of a user interacting with a home furnishing product by analyzing sensor data, captured by an imaging sensor, depicting a scene of the user interacting with the home furnishing product. The method further comprises comparing the user-furniture interaction activity against a set of predetermined user-furniture interaction activities, thereby determining a specific predetermined user-furniture interaction activity among the set of predetermined user-furniture interaction activities, wherein each of the predetermined user-furniture interaction activities is associated with a rule of controlling a smart device. The method further comprises controlling the smart device in accordance with the rule.

A method for use of machine learning in computer-assisted anatomical prediction. The method includes identifying with a processor parameters in a plurality of training images to generate a training dataset, the training dataset having data linking the parameters to respective training images, training at least one machine learning algorithm based on the parameters in the training dataset and validating the trained machine learning algorithm, identifying with the processor digitized points on a plurality of anatomical landmarks in a radiographic image of a person's skeleton displayed on a screen by determining anatomical relationships of adjacent bony structures as well as dimensions of at least a portion of a body of the skeleton in the displayed image using the validated machine learning algorithm and a scale factor for the displayed image, and making an anatomical prediction of the person's skeletal alignment based on the determined anatomical dimensions and a known morphological relationship.

A method and a corresponding system are provided. The method comprises steps of providing 2D images and subsequently detecting outlines of a primary structure in each of the images. A visual representation of the 2D images is generated and the 2D images are then arranged as 2D slices in a 3D visual representation. To this end, at least two of the 2D images are taken at different imaging angles. The method provides a 3D visual representation of a region of interest comprising a primary structure to support a spatial sense of a user.