A non-transitory computer-readable recording medium stores a voxelization program that causes a computer to execute a process. The process includes voxelizing a three-dimensional shape to generate a first voxel structure corresponding to the three-dimensional shape, specifying, in a case where lines perpendicular to respective faces of a cube or a cuboid containing the generated first voxel structure are extended from the respective faces toward inside the cube or the cuboid until the lines hit the first voxel structure, a region outside an outer periphery of the first voxel structure according to whether at least lines extended from three faces orthogonal to each other intersect, and setting the specified outside region as a second voxel structure, and performing inversion to invert a region of the voxel structures and a region not set as a voxel in the cube or the cuboid.

Some implementations of the disclosure involve, at a device having one or more processors, one or more image sensors, and an illumination source, detecting a first attribute of an eye based on pixel differences associated with different wavelengths of light in a first image of the eye. These implementations next determine a first location associated with the first attribute in a three dimensional (3D) coordinate system based on depth information from a depth sensor. Various implementations detect a second attribute of the eye based on a glint resulting from light of the illumination source reflecting off a cornea of the eye. These implementations next determine a second location associated with the second attribute in the 3D coordinate system based on the depth information from the depth sensor, and determine a gaze direction in the 3D coordinate system based on the first location and the second location.

The present invention is in the field of methods for determining material properties from foam samples. It relates to a computer-implemented method for determining a material property of a foam sample comprising (a) providing a representation of the sample, (b) extracting at least one structural feature from the representation, wherein the at least one structural feature comprises walls, struts, or nodes (c) providing the at least one structural feature to a material model suitable for obtaining at least one material property from the structural feature, and (d) outputting the at least one material property received from the material model.

A patient-specific assessment of fracture probability for the femur proximal end is provided. 3D locations of the femur head center, a point on the femoral shaft center, and the femoral intercondylar notch are determined from a clinical image. A frontal plane, a perpendicular thereunto and a bone shaft axis are determined from the 3D locations. An FEA coordinate system is defined from the frontal plane, the perpendicular and the axis. Two FEA analyses are performed, one for neck fracture and one for pertrochanteric fracture, with the same displacement constraints and the same load magnitude but different load angles. The femur proximal end is divided into four anatomically-based regions. For each region and each load, maximum tensile and compressive principal strains are determined and, based on the body weight and the principal strains, a likelihood of fracture is obtained. The minimum of these 8 likelihoods gives the probability of fracture.

Various methods and systems are provided for real-time image segmentation of medical image data. In one example, the real-time image segmentation of the medical image data may include updating an initial segmentation of the medical image data in real-time. The update may be based on a user input to a regularization brush applied to the medical image data, the user input to the regularization brush allowing modification of a volume of the initial segmentation.

The present disclosure provides methods and systems directed to performing real-time and/or AI-assisted radiology. A method for processing an image of a location of a body of a subject may comprise (a) obtaining the image of the location of a body of the subject; (b) using a trained algorithm to classify the image or a derivative thereof to a category among a plurality of categories, wherein the classifying comprises applying an image processing algorithm; (c) directing the image to a first radiologist for radiological assessment if the image is classified to a first category among the plurality of categories, or (ii) directing the image to a second radiologist for radiological assessment, if the image is classified to a second category among the plurality of categories; and (d) receiving a recommendation from the first or second radiologist to examine the subject based at least in part on the radiological assessment.

A computer-implemented method for automated classification of 3D image data of teeth includes a computer receiving one or more of 3D image data sets where a set defines an image volume of voxels representing 3D tooth structures within the image volume associated with a 3D coordinate system. The computer pre-processes each of the data sets and provides each of the pre-processed data sets to the input of a trained deep neural network. The neural network classifies each of the voxels within a 3D image data set on the basis of a plurality of candidate tooth labels of the dentition. Classifying a 3D image data set includes generating for at least part of the voxels of the data set a candidate tooth label activation value associated with a candidate tooth label defining the likelihood that the labelled data point represents a tooth type as indicated by the candidate tooth label.

Technologies are provided herein for modeling and tracking physical objects, such as human hands, within a field of view of a depth sensor. A sphere-mesh model of the physical object can be created and used to track the physical object in real-time. The sphere-mesh model comprises an explicit skeletal mesh and an implicit convolution surface generated based on the skeletal mesh. The skeletal mesh parameterizes the convolution surface and distances between points in data frames received from the depth sensor and the sphere-mesh model can be efficiently determined using the skeletal mesh. The sphere-mesh model can be automatically calibrated by dynamically adjusting positions and associated radii of vertices in the skeletal mesh to fit the convolution surface to a particular physical object.

An estimation device includes a memory and at least one processor. The at least one processor is configured to acquire information regarding a target object. The at least one processor is configured to estimate information regarding a location and a posture of a gripper relating to where the gripper is able to grasp the target object. The estimation is based on an output of a neural model having as an input the information regarding the target object. The estimated information regarding the posture includes information capable of expressing a rotation angle around a plurality of axes.

The present disclosure provides an error detector for determining an error vector between a radio trajectory and an image trajectory. The error detector includes: an input for monitoring a radio trajectory of an object from a radio signal and an image trajectory of an object from an image over an observation area; a correlation module arranged to correlate the radio trajectory with the image trajectory; an error module arranged to determine an error vector between the radio trajectory and the image trajectory; and an output arranged to transmit the error vector for use in determining an estimated trajectory of a target based on a target trajectory from a radio signal.