A face image processing device, includes: an image coordinate system coordinate value derivation unit detecting an x-coordinate value and a y-coordinate value in an image coordinate system at a feature point of an organ of a face of a person in an image, and estimating a z-coordinate value, so as to derive three-dimensional coordinate values in the image coordinate system; a camera coordinate system coordinate value derivation unit deriving three-dimensional coordinate values in a camera coordinate system from the three-dimensional coordinate values in the image coordinate system derived by the image coordinate system coordinate value derivation unit; and a parameter derivation unit applying the three-dimensional coordinate values in the camera coordinate system derived by the camera coordinate system coordinate value derivation unit to a predetermined three-dimensional face shape model to derive a model parameter of the three-dimensional face shape model in the camera coordinate system.

A method and a device for image processing are disclosed. The method includes: receiving a first image, wherein the first image includes a face; detecting the face and a background region in the first image, establishing a three-dimensional model of the face according to the first image; rotating the three-dimensional model of the face by a first angle; and projecting the three-dimensional model of the face rotated by the first angle to an image coordinate system of the first image and fusing a face region with a processed background region to obtain a second image.

A work cell and method for automatically separating objects disposed in 3D clusters includes dispensing the objects onto a horizontal conveying surface to form a 2D array, reforming the 2D array into a 1D stream in which the objects move in single-file in a predefined moving direction, utilizing a vision-based or other stationary sensing system to identify a selected target object in the 1D stream as the target object passes through an image capture (sensing) region, calculating trajectory data defining the target object's time-based position in the 1D stream, and then utilizing the trajectory data to control a robot arm or other object removal mechanism such that only the selected object is forcibly removed (e.g., swiped or picked-up) from the horizontal conveying surface. A continuous-loop-type conveying mechanism includes two parallel conveyor-belt-type conveying structures and associated belt-switching structures. An AI-powered vision system identifies new object shapes during preliminary learning phases.

Various embodiments are generally directed to techniques of overlaying a virtual object on a physical object in augmented reality (AR). A computing device may receive one or more images of the physical object, perform analysis on the images (such as image segmentation) to generate a digital outline, and determine a position and a scale of the physical object based at least in part on the digital outline. The computing device may configure (e.g., rotate, scale) a 3D model of the physical object to match the determined position and scale of the physical object. The computing device may place or overlay a 3D virtual object on the physical object in AR based on a predefined location relation between the 3D virtual object and the 3D model of the physical object, and further, generate a composite view of the placement or overlay.

Aspects of the present disclosure involve a system and a method for performing operations comprising: receiving a two-dimensional continuous surface representation of a three-dimensional object, the continuous surface comprising a plurality of landmark locations; determining a first set of soft membership functions based on a relative location of points in the two-dimensional continuous surface representation and the landmark locations; receiving a two-dimensional input image, the input image comprising an image of the object; extracting a plurality of features from the input image using a feature recognition model; generating an encoded. feature representation of the extracted features using the first set of soft membership functions; generating a dense feature representation of the extracted features from the encoded representation using a second set of soft membership functions; and processing the second set of soft membership functions and dense feature representation using a neural image decoder model to generate an output image.

Methods and systems for well tool wear classification system are provided. A wear classifier tool is configured to classify wear of a scanned well tool using a machine learning engine. Computer-readable memory stores a training dataset and a trained ML model. The training data set includes scanned image data and associated labels representative of classification types of failure. The trained ML model has a neural network. The wear classifier tool can output data identifying a failure mode of the scanned well tool based on classification of input by the machine learning engine. A database is configured to stored historical data on scanner type, patterns of scanner cutting elements, sensor type, and age and usage conditions. A scanning system includes a camera and a three-dimensional (3D) scanner configured to scan a drill bit.

The invention relates to a method for calculating three-dimensional information of food received within a cavity of a food preparation entity, the method comprising the steps of: capturing at least one image of said food received within the cavity by a plenoptic camera, said image comprising information regarding the light intensity and the direction of light rays traveling in space; or capturing at least two images of said food received within the cavity, said images being taken from different positions during movement of a camera; or capturing at least two images of said food received within the cavity using a camera, said images comprising different focus points; and processing the at least one image in order to establish three-dimensional information of said food received within the cavity.

A measurement device adapted to cooperate with a three-dimensional image is provided. The three-dimensional image includes a plurality of three-dimensional positioning points. The measurement device comprises: a first camera unit for providing a two-dimensional image; an analysis module for analyzing the two-dimensional image to define a plurality of two-dimensional positioning points; a matching module for making the two-dimensional positioning points correspond to the three-dimensional positioning points, respectively, to generate a three-dimensional model; an input module for receiving a starting point and a destination in the two-dimensional image; a measurement module for obtaining first position information and second position information that correspond to the starting point and the destination respectively and calculating data; and an output module. A processor configured to execute a measurement method is also provided.

The invention relates to a first computer implemented method for automatically generating a first 3D garment model representing a first garment to be fabricated from first garment panels, a second computer implemented method for virtually finishing a second 3D garment model representing a second garment to be fabricated without finishes or with default finishes, and a third computer implemented method for automatically generating a plurality of third 3D garment models in a batch process, each third 3D garment model representing a third garment to be fabricated from third garment panels.

A system first registers a two-dimensional image to targetable three-dimensional data. A user or automated process selects image coordinates of a target within the registered two-dimensional image. The system intersects the image coordinates of the target with the targetable three-dimensional data, thereby generating geodetic coordinates of the target in a point cloud. Error estimates for the geodetic coordinates of the target are generated, and the system stores the geodetic coordinates and associated error of the target in a database for use in downstream exploitation.