A control method for a mobile platform includes obtaining a captured image, determining a target first characteristic part of a target object from the captured image, determining a second characteristic part of the target object in the captured image, and switching from tracking the second characteristic part to tracking the target first characteristic part in response to a tracking parameter of the target object meeting a preset tracking condition.

A method and system for dynamically providing visual feedback about a quality of data collected during intra-oral scanning Images or light patterns are projected onto an object such as teeth for 3D measurement and for relaying feedback about a quality of the 3D measurement to a user. In this way unsuccessful registrations surface areas of the teeth that have not been acquired yet may be corrected by informing the user to repeat scans at corresponding locations in the intra-oral cavity.

The present disclosure teaches a method of utilizing image “match points” to measure and detect changes in a physical object. In some cases “degradation” or “wear and tear” of the physical object is assessed, while in other applications this disclosure is applicable to measuring intentional changes, such as changes made by additive or subtractive manufacturing processes, which may, for example, involve adding a layer or removing a layer by machining. A system may include a scanner, and a digital fingerprinting process, coupled to an object change computer server. The server is coupled to a datastore that stores class digital fingerprints, selected object digital fingerprints collected over time, match measurements, and deterioration metrics.

A method for monitoring a person performing a physical exercise based on a sequence of image frames showing an exercise activity of the person. The method includes extracting, based on the sequence of image frames, for each image frame a set of body key points using a neural network, the set of body key points being indicative of a posture of the person in the image frame; deriving, based on a subset of the body key points in each image frame, at least one characteristic parameter indicating a progression of a movement of the person; detecting a start loop condition by evaluating the time progression of the at least one characteristic parameter, said start loop condition indicating a transition from a start posture of the person to the movement of the person when performing the physical exercise, wherein a loop of exercising encompasses one single repetition of the physical exercise; detecting an end loop condition by evaluating the time progression of at least one of the characteristic parameters, said end loop condition indicating a transition from the movement of the person when performing the physical exercise to an intermediate posture, wherein, as a result, the start of the loop and the end of the loop are determined; and deriving the time period for a single loop of the physical exercise based on the start of the loop and the end of the loop and evaluating the time period.

According to the present invention, disclosed are a device and a method of generating an object image, recognizing an object, and learning an environment of a mobile robot which perform a deep learning algorithm which allows a robot to create a map and load environment information acquired during the autonomous movement while the autonomous mobile robot is being charged and may be used for an application which finds out a location by finally recognizing objects such as furniture using a method of checking a location of the recognized objects to mark the location on the map.

Provided are a training method and apparatus for a human keypoint positioning model, a human keypoint positioning method and apparatus, a device, a medium and a program product. The training method includes determining an initial positioned point of each of keypoints; acquiring N candidate points of each keypoint according to a position of the initial positioned point; extracting a first feature image, and forming N sets of graph structure feature data according to the first feature image and the N candidate points; performing graph convolution on the N sets of graph structure feature data to obtain N sets of offsets; correcting initial positioned points of all the keypoints to obtain N sets of current positioning results; and calculating each set of loss values according to labeled true values of all the keypoints and each set of current positioning results, and performing supervised training on the positioning model.

Systems, methods, and non-transitory computer readable media are provided for obtaining a slice of static map data comprising a plurality of blocks, each block comprising a plurality of cells, each, each cell having a cell value indicating a probability that an object is present in the cell; loading the slice into a cache memory of a parallel processor; arranging the static map data in the cache memory in contiguous memory spaces assigned to a group of workers of the parallel processor that have coalescing constraints; loading a frame of dynamic map data into the cache memory; obtaining a plurality of scan match candidates each representing a possible position and attitude of the vehicle; processing, in the parallel processor, the static and dynamic map data and the candidates to generate results each representing a candidate and score; and selecting the candidate having the highest score as a vehicle position.

A non-transitory computer readable storage medium storing computer readable instructions executable by a computer is provided. The computer readable instructions cause the computer to obtain first image data composing a first image and second image data composing a second image; specify first edge pixels in the first image and second edge pixels in the second image using the first image data and the second image data, respectively; specify a first feature point and a second feature point corresponding to each other using an algorithm different from an algorithm to specify the first edge pixels and the second pixels; determine pixels located in vicinity to the first feature point and pixels located in vicinity to the second feature point as first usable pixels and second usable pixels, respectively; and using the first usable pixels and the second usable pixels, determine positional relation between the first image and the second image.

An automatic topology mapping processing method and system. The automatic topology mapping processing method includes the steps of: obtaining, by the automatic topology mapping processing system, a plurality of images, wherein at least two of the plurality of images include a common area in which a common space is captured; extracting, by the automatic topology mapping processing system, from respective images, features of the respective images through a feature extractor using a neural network; and determining, by the automatic topology mapping processing system, mapping images of the respective images on the basis of the features extracted from the respective images.

An acquired image is analysed to determine a test outcome value. A shape template for a test structure is provided to identify a position of a target measurement region on the test structure. A mapping defining a transformation of the shape template onto the image frame is determined. A mapping is determined and it is determined if a first matching condition based on first displacements of one or more edges identified in the image frame relative to the shape template is satisfied for the mapping. When the mapping satisfies the first matching condition and a second different matching condition based on second displacements of one or more edges identified in the image frame relative to the shape template, a verified image of a test structure is established. In the verified image of the test structure a target measurement region is identified. A test outcome value is determined by analysing the target measurement region.