Systems, methods, and non-transitory computer readable media including instructions for extracting content from a virtual display are disclosed. Extracting content from a virtual display includes generating a virtual display via a wearable extended reality appliance, wherein the virtual display presents a group of virtual objects and is located at a first virtual distance from the wearable extended reality appliance; generating an extended reality environment via the wearable extended reality appliance including at least one additional virtual object at a second virtual distance from the wearable extended reality appliance; receiving input for causing a specific virtual object to move from the virtual display to the extended reality environment; and in response, generating a presentation of a version of the specific virtual object in the extended reality environment at a third virtual distance different from the first virtual distance and the second virtual distance.

Aspects relate to apparatuses and methods for parsing and comparing resume video duplications. An exemplary apparatus includes a memory communicatively connected to at least a processor and includes instructions configuring the at least a processor to acquire a plurality of video elements from an existing video resume, wherein the existing video resume includes at least an image component, recognize subject-specific data of the existing video resume as a function of the at least an image component, wherein the subject-specific data includes verbal content and non-verbal content, recognize at least a keyword of the existing video resume as a function of the subject-specific data, recognize at least a feature of the existing video resume as a function of the subject-specific data, compare the subject-specific data to a target video resume and determine, as a function of the comparison result, a duplication coefficient for the target resume video.

In a motion analysis apparatus 101, a data input unit 205 acquires a first imaging result and a second imaging result. in the motion analysis apparatus 101, a skeleton recognition unit 206 recognizes skeleton positions of a subject using the first imaging. result acquired by the data input unit 205, and recognizes skeleton positions of the subject using the second imaging result acquired by the data input unit 205. A motion period extraction unit 403 extracts a period from a start of a motion to an end of the motion as a range of data for comparing skeleton feature points recognized by the skeleton recognition unit 206. The similarity calculation unit 401 compares skeleton feature points recognized for an input from a depth camera with skeleton feature points recognized for an input from an RGB camera to calculate similarities, and outputs a determination result based on the similarities.

A method includes receiving a first image, receiving a motion dataset, determining a motion level, determining an initialization state, and determining a tracking level. In a first condition, the method includes generating a first image pyramid, detecting a plurality of features in the first image pyramid using a first detector threshold, and generating a first set of detected keypoints from the plurality of features. In a second condition, the method includes generating a second image pyramid, detecting the plurality of features in the second image pyramid using a second detector threshold, the second detector threshold being less restrictive than the first detector threshold, and generating a second set of detected keypoints. In a third condition, the method includes detecting the plurality of features in the first image according to the first detector threshold and generating a third set of detected keypoint.

A dimension measurement method includes: extracting a plurality of lines from a plurality of images generated by shooting a target area from a plurality of viewpoints, and generating a line segment model which is a three-dimensional model of the target area that is expressed using the plurality of lines; calculating a dimension of a particular part inside the target area, using the line segment model; and outputting the dimension calculated.

A three-dimensional model generation method executed by an information processing device includes: obtaining images generated by shooting a subject from respective viewpoints; searching for a similar point that is similar to a first point in a first image among the images, from second points in a search area in a second image different from the first image, the search area being provided based on the first point; calculating an accuracy of a search result of the searching, using degrees of similarity between the first point and the respective second points; and generating a three-dimensional model using the search result and the accuracy.

A processor of the inspection support device acquires an image obtained by imaging a structure to be inspected and detects damage to the structure on the basis of the acquired image. In a case where two or more types of damage (cracking B and linear free lime C.sub.2) to the structure are detected, the processor determines whether or not two or more types of damage are detected from the same or adjacent positions. In a case where determination is made that the cracking B and the linear free lime C.sub.2 are detected from the same or adjacent positions when the processor outputs the damage detection result (a damage image, a damage diagram, and the like), the processor preferentially outputs a damage detection result of the linear free lime C.sub.2 in accordance with a priority of a damage type (FIG. 16A and FIG. 16B).

A shooting method executed by a shooting device includes: shooting first images of a target space; generating a first three-dimensional point cloud of the target space, based on the first images and a first shooting position and a first shooting orientation of each of the first images; and determining a first region of the target space for which generating a second three-dimensional point cloud which is denser than the first three-dimensional point cloud is difficult, using the first three-dimensional point cloud and without generating the second three-dimensional point cloud. The determining includes generating a mesh using the first three-dimensional point cloud, and determining the region other than a second region of the target space for which the mesh is generated.

Provided are a method and apparatus for detecting a pattern image, and more particularly, a pattern image detection method and a pattern image detection apparatus for effectively detecting a pattern of a template image from a target image. The pattern image detection method and the pattern image detection apparatus can provide an effect of quickly and accurately detecting the pattern of the template image from the target image while reducing the amount of computation for detecting the pattern of the template image.

The invention relates to a method for operating a stereoscopic medical microscope, wherein deteriorated and/or invalid calibration data are recognized, wherein for this purpose mutually corresponding image representations of at least one feature arranged in capture regions of cameras of a stereo camera system of the medical microscope are captured by means of the cameras, the captured image representations are evaluated by means of feature-based image processing, wherein the at least one feature is recognized in this case in the captured image representations and a misalignment and/or a decalibration of the cameras of the stereo camera system are/is recognized on the basis of the at least one feature recognized; and wherein at least one measure is carried out depending on an evaluation result. Furthermore, the invention relates to a medical microscope.