An object is to provide an image display device capable of correcting a position in a simple manner even when a deviation occurs in position alignment in accordance with movement of a user. The image display device 100 includes a correction processing unit 102 that corrects a deviation when the deviation occurs between image data G and a steel frame T in a field of vision of a user U that occurs in accordance with movement of the user. This correction processing unit 102 acquires a coordinates correction position S that becomes a reference position used for a correction process from the steel frame T1 or the like that is a partial member using design data stored by a storage unit 103 and a self-position of the user U with respect to the steel frame T. The correction processing unit 102 performs correction by performing position alignment of the image data G on the basis of the one coordinates correction position S.

Provided herein are techniques for label free cell sorting. The systems and methods provided herein may use machine learning based image classification techniques to identify cells of interest within a sample of cells. The cells of interest may then be separated from the sample using mechanical, pneumatic, piezoelectric, and/or electronic devices.

An object tracking system that includes a sensor that is configured to capture frames of at least a portion of a global plane for a space. The system is configured to receive a first frame from the sensor, to identify a pixel location within the first frame, and to determine an estimated sensor location for the sensor by applying a homography to the pixel location. The homography includes coefficients that translate between pixel locations in a frame from the sensor and (x,y) coordinates in the global plane. The system is further configured to determine an actual sensor location for the sensor and to determine a location difference between the estimated sensor location and the actual sensor location. The system is further configured to compare the location difference to a difference threshold level and to recompute the homography in response to determining that the location difference exceeds the difference threshold level.

A method, system and computer program product for determining a map position of an ego-vehicle are disclosed. The method includes acquiring map data comprising a road geometry, initializing at least one dynamic landmark by measuring a position and velocity, relative to the ego-vehicle, of a surrounding vehicle, and determining a first map position of the surrounding vehicle based on this measurement and the geographical position of the ego-vehicle. Further, the method includes predicting a second map position of the surrounding vehicle, and measuring a location, relative to the ego-vehicle, of the surrounding vehicle when it is estimated to be at the second map position, whereby the geographical position of the ego-vehicle can be computed and updated.

The present disclosure discloses a photography-based 3D modeling system and method, and an automatic 3D modeling apparatus and method, including: (S1) attaching a mobile device and a camera to the same camera stand; (S2) obtaining multiple images used for positioning from the camera or the mobile device during movement of the stand, and obtaining a position and a direction of each photo capture point, to build a tracking map that uses a global coordinate system; (S3) generating 3D models on the mobile device or a remote server based on an image used for 3D modeling at each photo capture point; and (S4) placing the individual 3D models of all photo capture points in the global three-dimensional coordinate system based on the position and the direction obtained in S2, and connecting the individual 3D models of multiple photo capture points to generate an overall 3D model that includes multiple photo capture points.

A computer-implemented method includes receiving a 3D model representative of upper teeth (U1) of a patient (P) and receiving a plurality of images of a face of the patient (P). The method also includes generating a facial model (200) of the patient based on the received plurality of images and determining, based on the determined facial model (200), the received 3D model of 10 the upper teeth (U1) and the plurality of images, a spatial relationship between the upper teeth (U1) of the patient (P) and a facial skeleton of the patient (P).

A kit for a fiducial marker-based registration of preinterventional image data to an intra-interventional scene is disclosed. The kit comprises: at least one first component, wherein the first component is configured for attachment to a body of a patient, wherein the first component comprises at least one base part of an arresting mechanism, at least one second component, wherein the second component comprises at least one fiducial which is localizable in the preinterventional image data, wherein the second component comprises at least one first counterpart of the arresting mechanism, at least one third component, wherein the third component comprises at least one mounting unit for mounting a spatially localizable tracking object on the third component, wherein the third component comprises at least one second counterpart of the arresting mechanism;
wherein the second component and the third component are selectively attachable to the first component by establishing a releasable mechanical connection between the base part and the first counterpart or between the base part and the second counterpart.

A method for automatically reconstituting the architecture, along a reinforcing axis, of the reinforcement of a composite material, includes acquiring images of the reinforcement of the composite material, each image being acquired along a section plane perpendicular to the reinforcing axis; for each image acquired, detecting, using a neural network, barycentre and/or the circumference of each section of the reinforcing thread; for at least one acquired reference image, assigning a tag corresponding to a reinforcing thread, to each detected barycentre or circumference; for each other acquired image, assigning, to each detected barycentre and/or each detected circumference, the tag of the corresponding barycentre in the acquired reference image; reconstituting the architecture of each reinforcing thread from each detected barycentre and/or circumference having the tag of the reinforcing thread and the position on the reinforcing axis associated with the acquired image on which the barycentre and/or the circumference has been detected.

Systems and methods are provided to perform PdM surveys using data acquisition units which scan screen multiple locations where equipment or structures to be evaluated are present. Video data will be acquired and processed to measure translational and vibratory motion and additional data will be collected from other camera, sensors or via data links. The motion present in the equipment or structures under test and the supplemental data will be automatically evaluated to detect suspect equipment conditions and to minimize the amount of video data maintained on the data acquisition unit and transmitted back the central PdM server for review by a PdM analyst and long term archival.

Various embodiments of a physical instrument are described herein. The physical instrument comprises a reference array platform having a top surface and a bottom surface. A reference array including one or more different fiducial markers is disposed on the top surface of the reference array platform. The reference array platform may have a bent physical configuration or a tilted physical configuration. An adhesive layer is disposed on the bottom surface of the reference array platform.