A system includes a calibration plate with fiducial markers, a device under test interface, an alignment stage with a rotary sub-stage and a linear sub-stage, and a camera on the alignment stage.

A system includes a calibration plate with fiducial markers, a device under test interface, an alignment stage with a rotary sub-stage and a linear sub-stage, and a camera on the alignment stage.

Provided are methods and systems for aligning multiple parts using simultaneous scanning of features of different parts and using feature-based coordinate systems for determining relative positions of these. Specifically, a feature-based coordinate system may be constructed using one or more critical dimensions between features of different parts. The scanner may be specifically positioned to capture each of these critical dimensions precisely. The feature-based coordinate system is used to compare the critical dimensions to specified ranges. The position of at least one part may be adjusted based on results of this comparison using, for example, a robotic manipulator. The process may be repeated until all critical dimensions are within their specified ranges. In some embodiments, multiple sets of features from different parts are used such that each set uses its own feature-based coordinate system. The part adjustment may be performed based on the collective output from these multiple sets.

A computer implemented method may include the steps of: (1) providing a first point cloud representative of at least a bare portion of a surface of an object relative to a reference portion of the surface, (2) providing a second point cloud representative of at least a coated portion of the surface relative to the reference portion, (3) registering the first point cloud and the second point cloud with respect to a common reference coordinate system, (4) registering a first reference portion subset of the first data points representative of the reference portion and a second reference portion subset of the second data points representative of the reference portion, and (5) calculating difference values indicative of a thickness of the surface coating based on differences between a coated portion subset of the second data points and a bare portion subset of the first data points.

A computer implemented method may include the steps of: (1) providing a first point cloud representative of at least a bare portion of a surface of an object relative to a reference portion of the surface, (2) providing a second point cloud representative of at least a coated portion of the surface relative to the reference portion, (3) registering the first point cloud and the second point cloud with respect to a common reference coordinate system, (4) registering a first reference portion subset of the first data points representative of the reference portion and a second reference portion subset of the second data points representative of the reference portion, and (5) calculating difference values indicative of a thickness of the surface coating based on differences between a coated portion subset of the second data points and a bare portion subset of the first data points.

A method and device for automatically identifying a point of interest in a depth measurement on a viewed object using a video inspection device is disclosed. The video inspect device determines the three-dimensional coordinates in a region of interest on the viewed object and analyzes those surface points to determine the desired measurement application (e.g., determining the deepest point, the highest point, or the clearance between two surfaces). Based on the desired measurement application, the video inspection device automatically identifies the point of interest on the viewed object and places a cursor at that location.

A method and device for automatically identifying a point of interest in a depth measurement on a viewed object using a video inspection device is disclosed. The video inspect device determines the three-dimensional coordinates in a region of interest on the viewed object and analyzes those surface points to determine the desired measurement application (e.g., determining the deepest point, the highest point, or the clearance between two surfaces). Based on the desired measurement application, the video inspection device automatically identifies the point of interest on the viewed object and places a cursor at that location.

[Object] To appropriately decide an image generation target position in a space. [Solution] Provided is an information processing device including a processor configured to achieve a function of collecting information indicating visual target positions from a plurality of viewpoints existing in a space, and a function of evaluating positions in the space in accordance with density of the visual target positions.

[Object] To appropriately decide an image generation target position in a space. [Solution] Provided is an information processing device including a processor configured to achieve a function of collecting information indicating visual target positions from a plurality of viewpoints existing in a space, and a function of evaluating positions in the space in accordance with density of the visual target positions.

A variable focal length lens system includes a tunable acoustic gradient (TAG) lens; a TAG lens controller; and processor(s) configured to: (a) operate the TAG lens controller to drive a periodic modulation of the TAG lens optical power at a TAG lens resonant frequency, using a first amplitude driving signal that provides a first focal Z range extending between peak focus distances Z1max+ and Z1max−; and expose a first image using a first exposure increment approximately at the timing of Z1max+ or Z1max−; (b) adjust the TAG lens controller to drive the periodic modulation using a second amplitude driving signal that provides a second focal Z range extending between peak focus distances Z2max+ and Z2max−; and expose a second image using a second exposure increment approximately at the timing of Z2max+ or Z2max−; and (c) perform processing that includes determining focus metric values for the first and second images.