A non-destructive system for detecting anomalies in weldment of a pipeline is provided including an imaging apparatus, an anomaly detection unit, and a computing device. The imaging apparatus produces image segments corresponding to segments of the circumferential area of the weldment. The anomaly detection unit includes an artificial intelligence platform that processes and analyzes the image segments to identify at least one of a type, size, and location of a welding anomaly within the weldment using a database of truth data. The computing device includes a graphical user interface that displays the image segments with an overlay of information relating to at least one of the type, size, and location of the welding anomaly to the user.

A non-destructive system for detecting anomalies in weldment of a pipeline is provided including an imaging apparatus, an anomaly detection unit, and a computing device. The imaging apparatus produces image segments corresponding to segments of the circumferential area of the weldment. The anomaly detection unit includes an artificial intelligence platform that processes and analyzes the image segments to identify at least one of a type, size, and location of a welding anomaly within the weldment using a database of truth data. The computing device includes a graphical user interface that displays the image segments with an overlay of information relating to at least one of the type, size, and location of the welding anomaly to the user.

A method for metrology includes directing at least one illumination beam to illuminate a semiconductor wafer on which at least first and second patterned layers have been deposited in succession, including a first target feature in the first patterned layer and a second target feature in the second patterned layer, overlaid on the first target feature. A sequence of images of the first and second target features is captured while varying one or more imaging parameters over the sequence. The images in the sequence are processed in order to identify respective centers of symmetry of the first and second target features in the images and measure variations in the centers of symmetry as a function of the varying image parameters. The measured variations are applied in measuring an overlay error between the first and second patterned layers.

A system for facilitating developmental monitoring of children comprises one or more processors and one or more hardware storage devices storing instructions that are executable by the one or more processors to configure the system to (i) access a set of image data depicting a subject, (ii) extract a set of features from the set of image data, the set of features indicating one or more body characteristics of the subject as represented in the set of image data, and (iii) determine a set of developmental metrics for the subject based upon the set of features, the set of developmental metrics being indicative of a developmental state for the subject.

An image processing device includes a processor, and the processor is configured to output information for displaying a three-dimensional target part image as a three-dimensional image showing a target part, on a display device, calculate a virtual axis of the target part in the three-dimensional target part image, output information for displaying a two-dimensional image corresponding to a first cross section crossing a first position on the virtual axis, on the display device, change a geometrical characteristic of the first cross section in response to an instruction to change the geometrical characteristic, and output information for displaying a two-dimensional image corresponding to a second cross section obtained by changing the geometrical characteristic, on the display device.

A method includes obtaining, using at least one processing device, a vanishing point and a boundary based on image data associated with a scene, where the boundary is associated with a detected object within the scene. The method also includes repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple patches within the boundary and (ii) determining a similarity of the image data contained within the multiple patches. The method further includes identifying, using the at least one processing device, a modification to be applied to the boundary based on the identified patches and the determined similarities. In addition, the method includes generating, using the at least one processing device, a refined boundary based on the modification, where the refined boundary identifies a specified portion of the detected object.

A method for the computer-aided completion of a 3D partial model—formed by points—of a partial region of an object that is captured by at least one capture device, wherein the 3D partial model can be supplemented with a hidden or missing partial region of the object situated outside the 3D partial model of the object that is to be completed, is provided. The method includes determining a geometry of the object, identifying the hidden or missing partial region of the object on the basis of the determined geometry of the object, supplementing the 3D partial model to form a complete 3D model with the identified hidden or missing partial region of the object, and c) outputting the completed 3D model at an output unit.

A method for the computer-aided completion of a 3D partial model—formed by points—of a partial region of an object that is captured by at least one capture device, wherein the 3D partial model can be supplemented with a hidden or missing partial region of the object situated outside the 3D partial model of the object that is to be completed, is provided. The method includes determining a geometry of the object, identifying the hidden or missing partial region of the object on the basis of the determined geometry of the object, supplementing the 3D partial model to form a complete 3D model with the identified hidden or missing partial region of the object, and c) outputting the completed 3D model at an output unit.

A system performs mask rule checks (MRC) for curvilinear shapes. The width of a curvilinear shape is different along different parts of the shape. A medial axis for a curvilinear shape is determined. The medial axis is trimmed to exclude portions that are within a threshold distance from corners or too far from edges. The trimmed medial axis is used to perform width checks for mask rules. The system generates medial axis between geometric shapes and uses it to determine whether two geometric shapes are at least a threshold distance apart. The system performs acute angle checks for sharp corners. The system determines angles using lines drawn from vertices to end points on the boundary of the shape that are at a threshold distance. These angles are used for checking acute angle mask rule violations.

According to one aspect of the presently disclosed subject there is provided a device configured figured to allow illumination of light towards a tunable filter, each time towards a different portion thereof, and detect the optical response, (e.g., transmission or reflection from the portion that is illuminated with light). By detecting optical response of isolated illuminations towards different portions of the tunable filter each time, the state of the tunable filter at the illuminated portion, e.g. the optical gap between a movable member and a stationary member of the tunable filter, can be determined. By monitoring different portions of the tunable filter, the general state of the tunable filter is determined. For example, the general state of the tunable filter may be determined based on the optical gaps at different portions of the tunable filter while the actuation parameters are maintained unchanged.