A multi-view vision inspection system includes a darkroom; and a plurality of cameras, the plurality of cameras including a plurality of first cameras, a plurality of second cameras and a plurality of third cameras, the plurality of first cameras being arranged spaced from one another at a top of an interior of the darkroom, the plurality of second cameras being arranged spaced from one another at a bottom of the interior of the darkroom, and the plurality of third cameras being arranged spaced from one another on a side wall of the interior of the darkroom in a circumferential direction of the darkroom. The multi-view vision inspection system can measure each angle of a workpiece to be inspected by using the plurality of cameras above.

The present disclosure relates to a method for inspecting a container body adapted and configured to hold substrates, comprising the steps of directing light from a light source onto a reflector element positioned within an interior space of the container body, such that the light is reflected to illuminate at least one interior surface of the container body, wherein the light is reflected by the reflector element in a diffuse manner and generating at least one image of the at least one interior surface by means of at least one camera, and evaluating the state of the container body on the basis of the at least one image.

A sensing and computing system and method for capturing images and data regarding an object and calculating one or more parameters regarding the object using an internal, integrated CPU/GPU. The system comprises an imaging system, including a depth imaging system, color camera, and light source, that capture images of the object and sends data or signals relating to the images to the CPU/GPU, which performs calculations based on those signals/data according to pre-programmed algorithms to determine the parameters. The CPU/GPU and imaging system are contained within a protective housing. The CPU/GPU transmits information regarding the parameters, rather than raw data/signals, to one or more external devices to perform tasks in an industrial environment related to the object imaged.

There is provided a method of examination of a semiconductor specimen and a system thereof. The method comprises: using a trained Deep Neural Network (DNN) to process a fabrication process (FP) sample, wherein the FP sample comprises first FP image(s) received from first examination modality(s) and second FP image(s) received from second examination modality(s) which differs from the first examination modality(s), and wherein the trained DNN processes the first FP image(s) separately from the second FP image(s); and further processing by the trained DNN the results of such separate processing to obtain examination-related data specific for the given application and characterizing at least one of the processed FP images. When the FP sample further comprises numeric data associated with the FP image(s), the method further comprises processing by the trained DNN at least part of the numeric data separately from processing the first and the second FP images.

A gas detection device includes: a processor that visualizes a gas by performing image processing on infrared image data in an inspection region imaged by an imaging device; a display that displays an inspection image that reflects a result of the image processing; and an input interface that receives an input of supplementary information on the inspection image displayed on the display.

A system and method are disclosed for training a system or a model to allow estimation of the value of livestock that is farmed for monetary gain. The various aspects of the invention include generation of data that is used to supplement or augment capture or real data, wherein the subject of the data is an animal. Labels or attributes are generated and validated.

An inspection system includes a light source, a mirror, Galvano mirrors, a casing that holds the mirror and the Galvano mirrors inside and includes an attachment portion for attaching an optical element, and a control unit that controls a deflection angle of the Galvano mirrors, wherein the control unit controls the deflection angle so that an optical path optically connected to a semiconductor device is switched between a first optical path passing through the Galvano mirrors and the mirror, and a second optical path passing through the Galvano mirrors and the attachment portion, and controls the deflection angle so that the deflection angle when switching to the first optical path has been performed and the deflection angle when switching to the second optical path has been performed do not overlap.

An x-ray inspection apparatus may comprise an x-ray source, an x-ray detector, and a drive assembly. The drive assembly may be configured to lift a part carrier such that the part carrier is disengaged from a feed assembly and an object mounted on the part carrier is positioned between the x-ray source and the x-ray detector. The feed assembly may be configured to feed part carriers into and out of the x-ray inspection apparatus. The drive assembly may be further configured to subsequently lower the part carrier such that the part carrier is reengaged with the feed assembly.

An inspection apparatus includes an image-capturing unit configured to capture an image of an article, an inspection unit configured to inspect the article, a generation-processing unit, and a storage-processing unit. The generation-processing unit generates a data group related to an inspection result obtained by the inspection unit, the data group including article information for distinguishing the article, the inspection result obtained by the inspection unit, captured-image information for distinguishing a captured image captured by the image-capturing unit from another captured image, and a hash value of the captured image that are associated with each other. The storage-processing unit stores the data group and the captured image in respective different storage units.

A method and a system for automated plant surveillance and manipulation are provided. Pursuant to the method and the system, images of target plants are obtained through a machine vision system having multiple cameras. The obtained images of the target plants are processed to determine tissue candidates of the target plants and to determine a position and an orientation of each tissue candidate. A tool is manipulated, based on the position and the orientation of each tissue candidate, to excise each tissue candidate to obtain tissue samples. The tissue samples are transported for subsequently manipulation including live processing of the tissue samples or destructive processing of the tissue samples.