A system may include a processor and a memory. The memory may include computer-executable code that, when executed by the processor, causes the processor to retrieve a workflow dataset from a database based on a query input associated with an industrial automation device. The workflow dataset may include an instruction associated with one or more operations for the industrial automation device and a virtual object associated with the one or more instructions and the industrial automation device. The memory may include computer-executable code that, when executed by the processor, causes the processor to transmit a first portion of the workflow dataset to a computing device corresponding to a first instruction. The memory may include computer-executable code that, when executed by the processor, causes the processor to transmit a second portion of the workflow dataset to the computing device in response to determining that the first instruction is completed.

A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller having one or more processors configured to receive a plurality of training images captured at one or more focal positions. The one or more processors may generate a machine learning classifier based on the plurality of training images. The one or more processors may receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features. The one or more processors may determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier. The one or more processors may receive one or more target images captured at the one or more target focal positions, the target images including the one or more target features of the target specimen, and determine overlay based thereon.

A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller having one or more processors configured to receive a plurality of training images captured at one or more focal positions. The one or more processors may generate a machine learning classifier based on the plurality of training images. The one or more processors may receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features. The one or more processors may determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier. The one or more processors may receive one or more target images captured at the one or more target focal positions, the target images including the one or more target features of the target specimen, and determine overlay based thereon.

An improved method for examining an article by using a vision system is presented. Also presented is a vision system for use within such a method.

Security related anomalies in the data related to network entities are identified, and a risk score is assigned to each entity based on the anomalies. Visualization data is generated for a color-coded interactive visualization. Generating the visualization data includes assigning each entity to a separate polygon to be displayed concurrently on a display screen; selecting a size of each polygon to indicate one of: a number of security related anomalies associated with the entity, or a risk level assigned to the entity, where the risk level is based on the risk score of the entity, and selecting a color of each polygon to indicate the other one of: the number of security related anomalies associated with the entity, or the risk level assigned to the entity; and causing, the color-coded interactive visualization to be displayed on a display device based on the visualization data.

The present invention discloses a clustering algorithm-based multi-parameter cumulative calculation method for lower limb vascular calcification indexes, including the following steps: firstly carrying out super-pixel segmentation of a CT image, and enabling calcified spots in the CT image to be segmented in each super-pixel region; after the super-pixel segmentation is accomplished, extracting a brightness characteristic value of a super-pixel region where the calcified spots are located by using a Lab color space, and performing edge detection and contour extraction on the calcified spots in the image; and after edge detection and contour extraction, fitting the calcified spots in the image by using a segmented ellipse, and extracting the area of the calcified spots after optimizing an ellipse contour.

A process for converting an arbitrary input digital pathology image into an output digital pathology image that shares statistical information with a reference image. The input digital pathology image is separated into two or more image sections. Each image section is encoded, using a first convolutional neural network, into one or more feature maps. The one or more feature maps of each image section are modified based on statistical information derived from a reference image. The modified feature map(s) of each image section is/are then decoded to construct a respective image section for an output image. The constructed image sections for an output image then form the output image.

A process for converting an arbitrary input digital pathology image into an output digital pathology image that shares statistical information with a reference image. The input digital pathology image is separated into two or more image sections. Each image section is encoded, using a first convolutional neural network, into one or more feature maps. The one or more feature maps of each image section are modified based on statistical information derived from a reference image. The modified feature map(s) of each image section is/are then decoded to construct a respective image section for an output image. The constructed image sections for an output image then form the output image.

The present invention relates to spectral correction. A spectral correction apparatus is described that is configured to identify a voltage fluctuation in the X-ray tube and to parameterize the high voltage fluctuation to correct the effective X-ray spectrum per individual frame.

A method for compensating motion in positron emission tomographic, PET, data comprising coincident lines of response from positron-emitting position markers, includes: detecting a slippage of one or more of the position markers; determining slippage correction parameters based on the detected slippage; and applying motion correction to the PET data by taking into account the slippage correction parameters, thereby obtaining a motion-compensated PET data.