A method for quantifying an exposure dose for a surface is disclosed. The method may include emitting one or more beams of 222 nm light onto a portion of the surface using one or more far ultraviolet (UV) light sources capable of emitting 222 nm light, the portion of the surface being coated with one or more fluorescent coatings. The method may include capturing images of the portion of the surface. The method may include adjusting one or more image characteristics for the captured images using one or more filtering methods. The method may include generating a histogram of the adjusted images based on the one or more filtering methods. The method may include determining a pixel surface area for the generated histogram. The method may include calculating the exposure dose for the surface based on the generated pixel surface area and a predetermined calibration curve.

A system and method for monitoring performance of an industrial process includes an input port for receiving signals representative of one or more performance parameters generated by the industrial process, a user interface including a display and a controller that is operably coupled with the input port and the user interface. The controller is configured to repeatedly receive signals over time via the input port representative of the one or more performance parameters of the industrial process and to generate a plurality of snapshots, wherein each snapshot includes a graphical representation of the one or more performance parameters of the industrial process at a corresponding time. The controller is configured to generate an animatable heat map including two or more of the plurality of snapshots arranged temporally and to display the animatable heat map on the display.

A computer can operated, including detecting defects, or other physical features, of artificial objects. Image data is received of one or more artificial objects, and applying an image segmentation process to the image data to detect predetermined defects of the one or more artificial objects. The image segmentation process identifies one or more regions of the image data determined to have a likelihood of showing one or more of the predetermined defects. The identified one or more regions is output. The image segmentation process determines severity metrics for the defects in the one or more regions, wherein a severity metric represents a severity or significance of a defect. The image segmentation process further determines a confidence factor for each region of the one or more regions, wherein the confidence factor represents a likelihood of the presence of a predetermined defect in the region.

The present invention enables highly accurate analysis when visualizing analysis results in spectral imaging.

An surface analysis method includes: acquiring spectral image data regarding a sample surface with use of a spectral camera; extracting n wavelengths dispersed in a specific wavelength range in the acquired spectral image data, and converting spectrums of the wavelengths in the spectral image data into n-dimensional spatial vectors for each pixel; normalizing the spatial vectors of the pixels; clustering the normalized spatial vectors into a specific number of classifications; and identifying and displaying pixels clustered into the classifications, for each of the classifications.

A computer-implemented method of automated X-ray inspection during the production of printed circuit board, PCB, assemblies. The method includes capturing an X-ray image of a PCB assembly, determining a first error indicator based on image processing of the captured X-ray image, determining, in case the first error indicator indicates the PCB assembly as faulty, a second error indicator based on the captured X-ray image using a trained adaptive algorithm, and outputting the second error indicator as a result of the inspection.

The present disclosure provides a method of processing an image, a device, and a medium. The method of processing the image includes: performing an image processing on an original image to obtain a component image for brightness of the original image; determining at least one of the original image and the component image as an image to be processed; classifying a pixel in the image to be processed, so as to obtain a classification result; processing the image to be processed according to the classification result, so as to obtain a target image; and determining an image quality of the original image according to the target image.

The present invention relates to the technical field of identification on adulterated meat, and in particular, to a method for identifying raw meat and high-quality fake meat based on a gradual linear array change of a component. The present invention spatially characterizes changing rules of featured components in the meat with the utilization of sensitivities of the visible/near-infrared spectral signals to changes of the components in the meat and the advantage that spectral scanning can acquire optical signals of the samples spatially and consecutively, further constructs the identification model according to differences in components and spectra of a region of interest in the hyperspectral image by taking a derivative for characterizing rates of change of the featured components.

In one embodiment, a method is provided. The method includes obtaining a sequence of images of a three-dimensional volume of a material. The method also includes determining a set of features based on the sequence of images and a first neural network. The set of features indicate microstructure features of the material. The method further includes determining a set of material properties of the three-dimensional volume of the material based on the set of features and a first transformer network.

A method includes patterning a hard mask over a target layer, capturing a low resolution image of the hard mask, and enhancing the low resolution image of the hard mask with a first machine learning model to produce an enhanced image of the hard mask. The method further includes analyzing the enhanced image of the hard mask with a second machine learning model to determine whether the target layer has defects.

The present disclosure provides a method and device for detecting an image category. The method includes: acquiring a sample data set including a plurality of sample images labeled with a category, the sample data set including a training data set and a verification data set; training a deep learning model using the training data set to obtain, according to different numbers of training rounds, at least two trained models; testing the at least two trained models using the verification data set to generate a verification test result; generating, based on the verification test result, a verification test index; determining, according to the verification test index, a target model from the at least two trained models; and predict a to-be-tested image of the target object using the target model to obtain the category of the to-be-tested image.