Abstract: There is provided an inspection method that includes determining, as a reference blur component, the magnitude of the blur component of a range, the range being a range where a number of first inspection images among the plurality of inspection images falling within the range is the largest among a plurality of ranges. The inspection method includes correcting, based on the reference blur component, a second inspection image among the plurality of inspection images having the magnitude of the blur component falling outside the range. The inspection method includes comparing the first inspection image and the corrected second inspection image with a reference image, the first inspection image having the magnitude of the blur component falling within the predetermined range, the reference image being generated in advance for the measurement object.

Abstract: An image processing device, including a first corrector configured to generate a first corrected image obtained by correcting a local intensity of an input image based on a complexity of an intensity distribution of the input image and an intensity value of an area around a pixel; and a second corrector configured to generate a second corrected image obtained by correcting a global intensity of the first corrected image based on a histogram of the input image.

Abstract: A method and apparatus for classification using a neural network. A classification apparatus includes at least one processor and a memory. The memory is configured to store a classifier and a preprocessor including a defensive noise generator. The at least one processor generates defensive noise from an input image through the defensive noise generator in the preprocessor, generates a combined image by combining the input image and the defensive noise, and outputs a classification result by inputting the combined image to the classifier.

Abstract: An information processing system, which determines a partial area used in an erectness determination which determines whether a determination target image is erect based on a determination feature in the partial area in an image having a predetermined format and a position of the partial area in a state in which the image having the predetermined format is erect, includes processor to receive input of a plurality of learning images having the predetermined format, extract area common to the plurality of learning images in an erect state as a candidate for the partial area, determine common area reliability indicative of a degree to which the candidate for the partial area is suitable as the partial area used in the erectness determination, and determine the partial area used in the erectness determination from the candidate for the partial area, based on the common area reliability.

Abstract: A method is provided for inspection of an item comprising acquiring a projection image of the item using a radiation imaging system and obtaining a plurality of simulated projection images of the item or a component thereof, based on a simulation of a numerical three-dimensional model. A relative orientation of the item with respect to the imaging system is determined by comparing the projection image to the plurality of simulated images, and at least one angle of rotation is determined by taking into account a viewing angle and the relative orientation. The method further comprises moving the item and/or the imaging system in accordance with the at least one angle of rotation and acquiring a further projection image of the item.

Abstract: Systems and methods of the present disclosure physical object-based passwords by registering an object password including first object representations, first background scene representations, and a first presentation sequence from first image data. Receiving second image data and a second presentation sequence that is a second order in which the user has presented second physical objects to an image acquisition device, and detecting in the second image data second object representations of the second physical objects, and second background scene representations. Computing a probability that the user is a permissioned user by inputting the first image data and the second image data into a machine learning model configured to compute the probability based on comparing the object password and the second image data. Tagging the user as the permissioned user or non-permissioned user based on the probability.

Abstract: Disclosed herein is a method and a system for reconstructing a three-dimensional image of an object, based on stitched images of the object obtained using multiple beams.

Abstract: A computer system is structured to determine a density of particulate matter in a diesel particulate filter (DPF) sample. The computer system includes a processing circuit having a processor and a memory. The processing circuit is structured to generate a computed tomography (CT) scan-based image of the DPF sample; and, segment the CT scan-based image of the DPF sample into a plurality of regions. For at least one region from the plurality of regions, the processing circuit is structured to determine a density of a portion of the DPF sample corresponding to the at least one region of the CT scan-based image of the DPF sample and cause an electronic display of a user device to display the CT scan-based image including the at least one region and an indication of the density for the at least one region.

Abstract: A processor acquires a first radiographic image and a second radiographic image that include the same subject and have different S/N ratios. The processor derives a processing content of a first graininess suppression process on the first radiographic image having a higher S/N ratio of the first radiographic image and the second radiographic image and derives a processing content of a second graininess suppression process on the second radiographic image on the basis of the processing content of the first graininess suppression process. The processor performs a graininess suppression process on the second radiographic image on the basis of the processing content of the second graininess suppression process.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilizes a neural network having a hierarchy of hierarchical point-wise refining blocks to generate refined segmentation masks for high-resolution digital visual media items. For example, in one or more embodiments, the disclosed systems utilize a segmentation refinement neural network having an encoder and a recursive decoder to generate the refined segmentation masks. The recursive decoder includes a deconvolution branch for generating feature maps and a refinement branch for generating and refining segmentation masks. In particular, in some cases, the refinement branch includes a hierarchy of hierarchical point-wise refining blocks that recursively refine a segmentation mask generated for a digital visual media item.

Abstract: A processing device and method for a panoramic image includes converting the panoramic image to a cubemap format; under the cubemap format, calculating a depth information image of the panoramic image, and performing a seamless processing on the depth information image. The format of the depth information image is converted to the original format from the cubemap format to obtain the depth information image corresponding to the panoramic image.