A method is performed at an electronic device with one or more processors and a non-transitory memory. The method includes obtaining a plurality of volumetric regions of a physical environment based on a first representation of the physical environment at a first time. Each of the plurality of volumetric regions includes a corresponding portion of the physical environment. The method includes determining a first feature property based on a query. The method includes identifying a first volumetric region of the first plurality of volumetric regions based on determining that the first volumetric region satisfies a criterion with respect to the first feature property.

In one aspect, a method includes receiving an image of a target subject; determining a direction in response to the receipt of the image, the direction being one in which the target subject was likely to move during a time period in the past or is likely to move during a time period in the future; determining a target area within which another image of the target subject can be expected to appear based on the determined direction; and determining if a portion of a subsequent image is outside the determined target area to identify if the subsequent image is one relating to the target subject, wherein the subsequent image is one taken during the time period in the past or during the time period in the future.

A method for defect classification is described. The method includes storing a plurality of defect classes in terms of a plurality of classification rules in a multi-dimensional feature space, wherein the plurality of classification rules, for each defect class of the plurality of defect classes, defines in the multi-dimensional feature space a boundary of a region associated with the defect class; receiving one or more electron beam image data associated with a plurality of defects detected in one or more display devices on a large area substrate under inspection; applying, by a processor, an automatic classifier to the electron beam image data, the automatic classifier based on the plurality of classification rules; and identifying the plurality of defects each classified with at least a first level of confidence based on at least one confidence threshold.

A display control unit displays a tomographic image of a specific tomographic plane in a first medical image on a display unit. A finding classification unit classifies each pixel of a partial region of the first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding in the partial region. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the partial region and a second feature amount for each finding calculated in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image.

A issuing apparatus is configured to acquire a first face image obtained by imaging a face of a person, output a verification image including the first face image and verification information for verifying validity to a printer that issues the ID card by printing the verification image on a card-shaped recording medium, and store, in a memory, a first captured image obtained by capturing the verification image printed on the ID card via a camera. A verification apparatus is configured to acquire, as a second captured image, an image obtained by capturing a verification image printed on an ID card to be verified via a camera, and verify authenticity of the ID card to be verified by comparing density characteristics of each of the first captured image acquired from the memory and the second captured image acquired from the camera.

A scene change detection method. The scene change detection method including: acquiring an image pair including two or more different images; generating a pair of feature maps corresponding to the image pair using a pre-trained image analysis model, and comparing the pair of feature maps with each other to calculate a similarity; calculating an asymmetry based on data distribution of the similarity to calculate an adaptive reference corresponding to the similarity based on asymmetry; and correcting the similarity based on the adaptive reference to generate a scene change mask representing an area where a change has occurred in the image pair.

There is provided a system and method for defect examination on a semiconductor specimen. The method comprises obtaining an original image of the semiconductor specimen, the original image having a first region annotated as enclosing a defective feature; specifying a second region in the original image containing the first region, giving rise to a contextual region between the first region and the second region; identifying in a target image of the specimen a set of candidate areas matching the contextual region in accordance with a matching measure; selecting one or more candidate areas from the set of candidate areas; and pasting the first region or part thereof with respect to the one or more candidate areas, giving rise to an augmented target image usable for defect examination on the semiconductor specimen.

A self-checkout system and techniques for illuminating one or more items placed within a predefined zone of the self-checkout system are provided. In one aspect, a self-checkout system includes one or more cameras, a light source, and a computing device. The computing device is configured to implement an operation, including: receiving one or more images of one or more items placed within the predefined zone, the images being captured by the cameras; determining an identity of the one or more items placed within the predefined zone, based at least in part on the captured images; and causing the light source to project an illuminated indicator onto at least one item of the one or more items placed within the predefined zone, based on the identity of the at least one item.

Visual inspection systems, and methods for utilizing such visual inspection systems, are disclosed for providing technological solutions that improve cleanliness, transmission performance, traceability, and security of optical network components such as optical assemblies. The visual inspection systems, and the methods for utilizing such visual inspection systems, also aid in preventing the counterfeiting of network components.

A computer-implemented method for growing a region in a medical image. The method comprises receiving a medical image, receiving a seed point in the medical image, and extracting first features from the medical image. A trained first machine learning algorithm is applied to the extracted first features to obtain a classification of tissue at the seed point. A trained second machine learning algorithm is applied to the extracted first features to obtain a first region of confidence relative to the seed point. The first region of confidence is a region in which the tissue is expected to be the same as at the seed point. In a graphical user interface, a region in the medical image is grown from the seed point to include the first region of confidence.