An inspection apparatus including an image generation device which generates a second image corresponding to a first image, and a defect detection device which detects a defect in the second image. Each of the first and second image includes partial regions each including pixels. The defect detection device is configured to estimate a first value indicating a position difference between the first and second image for each of the partial regions, based on a luminance difference between the first and second image, estimate a second value indicating a reliability of the first value for each of the partial regions, and estimate a position difference between the first and second image for each of the pixels, based on the first and second value estimated for each of the partial regions.

We propose a neural network-implemented method for base calling analytes. The method includes accessing a sequence of per-cycle image patches for a series of sequencing cycles, where pixels in the image patches contain intensity data for associated analytes, and applying three-dimensional (3D) convolutions on the image patches on a sliding convolution window basis such that, in a convolution window, a 3D convolution filter convolves over a plurality of the image patches and produces at least one output feature. The method further includes beginning with output features produced by the 3D convolutions as starting input, applying further convolutions and producing final output features and processing the final output features through an output layer and producing base calls for one or more of the associated analytes to be base called at each of the sequencing cycles.

A method of identifying a historical alert that is similar to an alert associated with a detected deviation from an operational state of a device includes receiving feature data including time series data for multiple sensor devices associated with the device and receiving an alert indicator for the alert. The method includes processing a portion of the feature data that is within a temporal window associated with the alert indicator to generate feature importance data for the alert. The feature importance data includes values indicating relative importance of each of the sensor devices to the alert. The method also includes identifying one or more historical alerts that are most similar, based on the feature importance data and stored feature importance data, to the alert.

An apparatus and method for inspecting articles incorporating positive temperature coefficient resistors. The inspection apparatus includes a computing device, a power source, a housing, a support, and a thermal imager, each mounted within an interior volume of the housing. The inspection method includes receiving a first thermal image of the unpowered article mounted within the support and receiving a second thermal image of the powered article after an optimized time delay. The method further includes outputting a health indication of the positive temperature coefficient resistors based on a comparison of the first thermal image and the second thermal image.

Systems can be configured for detecting license plates and recognizing characters in license plates. In an example, a system can receive an image and identify one or more regions in the image that include a license plate. Character recognition can be performed in the one or more regions to determine contents of a candidate license plate. Location-specific information about a license plate format can be used together with the determined contents of the candidate license plate to determine if the recognized characters are valid.

According to one embodiment, an inspection apparatus includes an image generation device which generates a second image corresponding to a first image and a defect detection device which detects a defect in the second image with respect to the first image. The defect detection device is configured to extract a first partial region in which an amount of change of a luminance of the first image and an amount of change of a luminance of the second image have a correlation, and correct, in the first partial region, the luminance of the first image with respect to the luminance of the second image.

An imaging unit, an extraction unit, a feature amount pair generation unit, and an imaging parameter adjustment unit are included. The imaging unit acquires images obtained by imaging each of N (N≥3) types of objects a plurality of times by setting a value of a specific imaging parameter, among a plurality of types of imaging parameters, as a certain candidate value and changing a value of the remaining imaging parameter. The extraction unit extracts a feature amount from each of the images. The feature amount pair generation unit generates, as a first feature amount pair for each of the N types of objects, a feature amount pair in which two feature amounts constituting the feature amount pair are extracted from images of objects of the same type, and generates, as a second feature amount pair for every combination of the N types of objects, a feature amount pair in which two feature amounts constituting the feature amount pair are extracted from a images of objects of the different types. The imaging parameter adjustment unit generates a first distribution that is a distribution of collation scores of the first feature amount pairs, generates a second distribution that is a distribution of collation scores of the second feature amount pairs, and on the basis of a degree of separation between the first distribution and the second distribution, determines the propriety of adopting the candidate value.

A system for digitizing gauges, lights and other human-readable machine gauges and functions and status without interfering with the operation of the machine or requiring reworking or interfering with the existing machine wiring, signaling, electrical or mechanical elements or operating modes, or adding new digitizing equipment to the machine.

An augmented reality (AR) providing method for recognizing a context using a neural network includes acquiring, by processing circuitry, a video; analyzing, by the processing circuitry, the video and rendering the video to arrange a virtual object on a plane included in the video; determining whether a scene change is present in a current frame by comparing the current frame included in the video with a previous frame; determining a context recognition processing status for the video based on the determining of whether the scene change is present in the current frame; and in response to determining that the context recognition processing status is true, analyzing at least one of the video or a sensing value received from a sensor using the neural network and calculating at least one piece of context information, and generating additional content to which the context information is applied and providing the additional content.

A camera includes an image sensor having a first recording channel of a first sensitivity for recording first image data including first pixels and a second recording channel of a second sensitivity lower than the first sensitivity for recording second image data including second pixels. The first pixels and second pixels are associated with one another by capturing a same object area. A control and evaluation unit processing the image data is configured to suppress noise effects in the second image data using a noise suppression filter that assigns a new value to a respective considered second pixel based on second pixels in a neighborhood of the considered second pixel. The noise suppression filter takes the second pixels in the neighborhood into account with a weighting that depends on how similar first pixels associated with the second pixels are to the associated first pixel of the respective considered second pixel.