A processing system comprising a first imaging system configured to capture a first image based on a terahertz wave from an inspection target, a second imaging system configured to capture a second image of the inspection target based on an electromagnetic wave of a wavelength different from the terahertz wave, and a processor configured to process the first image and the second image, wherein the processor detects an inspection region based on the second image and processes information of a region of the first image corresponding to the inspection region.

A processing system comprising a first imaging system configured to capture a first image based on a terahertz wave from an inspection target, a second imaging system configured to capture a second image of the inspection target based on an electromagnetic wave of a wavelength different from the terahertz wave, and a processor configured to process the first image and the second image, wherein the processor detects an inspection region based on the second image and processes information of a region of the first image corresponding to the inspection region.

Provided are a display panel and a display device. An array layer is located on a substrate. A display layer is located on a side of the array layer facing away from the substrate and includes light-emitting elements. A color filter layer is located on a side of the display layer facing away from the array layer. The color filter layer includes a light-blocking layer and color filters. The light-blocking layer includes first light-blocking part. At least one light-transmitting aperture is disposed in the first light-blocking part. First metal part overlaps the first light-blocking part. Further provided is a display device including the preceding display panel.

A method comprising determining a quantitative dispersion image of an object based on a set of quantitative phase images, each quantitative phase image of the set of quantitative phase images having been obtained with a respective different illumination light wavelength.

Provided is a fingerprint sensing system disposed under a display. The fingerprint sensing system includes a sensor and a controller. The sensor has a plurality of sensing pixels arranged into an array, and the sensing pixels includes at least one functional sensing pixel. The controller is electrically connected to the sensor. The controller calculates an environmental parameter according to a signal obtained by the at least one functional sensing pixel.

The invention relates to the domain of microscope based imaging. The invention provides methods and apparatuses for providing improved microscope based digital imaging solutions that are capable of providing high quality images with a high level of image detail. The invention additionally provides solutions for artificial intelligence based controlling of a digital microscope's imaging functions to enable bright field/dark field imaging functionality to be combined with spectroscopic functions to obtain higher detail and more meaningful information about a specimen sample.

The invention relates to the domain of microscope based imaging. The invention provides methods and apparatuses for providing improved microscope based digital imaging solutions that are capable of providing high quality images with a high level of image detail. The invention additionally provides solutions for artificial intelligence based controlling of a digital microscope's imaging functions to enable bright field/dark field imaging functionality to be combined with spectroscopic functions to obtain higher detail and more meaningful information about a specimen sample.

Non-parametric transforms such as t-distributed stochastic neighbor embedding (tSNE) are used to analyze multi-parametric data such as data derived from flow cytometry or other particle analysis systems and methods. These transforms may be included for dimensionality reduction and identification of subpopulations (e.g., gating). By nature, non-parametric transforms cannot transform new observations without training a new transformation based on the entire dataset including the new observations. The features described parameterize non-parametric transforms using a neural network thereby allowing a small training dataset to be transformed using non-parametric techniques. The training dataset may then be used to generate an accurate parametric model for assessing additional events in a manner consistent with the initial events.

An information processing apparatus includes a processor that processes first imaging data captured by a first imaging function using visible light and second imaging data captured by a second imaging function using infrared light; and a light-emitting part that outputs infrared light toward an imaging target by the second imaging function. The processor detects a face area from an image of the first imaging data, detects the brightness of the imaging target based on the first imaging data stored in a memory, and when the brightness detected is equal to or more than a predetermined threshold value, the processor controls the output of the light-emitting part depending on whether or not the face area is detected, while when the brightness is less than the predetermined threshold value, the processor controls the output of the light-emitting part regardless of detection of the face area.

An information processing apparatus includes a processor that processes first imaging data captured by a first imaging function using visible light and second imaging data captured by a second imaging function using infrared light; and a light-emitting part that outputs infrared light toward an imaging target by the second imaging function. The processor detects a face area from an image of the first imaging data, detects the brightness of the imaging target based on the first imaging data stored in a memory, and when the brightness detected is equal to or more than a predetermined threshold value, the processor controls the output of the light-emitting part depending on whether or not the face area is detected, while when the brightness is less than the predetermined threshold value, the processor controls the output of the light-emitting part regardless of detection of the face area.