A user device for imaging a scene includes a first plurality of optical sensors coupled to a substrate for collecting an image of a scene and a second plurality of optical sensors coupled to the substrate for collecting spectral information from the image. A plurality of sets of interference filters are associated with the second plurality of optical sensors, where each interference filter of a set of interference filters is configured to pass light in one of a plurality of wavelength ranges to one or more optical sensors of the second plurality of optical sensors and each optical sensor of the plurality of optical sensors is associated with a spatial area of the image. A processor is adapted to receive an output from the first plurality of optical sensors and the second plurality of optical sensors and determine, based on the spectral information, a target area within the scene. The processor is further adapted to retrieve focus data for the scene, determine a focus distance for the target area and output user-perceptible information to an output display.

An image processing apparatus comprises: an image forming unit configured to form an image on a recording medium using a plurality of color materials on the basis of first image data showing an image; and an estimating unit configured to estimate a characteristic of each one of the plurality of color materials in a target region on the formed image on the basis of second image data obtained by reading the formed image. The estimating unit selects an estimation processing unit to use from among a plurality of estimation processing units used for different estimation methods on the basis of a combination of the color materials in the target region.

An image processing apparatus comprises: an image forming unit configured to form an image on a recording medium using a plurality of color materials on the basis of first image data showing an image; and an estimating unit configured to estimate a characteristic of each one of the plurality of color materials in a target region on the formed image on the basis of second image data obtained by reading the formed image. The estimating unit selects an estimation processing unit to use from among a plurality of estimation processing units used for different estimation methods on the basis of a combination of the color materials in the target region.

Methods and systems for using multiple hyperspectral cameras sensitive to different wavelengths to predict characteristics of objects for further processing, including recycling, are described. The multiple hyperspectral images can be used to predict higher resolution spectra by using a trained machine learning model. The higher resolution spectra may be more easily analyzed to sort plastics into a recyclability category. The hyperspectral images may also be used to identify and analyze dark or black plastics, which are challenging for SWIR, MWIR, and other wavelengths. The machine learning model may also predict the base polymers and contaminants of plastic objects for recycling. The hyperspectral images may be used to predict recyclability and other characteristics using a trained machine learning model.

Methods and systems for using multiple hyperspectral cameras sensitive to different wavelengths to predict characteristics of objects for further processing, including recycling, are described. The multiple hyperspectral images can be used to predict higher resolution spectra by using a trained machine learning model. The higher resolution spectra may be more easily analyzed to sort plastics into a recyclability category. The hyperspectral images may also be used to identify and analyze dark or black plastics, which are challenging for SWIR, MWIR, and other wavelengths. The machine learning model may also predict the base polymers and contaminants of plastic objects for recycling. The hyperspectral images may be used to predict recyclability and other characteristics using a trained machine learning model.

Described herein is the use of a visible near infrared (VNIR) hyperspectral imaging system as a non-invasive diagnostic tool for early detection of Alzheimer's disease (AD). Also described herein is the use of a VNIR hyperspectral imaging system in high throughput screening of potential therapeutics against AD.

Described herein is the use of a visible near infrared (VNIR) hyperspectral imaging system as a non-invasive diagnostic tool for early detection of Alzheimer's disease (AD). Also described herein is the use of a VNIR hyperspectral imaging system in high throughput screening of potential therapeutics against AD.

An imaging apparatus includes an optical element that extracts a plurality of pieces of band light from incident light incident on an optical system, a photoelectric conversion device capable of imaging the plurality of pieces of band light extracted from the incident light by the optical element, and a processor that outputs image data obtained by imaging band light, among the plurality of pieces of band light, selected according to an imaging condition determined based on a field of view by the photoelectric conversion device.

Described herein is the use of a visible near infrared (VNIR) hyperspectral imaging system as a non-invasive diagnostic tool for early detection of Alzheimer's disease (AD). Also described herein is the use of a VNIR hyperspectral imaging system in high throughput screening of potential therapeutics against AD.

Described herein is the use of a visible near infrared (VNIR) hyperspectral imaging system as a non-invasive diagnostic tool for early detection of Alzheimer's disease (AD). Also described herein is the use of a VNIR hyperspectral imaging system in high throughput screening of potential therapeutics against AD.