A system includes: a sequential image string input unit configured to input a sequential image string having sequentiality; a reference image selection unit configured to select one or more images from the sequential image string as reference images; a variation calculation unit configured to select an adjacent reference image adjacent to the reference image from the sequential image string and calculate a variation between the reference image and the adjacent reference image; an image information regression unit configured to calculate class confidence by regression processing with the reference image as an input; a difference image information regression unit configured to calculate class confidence by regression processing with the variation as an input; a confidence integration unit configured to integrate class confidence calculated by the image information regression unit and class confidence calculated by the difference image information regression unit; and an output unit configured to output the integrated class confidence.

A method and apparatus for determining gaze direction information, includes a light source for forming illuminating light to an eye region of a user, and optical element(s) configured to guide the illuminating light from the light source to the eye region. The illuminating light is dynamically adjustable to generate a dynamic light beam on the eye region, and a sensor is configured to capture reflected light on the eye region and generate reflection eye data. The apparatus can maintain user profile information, adjust spectral power distribution of the light source based on the user profile information, receive the reflection eye data, and generate the gaze direction information based on the reflection eye data.

A system and method of analysing drilling cuttings using image data output from a hyperspectral imaging device and at least one optical camera, includes generating a hyperspectral imaging data set including a plurality of lines of hyperspectral data derived from line images taken by the hyperspectral imaging device positioned along a drilling fluid cuttings path, obtaining tracking information in respect of particles of interest from the output of the at least one optical camera, correcting the position of pixels associated with particles of interest in the plurality of lines of hyperspectral imaging data based on the obtained tracking information to generate corrected hyperspectral imaging data, and analysing the corrected hyperspectral imaging data to characterise the cuttings.

A system and method of analysing drilling cuttings using image data output from a hyperspectral imaging device and at least one optical camera, includes generating a hyperspectral imaging data set including a plurality of lines of hyperspectral data derived from line images taken by the hyperspectral imaging device positioned along a drilling fluid cuttings path, obtaining tracking information in respect of particles of interest from the output of the at least one optical camera, correcting the position of pixels associated with particles of interest in the plurality of lines of hyperspectral imaging data based on the obtained tracking information to generate corrected hyperspectral imaging data, and analysing the corrected hyperspectral imaging data to characterise the cuttings.

A method of training Artificial Intelligence (AI) algorithms for remote sensing image analyses by hyperspectral earth land surface property data analyses comprises subjecting a selected earth land property to unsupervised measurements of said property by using a hyperspectral image obtained from an area X of the earth land surface at a time Y by a remote sensing hyperspectral radiance measurement instrument, converting the obtained data into at least one set of data of said selected property using the appropriate unsupervised retrieval algorithm, using the at least one property data set obtained as training data for training the AI algorithm for multispectral images or microwave images of the area X; and applying the trained AI algorithm for the property to multispectral or microwave remote sensing data sets of a geographical area at least equal to the area X and for a time span at least equal to the time span Y.

A method of training Artificial Intelligence (AI) algorithms for remote sensing image analyses by hyperspectral earth land surface property data analyses comprises subjecting a selected earth land property to unsupervised measurements of said property by using a hyperspectral image obtained from an area X of the earth land surface at a time Y by a remote sensing hyperspectral radiance measurement instrument, converting the obtained data into at least one set of data of said selected property using the appropriate unsupervised retrieval algorithm, using the at least one property data set obtained as training data for training the AI algorithm for multispectral images or microwave images of the area X; and applying the trained AI algorithm for the property to multispectral or microwave remote sensing data sets of a geographical area at least equal to the area X and for a time span at least equal to the time span Y.

A method performed by a multispectral or hyperspectral meibomian gland imaging device is disclosed, which comprises directing light having visible and infrared spectra from a broadband illuminator toward an everted eyelid; forming images of the everted eyelid using an imaging system; recording the images using a detection system, which separates the images into spectral channels, which include at least one visible spectral channel and at least one infrared spectral channel; displaying the recorded images to adjust the position of the subject to optimize image quality; digitally processing the recorded images to obtain spatial and spectral information; evaluating the health of at least one meibomian gland of the eyelid by analyzing the information. Also disclosed is a multispectral or hyperspectral meibomian gland imaging device, comprising a broadband illuminator directing light that covers visible and infrared spectra; an imaging system, and a detection system.

Various embodiments of the present disclosure provide systems and methods for classifying and evaluating an electronic object based at least in part on terahertz (THz) data. THz data may comprise time-domain THz data collected via THz time domain spectroscopy of the electronic object. THz data may further comprise frequency-domain THz data, which may be generated based at least in part on the time-domain THz data. A unique THz fingerprint may be generated for the electronic object based at least in part on the THz data. This unique THz fingerprint may be compared to an earlier generated THz fingerprint of the same electronic object to evaluate reliability and consistency. The unique THz fingerprint may also be compared to THz fingerprints or THz data of other electronic objects of the same object type, class, design, and/or the like, to validate the electronic object (e.g., determine if the electronic object is counterfeit).

Techniques are described for automated operations to determine tree inventory information for an area of land using visual data of overhead image(s), such as by using a trained prediction model specific to an ecological region to which the land area belongs as part of probabilistically determining multiple types of information about trees in that land area, and for subsequently using the determined tree inventory information in one or more manners (e.g., to improve management of trees in that land area). The images may, for example, include spectral satellite images that include at least visible light data for an area of land, and the determined tree inventory information may include information about the trees present on the land area, such as, for example, predictions of particular tree species, quantities of each of the tree species, sizes of the trees, etc.

Systems and methods for processing hyperspectral image via a computer network. At a server: obtaining, from a first client in a plurality of clients, a request for a unique patient identifier; in response to the request, generating the unique patient identifier independent of patient identity; storing the unique patient identifier and information identifying the first client in a server lookup table; and transmitting the unique patient identifier to the first client; receiving, from a hyperspectral imaging device, a hyperspectral image data set, which includes including a plurality of sub-images of an object and the unique patient identifier, and the unique patient identifier is associated with the plurality of sub-images; forming a hyperspectral image using the hyperspectral image data set; identifying the first client in the plurality of clients using the unique patient identifier and the server lookup table; and transmitting, to the first client, the hyperspectral image.