This invention presents a system and method to acquire object measurements with and optical device and axial scanning mechanism, and to subsequently implement a digital processing algorithm to establish a match between similar object measurements acquired in the past. The digital processing algorithm consists of a probe-and-match strategy. It is generally challenging to determine whether or not an object, such as a work of art (e.g., a painting, drawing, sculpture, or collectable item), is the exact same object as something viewed previously with an extremely high degree of confidence. There is the potential for one object to look very similar to another object, but not be the same (i.e., to be a forgery). There is also the potential for one object to be the same object as viewed previously, but have changed significantly over time (e.g., aged or damaged). Being able to distinguish between these two cases is a difficult yet important task. This invention aims to address the challenge of identifying object uniqueness by considering it as a matching problem between optical measurements, wherein multiple acquired measurements from many objects must be sifted through to establish how similar they are, if at all, to any new measurements.

The present invention relates generally to a system and method for measuring the structural characteristics of an object. The object is subjected to an energy application processes and provides an objective, quantitative measurement of structural characteristics of an object. The system may include a device, for example, a percussion instrument, capable of being reproducibly placed against the object undergoing such measurement for reproducible positioning. The invention provides for a system and methods for analyzing measured characteristics utilizing machine learning to create a system for predicting pathologies from measurements.

A needle collection and counting tray for use in a clinical setting, such as an operating room, into which a user deposits used needles. The tray is then placed into a counting and identification machine the utilizes object recognition technology to identify and count the needles on the tray.

A needle collection and counting tray for use in a clinical setting, such as an operating room, into which a user deposits used needles. The tray is then placed into a counting and identification machine the utilizes object recognition technology to identify and count the needles on the tray.

An artificial neural network-based method for detecting a surface type of an object includes: receiving a plurality of object images, wherein a plurality of spectra of the plurality of object images are different from one another and each of the object images has one of the spectra; transforming each object image into a matrix, wherein the matrix has a channel value that represents the spectrum of the corresponding object image; and executing a deep learning program by using the matrices to build a predictive model for identifying a target surface type of the object. Accordingly, the speed of identifying the target surface type of the object is increased, further improving the product yield of the object.

A three-dimensional shape measuring method includes: projecting a first grid pattern based on a first light and a second grid pattern based on a second light onto a target object in such a way that the first grid pattern and the second grid pattern intersect each other, the first light and the second light being lights of two colors included in three primary colors of light; picking up, by a three-color camera, an image of the first grid pattern and the second grid pattern projected on the target object, and acquiring a first picked-up image based on the first light and a second picked-up image based on the second light; and performing a phase analysis of a grid image with respect to at least one of the first picked-up image and the second picked-up image and calculating height information of the target object.

The present disclosure provides a display apparatus including: a display module with a plurality of color filters and a black matrix with a plurality of first openings and a plurality of second openings, and the plurality of color filters are arranged in the plurality of first openings in a one-to-one correspondence; a fingerprint recognition circuit, on a side of the display module away from a display surface of the display module and including a plurality of fingerprint recognition elements; and a collimating structure, on an incident side of the fingerprint recognition circuit and including at least two light shading layers, wherein each of the light shading layers has a plurality of light transmitting holes, and one of the light shading layers is multiplexed as the black matrix.

Some embodiments are directed to a biometric authentication system including headwear having a plurality of biosensors each configured to sample muscle activity so as to obtain a respective time-varying signal, a data store for storing a data set representing characteristic muscle activity for one or more users, and a processor configured to process the time-varying signals from the biosensors in dependence on the stored data set so as to determine a correspondence between a time-varying signal and characteristic muscle activity of one of the one or more users, and in dependence on the determined correspondence, authenticate the time-varying signals as being associated with that user.

The invention relates to a free-beam interferometric method for illuminating a sequence of sectional areas in the interior of the light-scattering object. The method makes it possible for the user to select a larger image field and/or a higher image resolution than previously possible with the occurrence of self-interference of the specimen light from a scattering specimen.

A device with image processing includes: an image acquirer configured to acquire a plurality of images each having a different brightness; and one or more processors configured to extract an illumination map for an input image of the images and an illuminant color of the input image from the input image and temporal correlation information of the plurality of images, based on an illumination extraction model.