In an embodiment, a method includes determining a spatial illumination distribution using a first image caused by at least first structured light and a second image caused by at least second structured light, wherein a portion of the first image is caused by a portion of the at least first structured light traveling a first distance, a portion of the second image is caused by a portion of the at least second structured light traveling a second distance, the portion of the first image and the portion of the second image cause a same portion of the spatial illumination distribution, and the first distance is different from the second distance; building a first 3D face model; rendering the first 3D face model using the spatial illumination distribution, to generate a first rendered 3D face model; and displaying the first rendered 3D face model to a first camera.

An augmented reality system having a light source and a camera. The light source projects a pattern of light onto a scene, the pattern being periodic. The camera captures an image of the scene including the projected pattern. A projector pixel of the projected pattern corresponding to an image pixel of the captured image is determined. A disparity of each correspondence is determined, the disparity being an amount that corresponding pixels are displaced between the projected pattern and the captured image. A three-dimensional computer model of the scene is generated based on the disparity. A virtual object in the scene is rendered based on the three-dimensional computer model.

A computer-implemented method for surface modeling includes: receiving one or more polarization raw frames of a surface of a physical object, the polarization raw frames being captured with a polarizing filter at different linear polarization angles; extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and detecting a surface characteristic of the surface of the physical object based on the one or more first tensors in the one or more polarization representation spaces.

A three-dimensional measurement device includes one or a plurality of light source units configured to irradiate the object to be measured SA with measurement light having a predetermined pattern, one or a plurality of image capture units configured to capture an image of the object to be measured which is irradiated with the measurement light, and a measurement unit configured to measure a three-dimensional shape of the object to be measured on the basis of results of image capture performed by the image capture units. The light source units are constituted by an S-iPMSEL of M-point oscillation.

A live facial recognition method includes projecting a given pattern to a subject under recognition; capturing a reflected pattern of the subject under recognition; and detecting whether the subject under recognition is a flat surface according to the reflected pattern. The subject under recognition is determined to be a living subject when the subject under recognition is not a flat surface.

The invention relates to a biometric sensing system, comprising: a light emitter, a polarization sensor, and a signal processing module, wherein the polarization sensor includes a first polarizer and a second polarizer. First, the light emitter emits a plurality of emitted light from the object under sensing, and reflected by the object. Then, a first reflected light in a first polarization direction and a second reflected light in a second polarization direction in the reflected light are sensed by the polarization sensor. Finally, the signal processing module calculates a first reflectance and a second reflectance according to the first reflected light and the second reflected light, and generate a reflectance ratio based on the first reflectance and the second reflectance. As such, the user determines whether the surface of the object under sensing is 3D by the reflectance ratio, so as to achieve improving safety and saving costs.

The invention relates to a fingerprint recognition system. The fingerprint recognition system includes a light emitter, an optical receiver, and a comparison module. First, the light emitter emits at least one patterned emitted light to an object to be measured, and the patterned reflected light reflected by the object is received by the light receiver. Then, the patterned emitted light and the patterned reflected light are compared with each other and the comparison information is generated by the comparison module. Finally, when the comparison information is in the three-dimensional comparison interval, the surface of the object to be measured is determined to be three-dimensional; otherwise, the surface of the object to be measured is determined to be flat. Therefore, the fingerprint recognition system of the present invention can achieve the purposes of real-time differentiation and wide applicability.

Provided is a data transmission system including analog image frame buffer, line analog-to-digital converter, line buffer memory, and an interface. First, the analog image frame buffer stores the image data lines generated from the image sensor as analog signals, and then the line analog digital converter which is electrically connected to the analog image frame buffer converts the image data lines from analog signals to digital signals. Then, the image data lines converted into digital signals are stored in one of the line buffer memories. Then, according to the user's needs, the image data line of the digital signal is temporarily stored in another line buffer memory. Finally, according to the instructions of the master device, the interface outputs the image data lines of digital signals according to the conversion order of the line analog to digital converter.

Systems and methods are disclosed for trusted imaging. In some examples, a trusted imaging device can emit a patterned light onto a real-world scene while an image sensor (e.g. photo or video) generates data representative of the real-world scene. The data can be processed to attempt to recover a pattern of the patterned light from the data. Whether, or to what extent, the pattern can be recovered can be determinative of a trustworthiness of the data from the image sensor. In further examples, the image data can be encrypted, as well as the imaging device output. In still further examples, a depth map of the image data can also be used to determine the trustworthiness of the image data.

The method for item recognition can include: optionally calibrating a sampling system, determining visual data using the sampling system, determining a point cloud, determining region masks based on the point cloud, generating a surface reconstruction for each item, generating image segments for each item based on the surface reconstruction, and determining a class identifier for each item using the respective image segments.