An inspection apparatus includes a specimen stage, one or more imaging devices and a set of lights, all controllable by a control system. By translating or rotating the one or more imaging devices or specimen stage, the inspection apparatus can capture a first image of the specimen that includes a first imaging artifact to a first side of a reference point and then capture a second image of the specimen that includes a second imaging artifact to a second side of the reference point. The first and second imaging artifacts can be cropped from the first image and the second image respectively, and the first image and the second image can be digitally stitched together to generate a composite image of the specimen that lacks the first and second imaging artifacts.

A depth camera assembly (DCA) determines depth information within a local area by capturing images of the local area including a local region using a plurality of imaging sensors. The local region is represented by a first set of pixels in each captured image. For each image, the DCA identifies the first set of pixels corresponding to the surface in the local region and determines a depth measurement from the DCA to the local region by comparing the first set of pixels from images captured by different imaging sensors. To determine depth measurements for second sets of pixels neighboring the first set of pixels, the DCA selectively propagates depth information from the first set of pixels to second sets of pixels satisfying one or more criteria (e.g., satisfying a threshold saturation measurement or a threshold contrast measurement).

A method of image processing includes receiving a set of images of an object, the set of images including images corresponding to binary spherical gradient illumination along first, second and third mutually orthogonal axes and images corresponding to complementary binary spherical gradient illumination along the first, second and third axes. The method also includes determining a specular reflectance map of the object and a diffuse reflectance map of the object based on the set of images, and/or determining a diffuse photometric normal map and a specular photometric normal map of the object based on the set of images.

A machine vision system that uses an imager to capture an optical image of a target object that may contain a liquid. The target object is illuminated by an illumination source positioned oppositely from the imager and a predetermined pattern is positioned between the illumination source and the target object so that the imager will capture optical images of the background pattern through any liquid positioned in the target object. A processor is programmed to analyze captured images to detect any distortions of the pattern that are attributable to the presence of a liquid in the target object.

A device includes a first polarized image sensor configured to capture first image data relating to an object from a first perspective. The device also includes a second polarized image sensor configured to capture second image data relating to the object from a second perspective different from the first perspective. The device further includes a processor configured to obtain at least one of polarization information or depth information of the object based on at least one of the first image data or the second image data, and to extract a feature of the object based on the at least one of the polarization information or the depth information.

An inspection apparatus includes a specimen stage, one or more imaging devices and a set of lights, all controllable by a control system. By translating or rotating the one or more imaging devices or specimen stage, the inspection apparatus can capture a first image of the specimen that includes a first imaging artifact to a first side of a reference point and then capture a second image of the specimen that includes a second imaging artifact to a second side of the reference point. The first and second imaging artifacts can be cropped from the first image and the second image respectively, and the first image and the second image can be digitally stitched together to generate a composite image of the specimen that lacks the first and second imaging artifacts.

A near-eye-display (NED) includes a tracking system and a waveguide assembly. The waveguide assembly includes an infrared (IR) light source and an output waveguide. The output waveguide includes at least a decoupling element that outcouples the IR light emitted by the IR light source to form the structured light pattern. The structured light pattern is projected toward one or more regions of a user's face, for example, the user's eyes. The structured light pattern is reflected off the one or more regions of the user's face and captured by the tracking system. The tracking system can determine tracking information such as eye tracking information as well as face tracking information based on the captured reflected structured light pattern.

A display panel includes a substrate; an array layer on the substrate; a display layer located on a side of the array layer away from the substrate, where the display layer includes a plurality of light-emitting devices; an optical layer located on a side of the display layer away from the array layer, wherein the optical layer includes a first optical structure, and at least a portion of the first optical structure is arranged corresponding to intervals between the plurality of light-emitting devices; and a light-shielding member located on a side of the optical layer facing the substrate, where the light-shielding member includes a light pass area, and the light pass area and the first optical structure overlap each other.

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.

Disclosed is an artificial intelligence (AI) apparatus including a two-dimensional (2D) image sensor configured to acquire a 2D image of a head of a person, a three-dimensional (3D) image sensor configured to acquire 3D head pose information of the head, and a processor configured to match the 2D image with the 3D head pose information, to extract 3D head pose information for determining a rotation direction of the head from the 3D head pose information, to extract a 2D image matched with the extracted 3D head pose information, to acquire 3D relative coordinates as a reference for correcting the 3D head pose information based on 2D coordinates of a predetermined landmark point of the extracted 2D image, and to acquire the corrected 3D head pose information of the predetermined landmark point of each 2D image by correcting the 3D head pose information based on the 3D relative coordinates.