A method for generating a panoramic image is disclosed. The method comprises simultaneously capturing images from multiple camera sensors aligned horizontally along an arc and having an overlapping field of view; performing a cylindrical projection to project the captured images from the multiple camera sensors to a cylindrical images; and aligning overlapping regions of the cylindrical images corresponding to the overlapping field of view based on an absolute difference of luminance, wherein the cylindrical projection is performed by adjusting a radius for the cylindrical projection, wherein the radius is adjusted based on a scale factor and wherein the scale factor is calculated based on a rigid transform and wherein the scale factor is iteratively calculated for two sensors from the multiple camera sensors.

Devices and optical sensor modules are provided for provide on-screen optical sensing of fingerprints by using an under-screen optical sensor module that captures and detects light from a fiber on top of the screen. Various implementations of the under-LCD optical sensor modules are provided, including different optical imaging module designs for under-LCD optical sensing, invisible under-LCD optical sensor modules based on concealing optical transmissive features or regions under the LCD opaque borders and optical sensing of topographical features associated with inner tissues of a finger.

A deep neural network (DNN) system learns a map representation for estimating a camera position and orientation (pose). The DNN is trained to learn a map representation corresponding to the environment, defining positions and attributes of structures, trees, walls, vehicles, etc. The DNN system learns a map representation that is versatile and performs well for many different environments (indoor, outdoor, natural, synthetic, etc.). The DNN system receives images of an environment captured by a camera (observations) and outputs an estimated camera pose within the environment. The estimated camera pose is used to perform camera localization, i.e., recover the three-dimensional (3D) position and orientation of a moving camera, which is a fundamental task in computer vision with a wide variety of applications in robot navigation, car localization for autonomous driving, device localization for mobile navigation, and augmented/virtual reality.

Provided is a virtual image display apparatus including a laser light source, a scanner configured to scan light from the laser light source and render an intermediate image, a diffusion element arranged at a position of the intermediate image formed by the scanner, and an exit pupil forming unit configured to cause light that has passed through the diffusion element to be incident on a position of an exit pupil. The diffusion element separates the light from the scanner into first emission light used as a reference, and second emission light having a separation angle greater than a capturing angle corresponding to an inclination of light toward a predetermined pupil radius relative to the first emission light at an emission position of the light from the diffusion element, and then emits the first emission light and the second emission light.

A system and method. The system may include a monitor implemented as a virtual window, a camera, and a pivot motor. The pivot motor may be configured to change an orientation of a field of view of the camera relative to a vehicle based on a position of a passenger.

Disclosed are various embodiments for optimizing cubic utilization when loading items into a loading space. A current loading configuration of the loading space can be determined according to image data obtained by 3D sensors. Item data (e.g., volume, mass, type, dimensions, etc.) can be determined for incoming items to be loaded into the loading space. The current loading configuration and the item data can be used to determine an item sequence and optimal placement location for the next item to be loaded such that a cubic efficiency of the loading space is maximized and amount of air gaps between items is minimized. The current loading configuration can further be used to determine if a sensing system or an item loading system needs to be repositioned. Whether the next item was placed in the optimal placement can also be verified based on subsequent image data obtained by the 3D sensors.

An image or a video may include a spherical capture of a scene. A punchout of the image or the video may provide a panoramic view of the scene.

A computer vision system includes a camera that captures a plurality of image frames in a target field. A user interface is coupled to the camera. The user interface is configured to perform accelerated parallel computations in real-time on the plurality of image frames acquired by the camera. The system detects and tracks animal wellness and habitat/intervention design.

An optical flow accelerator (OFA) which provides hardware-based acceleration of optical flow and stereo disparity determination is described. A system is described which includes an OFA configured to determine a first optical flow using a first disparity search technique, and to determine a second optical flow using a second disparity search technique that is different from the first disparity search technique. The system also includes a processor configured to combine the first optical flow and the second optical flow to generate a third optical flow. In some implementations, the first and second disparity search techniques are based upon Semi-Global Matching (SGM). In some implementations, the OFA is further configurable to determine stereo disparity.

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.