Provided is a lens assembly, which comprises: a first optical lens module, comprising a first carrier and at least one first optical lens received in the first carrier; and a second optical lens module, comprising a second carrier, at least one second optical lens received in the second carrier, and a bearing portion connected to the second carrier. A lower end portion of the first carrier extends to the bearing portion, so as to constrain the relative positions of the first optical lens module and the second optical lens module.

The equipment management device includes an information collection unit, a specific state detection unit, a cause estimation unit, and a countermeasure process execution unit. The information collection unit acquires a video of a conference room or a sound of the conference room. The specific state detection unit detects that equipment provided in the conference room is in a specific state on the basis of a result of analyzing the video or the sound. The cause estimation unit acquires equipment information about the equipment when the equipment has been detected to be in the specific state and estimates a cause of the specific state on the basis of the equipment information. The countermeasure process execution unit selects and executes a countermeasure process to be executed on the basis of the estimated cause.

Methods and systems of eliminating corrupting influences caused by the propagation medium and the data capture devices themselves from useful image features or characteristics such as the degree of symmetry are disclosed. The method includes the steps of obtaining image-plane data using a plurality of data capture devices, wherein the image-plane data is a combined visibility from each of the data capture devices, measuring the closure phase geometrically in the image-plane directly from the image-plane, removing the corruptions from the image features based on the measured closure phase to remove the non-ideal nature of the measurement process, and outputting the uncorrupted morphological features of the target object in the image. The method relies on the Shape-Orientation-Size conservation principle for images produced from three visibilities made from a closed triad of data capture devices. The method includes the idea that true image of the object can be reconstructed from the three interferometer elements, independent of element-based calibration.

A test card and a test display adapter are disclosed. The test card includes an assembling plate and an electrical heat source. The electrical heat source is provided on the assembling plate. The electrical heat source includes a first heat transfer plate, a second heat transfer plate and at least one heat source element. The first heat transfer plate is stacked on the assembling plate, and the second heat transfer plate is laminated over the first heat transfer plate, with the heat source element being sandwiched between the first and second heat transfer plates. The test card and the test display adapter have a wide variety of advantages including very low cost, easy material availability, a short wait time, easy manufacturability, a long service life, easy modifiability and good interoperability.

Technologies for source degradation and auto-tuning of cameras are disclosed. In one embodiment, a system includes a compute node connected to one or more cameras. The cameras can monitor an environment, such as a manufacturing process in a factory. The compute node may use image processing, such as a machine-learning-based algorithm, to monitor processes. A system integrator may adjust parameters of the camera and/or the algorithm, such as focus, gain, contrast, white balance, etc. The compute node can monitor the system integrator and learn how to adjust parameters in order to improve key performance indicators (KPIs) of the monitored process. In a production environment, the compute node may then automatically adjust parameters of the camera and/or analysis algorithm based on the actions of the system integrator in order to improve performance of the analysis algorithm.

Disclosed are a method and system for testing a wearable device. The method includes: performing an angle acquisition process for at least two times, and calculating an optical imaging parameter value of a target virtual image on the basis of angle variation values acquired in the at least two angle acquisition processes. With the method and system according to the present disclosure, the finally calculated optical imaging parameter value is more objective and more accurate than that acquired by means of the human eyes.

In various embodiments, a prediction application computes a quality score for re-constructed visual content that is derived from visual content. The prediction application generates a frame difference matrix based on two frames included in the re-constructed video content. The prediction application then generates a first entropy matrix based on the frame difference matrix and a first scale. Subsequently, the prediction application computes a first value for a first temporal feature based on the first entropy matrix and a second entropy matrix associated with both the visual content and the first scale. The prediction application computes a quality score for the re-constructed video content based on the first value, a second value for a second temporal feature associated with a second scale, and a machine learning model that is trained using subjective quality scores. The quality score indicates a level of visual quality associated with streamed video content.

The disclosure relates to assessing operation of two or more cameras. These cameras may be a group of cameras of a perception system of a vehicle having an autonomous driving mode. A first image captured by a first camera and a second image captured by a second camera may be received. A first feature vector for the first image and a second feature vector for the second image may be generated. A similarity score may be determined using the first feature vector and the second feature vector. This similarity score may be used to assess the operation of the two cameras and an appropriate action may be taken.

In a solid-state imaging element equipped with per-column ADCs, noise is reduced. A test signal source generates a test signal of a predetermined level. An analog-to-digital converter increases/decreases an analog signal according to an analog gain selected from among a plurality of analog gains, and converts the increased/decreased analog signal to a digital signal. An input switching section inputs, as the analog signal, either a test signal or a pixel signal to the analog-to-digital converter. A correction value calculation section obtains, on the basis of the test signal and the digital signal, a correction value for correcting an error in the selected analog gain, and outputs the correction value. A correction section corrects the digital signal according to the outputted correction value.

Position detection methods and systems are disclosed herein. The position detection method of detecting a position in an operation surface pointed by a pointing element includes obtaining a first taken image with the first infrared camera, obtaining a second taken image with the second infrared camera, removing a noise component from the first and second images converting the first and second taken into converted images without the noise component, forming a difference image between the first converted taken image and the second converted taken image, extracting a candidate area in which a disparity amount between the first converted taken image and the second converted taken image is within a predetermined range, detecting a tip position of the pointing element from the candidate area, and determining a pointing position of the pointing element and whether or not the pointing element had contact with the operation surface based on the detecting.