A display apparatus includes a display panel, a driving controller and a data driver. The display panel displays an image based on input image data. The driving controller generates a data signal based on the input image data, determines an optical flow based on previous frame data of the input image data and present frame data of the input image data and determines a user's self-motion using the optical flow. The data driver converts the data signal to a data voltage and outputs the data voltage to the display panel.

The present disclosure generally relates to a new computer input and tracking device, and method of using, more particularly, a new type of digital pen which tracks user hand movements to generate series of point coordinates that represent a drawing or hand writing. Unlike prior digital pens such as the Apple Pencil™ or the Microsoft Surface Pen™, the new system does not require users to write on a touch-sensitive surface. Users can write on any surface such as a notebook, a table, a board, or even in mid air. Users can draw two-dimensional or three-dimensional drawings. The new method utilizes a pen marked with one or more groups of distinct tracking marks or tracking patterns and a camera to track the pen in real-time. A computer running a 3D algorithm processes the camera images, detects and distinguishes the tracking marks, and uses the tracking marks to calculate the positions of the tip of the pen in three-dimensional space. The positions of the tips of the pen are then sent to a drawing application to generate a drawing or hand writing.

A video analyzer measures and outputs a visual indication of a dynamic range of a video signal. The video analyzer includes a video input to receive the video signal and a cumulative distribution function generator generates a cumulative distribution function curve from a component of the video signal. A feature detector generates one or more feature vectors from the cumulative distribution function curve and a video dynamic range generator produces a visual output indicating a luminance of one or more portions of the video signal.

A design system generates a design for an in-ear device customized for a user. The in-ear device produces audio content for the user. The design system captures anthropometric data of the user. Using machine learning techniques, the design system determines features of an ear of the user from the anthropometric and generates a three dimensional (3D) geometry of the user's ear. A design for the in-ear device is generated based on the 3D geometry of the user's ear and includes a shell configured to fit in at least a portion of an ear canal of the user.

An attached substance detection device according to an embodiment includes a calculation unit and a determination unit. The calculation unit calculates a score of a divided region that is provided by dividing a predetermined target region, based on an edge intensity in each pixel region of a captured image that is captured by an image-capturing device. The determination unit determines whether or not the target region is an attached substance region where an attached substance is attached to a lens of the image-capturing device, based on the score that is calculated by the calculation unit.

A method and device for controlling image display in a VR system, and a VR head mounted device. The method comprises: monitoring a synchronization signal of an image frame in a VR system and acquiring an original 2D image; sampling sensor data at a preset time point before a next synchronization signal arrives to obtain latest pose information of a tracked object; converting the original 2D image into a corresponding 3D image, and calculating a motion vector corresponding to each pixel point of the 3D image according to the latest pose information and pose information corresponding to the 3D image; performing position transformation on pixel points of the original 2D image based on the motion vector, and filling at pixel points of a vacant area appearing after the position transformation to obtain a target frame; and triggering display of the target frame when the next synchronization signal arrives.

An automatic optical inspection (AOI) method for inspecting defects on a surface of an object is provided. The method includes: providing at least two different illumination systems; acquiring, by at least one detector, at least two pieces of image information of the object, each piece of image information being acquired under illumination of a corresponding one of the illumination systems; obtaining at least two pieces of surface defect information of the object by analyzing the acquired at least two pieces of image information using a computer and storing at least one of the obtained at least two pieces of surface defect information by the computer; and combining, by the computer, all of the at least two pieces of surface defect information to de-duplicate the at least two pieces of surface defect information and obtain a piece of combined surface defect information.

Embodiments of the disclosure provide methods and systems for processing a neural network associated with an input matrix having a first number of elements. The method can include: dividing the input matrix into a plurality of vectors, each vector having a second number of elements; grouping the plurality of vectors into a first group of vectors and a second group of vectors; and pruning the first group of vectors and the second group of vectors.

A method for calculating and reporting image quality properties of an image acquisition device after a subject or object has been scanned consists of a scan quality monitoring system with automated software for receiving scans and radiation dose data from scanners and automated algorithms for analyzing image quality metrics and radiation dose tradeoffs. Image quality assessment methods include algorithms for measuring fundamental imaging characteristics, level and type of image artifacts, and comparisons against large databases of historical data for the scanner and protocols. Image quality reports are further customized to report on expected clinical performance of image detection or measurement tasks.

A framework for identifying a type of organ in a volumetric medical image. The framework may include receiving a volumetric medical image, the volumetric medical image comprising at least one organ or portion thereof, and further receiving a single point of interest within the volumetric medical image. Voxels are sampled from the volumetric medical image, wherein at least one voxel is skipped between two sampled voxels. The type of organ is identified at the single point of interest by applying a trained classifier to the sampled voxels.