An image processing method is provided. The image processing method is configured to process the color-block image output by the image sensor. The brightness area is identified in the color-block image. A first part of the color-block image within the brightness area is converted into a first image using a first interpolation algorithm. The second part of the color-block image beyond the brightness area is converted into a second image using a second interpolation algorithm. The first image and the second image are merged to generate a simulation image corresponding to the color-block image. Moreover, an image processing apparatus and an electronic device are provided.

The invention notably relates to a computer-implemented method for reconstructing a 3D modeled object that represents a real object, from a 3D mesh and measured data representative of the real object, the method comprising providing a set of deformation modes; determining a composition of the deformation modes which optimizes a program that rewards fit between the 3D mesh as deformed by the composition and the measured data, and that further rewards sparsity of the deformation modes involved in the determined composition; and applying the composition to the 3D mesh. The method improves reconstructing a 3D modeled object that represents a real object.

A method includes acquiring, from a camera, an image frame including a representation of an object, and retrieving from a memory, data containing a template of a first pose of the object. A processor compares the first template to the image frame. A plurality of candidate locations in the image frame having a correlation with the template exceeding a predetermined threshold is determined. Edge registration on at least one candidate location of the plurality of candidate locations is performed to derive a refined pose of the object. Based at least in part on the performed edge registration, an initial pose of the object is determined, and a display image is output for display on a display device. The position at which the display image is displayed and/or the content of the display image is based at least in part on the determined initial pose of the object.

A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.

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.

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.

According to an embodiment, an arithmetic method includes accepting an input of image data, detecting a representative position of a characteristic portion from the image data the input of which has been accepted, acquiring a user instructed position in the image data, calculating a position difference indicating a difference between the detected representative position and the acquired user instructed position, and selecting the representative position of the characteristic portion as a target portion if the calculated position difference is smaller than a predetermined first threshold and selecting the user instructed position as a target portion if the position difference is not smaller than the first threshold.

Systems and methods for geometric calibration and image reconstruction in computed tomography (CT) scanning using iterative reconstruction algorithms are provided. An iterative reconstruction algorithm can be used to reconstruct an improved image, and then the improved image can be used to adjust inaccurate parameters by using a Locally Linear Embedding (LLE) method. Adjusted parameters can then be used to reconstruct new images, which can then be used to further adjust the parameters. The steps of this iterative process can be repeated until a quality threshold is met.

A region extraction unit extracts first and second shape-invariant regions corresponding to each other from first and second three-dimensional images acquired by an MRI apparatus. A first registration unit acquires a first deformation vector by performing rigid registration between the first and second shape-invariant regions. A second registration unit acquires a second deformation vector by performing non-rigid registration between the first and second three-dimensional images in each shape-invariant region. A magnetic field distortion vector calculation unit calculates a magnetic field distortion vector, which represents relative magnetic field distortion between the first and second three-dimensional images based on the first and second deformation vectors.

The invention notably relates to a computer-implemented method for reconstructing a 3D modeled object that represents a real object, from a 3D mesh and measured data representative of the real object, the method comprising providing a set of deformation modes; determining a composition of the deformation modes which optimizes a program that rewards fit between the 3D mesh as deformed by the composition and the measured data, and that further rewards sparsity of the deformation modes involved in the determined composition; and applying the composition to the 3D mesh. The method improves reconstructing a 3D modeled object that represents a real object.