Textual content is analyzed to determine a tone of the content. A first color palette including a first plurality of colors is computed. The first plurality of colors corresponds to the tone. A first color of the first plurality of colors is selected, and a second color palette including a second plurality of colors is computed. The second plurality of colors corresponds to the first plurality of colors and satisfies a predetermined color-related accessibility requirement between the first selected color and each of the second plurality of colors. A second color of the second plurality of colors is selected, and the content is modified using the first selected color and the second selected color.

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.

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.

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.

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 present disclosure provides a method for target detection, including: obtaining an image captured by a camera, and zoning the image based on a mounting angle of the camera to obtain at least one image block; determining target-detection algorithms for the at least one image block based on positions of the at least one image block in the image; and performing target detection on the at least one image block based on the target-detection algorithms.

Technologies for displaying graphical elements on a graphical user interface include a wearable computing device to generate a captured image. The wearable computing device analyzes the captured image to generate image location metric data for one or more prospective locations on the graphical user interface at which to place a graphical element. The image location metric data indicates one or more image characteristics of the corresponding prospective location. The wearable computing device determines appropriateness data for each prospective location based on the corresponding image location metric data. The appropriate data indicates a relative suitability of the corresponding location for display of the graphical element. The wearable computing device selects one of the prospective locations based on the appropriateness data and displays the graphical element on the graphical user interface at the selected prospective location.

A method is provided. The method includes activating an anti-shake mode for an electronic display. The method includes, in response to activating the anti-shake mode, identifying movement of a user relative to the electronic display at least based on processing of a plurality of captured images of at least part of the user, and transforming an image displayed on the electronic display based on the identified movement of the user. A computing device is also provided.

Various of the disclosed embodiments provide Human Computer Interfaces (HCI) that incorporate depth sensors at multiple positions and orientations. The depth sensors may be used in conjunction with a display screen to permit users to interact dynamically with the system, e.g., via gestures. Calibration methods for orienting depth values between sensors are also presented. The calibration methods may generate both rotation and translation transformations that can be used to determine the location of a depth value acquired in one sensor from the perspective of another sensor. The calibration process may itself include visual feedback to direct a user assisting with the calibration. In some embodiments, floor estimation techniques may be used alone or in conjunction with the calibration process to facilitate data processing and gesture identification.

An image processing method is provided. The method is configured to process the color-block image output by the image sensor. The face region of the color-block image is determined. A part of the color-block image beyond the face region is converted into a first simulation image using a first interpolation algorithm. A part of the color-block image within the face region is converted into a second simulation image using a second interpolation algorithm. The complexity of the second interpolation algorithm is less than that of the first interpolation algorithm. The first simulation image and the second simulation image are merged into a simulation image corresponding to the color-block image. An image processing apparatus and an electronic device are provided. With the image processing method, the image processing apparatus and the electronic device, distinguishability and resolution of the image are improved, and time required for processing the image is reduced.