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.

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.

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.

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.

In a freespace detection apparatus and method, a camera provides at least first and second image data captured at different points in time. A motion vector of the camera is detected and assigned to the image data. The image data is transformed with respect to a predetermined, configurable image plane, and motion compensation of the transformed image data is performed. Difference image data between the motion compensated transformed image data and the transformed second image data is computed, and an object is identified in the computed difference image data.

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.

Tags comprising marks arranged in a predetermined pattern are applied to an item. The marks are made with an ink that fluoresces under infrared (IR) light. A camera with a filter acquires an image of the light emitted by the fluorescence of the ink. This image is processed to determine a portion of the image in which the tag is located. Once located, that portion is processed to rectify and align the tag. This rectified image is then processed to read out tag data that is encoded by the arrangement of marks. The tag data may then be used to identify the item, designate shipping information, and so forth.

An iterative reconstruction approach is provided that allows the use of differing weights in pixels or larger sub-regions in the reconstructed image. By way of example, the relative significance of each projection measurement may be determined based on both the measurement position and the location of the reconstructed pixel. Computationally, the significance of each projection based on these two factors is represented by a weight factor employed in the algorithmic computation.

An automatic separating and identifying system of insects on leaves that includes an insects separation system, an insects dispersing system, and an insects identification system. The insects separation system includes a circulating container and water circulation mechanism. The dispersing system includes an examination mesh that is designed to receive the water with the insects. The identification system includes a camera, lighting means, a computer with a database and algorithm, and is designed to identify types and amounts of the insects on the examination mesh.

Disclosed is an image recognition based workstation for evaluation on quality check of colonoscopy, relating to the technical field of intelligent healthcare. The workstation comprises an algorithm module, a timing module, a data transmission module, a display device, a colonoscopy device, and a computer host. The colonoscopy device is connected with the data transmission module, and the data transmission module is connected with the computer host through the algorithm module and the timing module; and the display device is used to display the results of the computer host. The described workstation can evaluate different techniques of doctors during each colonoscopy check by means of different image recognition algorithms. During the checking process, the workstation determines whether the operation of the doctor is appropriate and gives the corresponding reference suggestions, which is responsible for patients and allows the doctor to continuously improve his ability during the checking process, thereby greatly reducing the pressure on doctors, and allowing doctors to focus more on other more creative tasks, and besides bringing huge economic and social benefits.