A image matching method includes: converting an original image and a reference image to a hue-based color space; adjusting hue values of the original image based on hue values of the reference image to obtain an adjusted image; generating a hue-difference image based on the adjusted image and the original image; obtaining a binary image based on the hue-difference image; and for each pixel having a non-zero pixel value in the binary image, determining whether an adjacent pixel has a pixel value equal to zero, and in the affirmative, correcting a hue value corresponding to the pixel in the adjusted image based on the hue values corresponding to the adjacent pixel in the original image and in the adjusted image.

Executable code causes a processor to segment a first frame to determine a background portion of the first frame, and segment a second frame to determine a background portion of the second frame. The executable code causes the processor to compare the background portion of the first frame to the background portion of the second frame to determine a difference between the first frame and the second frame. The executable code also causes the processor to alter the background portion of the second frame responsive to the difference. The executable code causes the processor to display the altered second frame on a display.

The present invention relates to a computer implemented method for aligning a three-dimensional model (6) of a patient's dentition to an image of the face of the patient recorded by a camera (3), the image including the mouth opening, comprising: estimating the positioning of the camera (3) relative to the face of the patient during recording of the image to obtain an estimated positioning, retrieving the three-dimensional model (6) of the dentition of the patient, rendering a two-dimensional image (7) of the dentition of the patient using the virtual camera (8) processing the three-dimensional model (6) of the dentition at the estimated positioning, carrying out feature detection in a dentition area in the mouth opening of the image (1) of the patient recorded by the camera (3) and in the rendered image (7) by performing edge detection and/or a color-based tooth likelihood determination in the respective images and forming a detected feature image for the or each detected feature, calculating a measure of deviation between the detected feature images of the image taken by the camera (3) and the detected feature image of the rendered image, varying the positioning of the virtual camera (8) to a new estimated positioning and repeating the preceding three steps in an optimization process to minimize the deviation measure to determine the best fitting positioning of the virtual camera (8).

An information processing system (1) includes an operation amount data acquisition unit (109), an image data acquisition unit (101), a difference expression extraction unit (108), and a mirror surface region identification unit (111). The operation amount data acquisition unit (109) acquires operation amount data of an object. The image data acquisition unit (101) acquires image data captured by an imaging device mounted on the object. The difference expression extraction unit (108) extracts a difference expression representing feature information about a difference between two images based on two pieces of image data captured before and after the operation of the object. The mirror surface region identification unit (111) identifies a mirror surface region based on a correlation between the difference expression and the operation amount data.

A non-intrusive detection method for detecting at least one pop-up window button of the pop-up window includes the following steps: retrieving a screen image on a display device; comparing the screen image with a preset screen image and generating a differential image area according the screen image and the preset screen image; determining the differential image area as the pop-up window when the differential image area is greater than an image area threshold value; selecting a plurality of contour lengths of the pop-up window matching up with a contour length threshold value by Canny edge detector; and analyzing the contour lengths according to Douglas-Peucker algorithm and an amount of endpoints to generate a contour edge corresponding to the pop-up window button.

An inspection apparatus includes an image distortion estimation unit that estimates a distortion amount between a reference image and an inspection image, an image distortion correction unit that corrects the inspection image and/or the reference image using an estimated distortion amount, and an inspection unit that performs inspection using a corrected inspection image and the reference image or the inspection image and a corrected reference image. The image distortion estimation unit estimates a distortion amount in which only distortion occurring in an entire image can be corrected by adjustment of a correction condition.

A 3D object sensing system includes an object positioning unit, an object sensing unit, and an evaluation unit. The object positioning unit has a rotatable platform and a platform position sensing unit. The object sensing unit includes two individual sensing systems which each have a sensing area. A positioning unit defines a positional relation of the individual sensing systems to one another. The two individual sensing systems sense object data of object points of the 3D object and provide the object data the evaluation unit. The evaluation unit includes respective evaluation modules for each of the at least two individual sensing systems, an overall evaluation module and a generation module.

The present disclosure relates to a method and an apparatus for detecting a subject, an electronic device, and a computer readable storage medium. The method includes the following. A current image and a previous image are obtained. A transformation matrix between the current image and the previous image is obtained in response to determining that the current image indicates the shaking state. The previous image is corrected based on the transformation matrix. The subject detection model is updated based on the corrected previous image. The subject detection is performed on the current image based on the updated subject detection model, to obtain a target subject.

Neural stimulation is provided according to a closed loop algorithm to treat sleep disordered breathing (SOB), including obstructive sleep apnea (OSA). The closed loop algorithm is executed by a system comprising a processor (which can be within the neural stimulator). The closed loop algorithm includes monitoring physiological data (e.g., EMG data) recorded by a sensor implanted adjacent to an anterior lingual muscle; identifying a trigger within the physiological data, wherein the trigger is identified as a biomarker for a condition related to sleep (e.g., inspiration); and applying a rule-based classification (which can learn) to the trigger to determine whether one or more parameters of a stimulation should be altered based on the biomarker.

An information processing device (100) is provided with a generation unit that acquires an input image serving as an intraoperative image and generates an output image based on whether the input image includes an intraoperatively generated matter or not.