Systems and methods disclosed herein use a first machine learning architecture and a second machine learning architecture where the first machine learning architecture executes on a first processor and receives a first image representing a mouth of a user, determines user feedback for outputting to the user based on a first machine learning model, and outputs the user feedback for capturing a second image representing the mouth of the user. The second machine learning architecture executes on a second processor and receives the first image and the second image, and generates a 3D model of at least a portion of a dental arch of the user based on the first image and the second image where the 3D model is generated based on a second machine learning model of the second machine learning architecture.

Welding headwear comprises one or more image sensors, processing circuitry, and a display. The image sensor(s) are operable to capture an image of an unpowered weld torch as the torch passes along a joint of a workpiece to be welded. The processing circuitry is operable to: determine, through processing of pixel data of the image, one or more welding parameters as the torch passes along the joint to be welded; generate, based on the one or more welding parameters, a simulated weld bead; and superimpose on the image, in real time as the torch passes along the joint, the simulated weld bead on the joint. The display is operable to present, in real time as the torch passes along the joint, the image with the simulated bead overlaid on it.

An image frame loss detection method is performed by a computer device, including: acquiring first coded data respectively corresponding to a plurality of first image frames and a color signal corresponding to at least one second image frame; obtaining second coded data corresponding to at least one second image frame generated by a terminal device through image rendering of a color signal based on the coded data respectively corresponding to the plurality of first image frames; and comparing the first coded data respectively corresponding to the plurality of first image frames with the second coded data corresponding to the at least one second image frame to determine whether a frame loss occurs. The first coded data and the second coded data each include color-coded data respectively corresponding to M image blocks of a correspond image frame, and each of the M image blocks has a color in the image frame.

A method for measuring distorted illumination patterns and correcting image artifacts in structured illumination microscopy. The method includes the steps of generating an illumination pattern by interfering multiple beams, modulating a scanning speed or an intensity of a scanning laser, or projecting a mask onto an object; taking multiple exposures of the object with the illumination pattern shifting in phase; and applying Fourier transform to the multiple exposures to produce multiple raw images. Thereafter, the multiple raw images are used to form and then solve a linear equation set to obtain multiple portions of a Fourier space image of the object. A circular 2-D low pass filter and a Fourier Transform are then applied to the portions. A pattern distortion phase map is calculated and then corrected by making a coefficient matrix of the linear equation set varying in phase, which is solved in the spatial domain.

In a crack detection device (10), an image acquisition unit (21) acquires image data acquired by taking an image of a road surface from an oblique direction with respect to the road surface, An image classification unit (22) classifies image data acquired into an acceptable range with a resolution higher than a standard value, and an unacceptable range with a resolution equal to or less than the standard value. A data output unit (23) outputs acceptable data being image data of a part classified into the acceptable range as data to detect a crack on the road surface. An image display unit (24) displays data output.

A deep learning-based video editing method can allow for automated editing of a video, reducing or eliminating user input, saving time and labor investments, and thereby improving video editing efficiency. Attribute recognition is performed on an object in a target video using a deep learning model. A target object is selected that satisfies an editing requirement of the target video. A plurality of groups of pictures associated with the target object from the target video are obtained using editing. An edited video corresponding to the target video is generated using the plurality of groups of pictures.

The present disclosure provides a method of processing an image, a device, and a medium. The method of processing the image includes: performing an image processing on an original image to obtain a component image for brightness of the original image; determining at least one of the original image and the component image as an image to be processed; classifying a pixel in the image to be processed, so as to obtain a classification result; processing the image to be processed according to the classification result, so as to obtain a target image; and determining an image quality of the original image according to the target image.

The present disclosure provides a method of processing an image, a device, and a medium. The method of processing the image includes: performing a noise reduction on an original image to obtain a smooth image; performing a feature extraction on the original image to obtain feature data for at least one direction; and determining an image quality of the original image according to the original image, the smooth image, and the feature data for the at least one direction.

A system includes a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising displaying a prompt to a user of a mobile device on a display of a mobile device to capture an image representing at least a portion of a mouth of the user using a rear-facing camera of the mobile device, where the rear-facing camera is on an opposite side of the mobile device including the display. The operations further comprise controlling the rear-facing camera to enable the rear-facing camera to capture the image, receiving the image, and outputting, user feedback based on the image, where the user feedback is outputted on the display that is on the opposite side of the mobile device than the rear-facing camera.

A first frame of time of flight (TOF) data including projection off data and infrared data is obtained, and after determining that a data block satisfying that a number of data points with values greater than a first threshold is greater than a second threshold is present in the infrared data, TOF data for generating a first frame of a TOF image is obtained based on a difference between the infrared data and the projection off data. Because the data block satisfying the number of data points with values greater than the first threshold is greater than the second threshold is an overexposed data block, and the projection off data is TOF data acquired by a TOF camera with a TOF light source being off, the difference between the infrared data and the projection off data can correct the overexposure, improving quality of the first frame of the TOF image.