A device configured to detect a triggering event corresponding with a user placing a first item on the platform, to capture a first image of the first item on the platform using a camera, and to input the first image into a machine learning model that is configured to output a first encoded vector based on features of the first item that are present in the first image. The device is further configured to identify a second encoded vector in an encoded vector library that most closely matches the first encoded vector and to identify a first item identifier in the encoded vector library that is associated with the second encoded vector. The device is further configured to identify the user, to identify an account that is associated with the user, and to associate the first item identifier with the account of the user.

Systems, methods, and non-transitory computer-readable media can be configured to train a machine learning model to identify corrupted frames of videos based on training data including video frames exhibiting corruption that is intentionally generated. A frame of a video can be provided to the trained machine learning model. A score indicating a likelihood that the frame of the video exhibits corruption can be determined based on the trained machine learning model.

A device configured to capture a first image of an item on a platform using a camera and to determine a first number of pixels in the first image that corresponds with the item. The device is further configured to capture a first depth image of an item on the platform using a three-dimensional (3D) sensor and to determine a second number of pixels within the first depth image that corresponds with the item. The device is further configured to determine that the difference between the first number of pixels in the first image and the second number of pixels in the first depth image is less than the difference threshold value, to extract the plurality of pixels corresponding with the item in the first image from the first image to generate a second image, and to output the second image.

A computer-implemented method of training a convolutional neural network configured to morph content features of an input image with style features of a style image is provided. The computer-implemented method includes selecting a training style image; extracting style features of the training style image; selecting a training content image; extracting content features of the training content image; processing the training content image through the convolutional neural network to generate a training output image including the content features of the training content image morphed with the style features of the training style image; extracting content features and style features of the training output image; computing a total loss; and tuning the convolutional neural network based on the total loss including a content loss, a style loss, and a regularization loss.

This application discloses a video image anti-shake method and a terminal, and relates to the field of image processing, to implement compensation for translational shake in a Z direction. A video image anti-shake method includes: turning on, by a terminal, a camera lens, and photographing a video image by using the camera lens; detecting, by the terminal, shake on an X-axis, a Y-axis, and a Z-axis during photographing, where the Z-axis is an optical axis of the camera lens, the X-axis is an axis perpendicular to the Z-axis on a horizontal plane, and the Y-axis is an axis perpendicular to the Z-axis on a vertical plane; and performing, by the terminal, anti-shake processing on the video image based on the shake on the X-axis, the Y-axis, and the Z-axis. Embodiments of this application are applied to video image anti-shake.

A target detection method and apparatus, an electronic device and a computer-readable storage medium are provided by the embodiments of the present disclosure. The method includes: obtaining a detection result by performing a target detection on a to-be-detected image, wherein the detection result comprises a target classification to which a target object involved in the to-be-detected image belongs and position information corresponding to the target object involved in the to-be-detected image; cropping out a proposal image involving the target object from the to-be-detected image based on the position information; determining a confidence that the target object belongs to a target classification based on the proposal image; and deleting, in response to that the confidence is less than a preset threshold, an information item concerned the target object from the detection result.

In learning processing performed before sample observation processing (steps S705 to S708), the sample observation device acquires a low-picture quality learning image under a first imaging condition for each defect position indicated by defect position information, determines an imaging count of a plurality of high-picture quality learning images associated with the low-picture quality learning image for each defect position and a plurality of imaging points based on a set value of the imaging count, acquires the plurality of high-picture quality learning images under a second imaging condition (step S702), learns a high-picture quality image estimation model using the low-picture quality learning image and the plurality of high-picture quality learning images (step S703), and adjusts a parameter related to the defect detection in the sample observation processing using the high-picture quality image estimation model (step S704).

Systems and methods for automating image annotations are provided, such that a large-scale annotated image collection may be efficiently generated for use in machine learning applications. In some aspects, a mobile device may capture image frames, identifying items appearing in the image frames and detect objects in three-dimensional space across those image frames. Cropped images may be created as associated with each item, which may then be correlated to the detected objects. A unique identifier may then be captured that is associated with the detected object, and labels are automatically applied to the cropped images based on data associated with that unique identifier. In some contexts, images of products carried by a retailer may be captured, and item data may be associated with such images based on that retailer's item taxonomy, for later classification of other/future products.

Systems, apparatuses, and methods are described herein for disease detection using an analyte-agnostic approach. Such systems, apparatuses, and methods can include using an array with hydrogels disposed on a substrate, where the hydrogels include one or more polymerized monomers and one or more photoinitiators or photocleavage products thereof. One or more samples including one or more unlabeled analytes can be contacted with an array of polymers. The samples disposed on the array can be incubated for a first predetermined period of time, and heated at a predetermined temperature for a second predetermined period of time. An imaging device (e.g., flatbed scanner) can be used to measure an amount of one or more colorimetric or luminescence signals produced by the array after the incubating and heating. A neural network trained using the samples can then be used to predict a diagnostic or disease class for the sample.

Embodiments of the present disclosure relate to the field of image processing technologies and disclose an image processing method and apparatus, an electronic device, and a computer-readable storage medium. The image processing method includes: when an obtained first image includes a human face, performing a first transformation process on the first image to obtain a second image; determining, based on a first target face key point of the human face in the first image, a target position, in the first image, of a second target face key point of the human face in the second image; performing a first movement process on the second image based on the target position; and generating a target image based on the first image and the second image processed through the first movement process, and displaying the target image.