A predetermined image correction process is automatically performed on an image, and information for identifying that the predetermined image correction process has been performed is displayed in a state that an image having undergone the predetermined image correction process is being displayed.

A system includes a computing device that receives a query thyroid image, where the query thyroid image is an ultrasound image of a thyroid comprising a thyroid nodule of interest. The computing device processes the query thyroid nodule image using a machine learning model to identify at least one labelled thyroid image from a plurality of labelled thyroid images that is similar to the query thyroid nodule image. The plurality of labelled thyroid images are used as training data to generate the machine learning model. The at least one labelled thyroid image has labels associated therewith and comprises an ultrasound image of a thyroid nodule that has a confirmed diagnosis. The computing device generates an output report based on the labels associated with the at least one labelled thyroid image, where the output report indicates whether the thyroid nodule of interest resembles a malignant thyroid nodule or benign thyroid nodule.

Techniques are provided for providing a user with retrieved images of a specific article of clothing or accessory worn by models having a particular body type and pose as selected by the user. The images have been analyzed to identify both a body type and pose of the model wearing the article of clothing in each of the images. The images are labeled based on at least body type and pose of the model and clustered based on their labels, and are thus available for retrieval by subsequent requests by a user. In particular, a user that is interested in the article of clothing can input one or more requests for images of models having a selected body type and pose. Any of the images labeled with the selected body type and pose can then be provided to the user in any number ways.

Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. A representation is generated for each image using a first machine learning model. One or more second machine learning models are then used to analyze each image using the corresponding representation. The analyses can be provided to a consuming application or process for quality assurance analysis.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to predict optical flow. One of the methods includes obtaining a batch of one or more training image pairs; for each of the pairs: processing the first training image and the second training image using the neural network to generate a final optical flow estimate; generating a cropped final optical flow estimate from the final optical flow estimate; and training the neural network using the cropped optical flow estimate.

Digital imaging and learning systems and methods are described for analyzing pixel data of a scalp region of a user's scalp to generate one or more user-specific scalp classifications. A digital image of a user is received at an imaging application (app) and comprises pixel data of at least a portion of a scalp region of the user's scalp. A scalp based learning model, trained with pixel data of a plurality of training images depicting scalp regions of scalps of respective individuals, analyzes the image to determine at least one image classification of the user's scalp region. The imaging app generates, based on the at least one image classification, a user-specific scalp classification designed to address at least one feature identifiable within the pixel data comprising the at least the portion of a scalp region of the user's scalp.

A system, and method of operating the same detects moving targets in images and performs image turbulence correction. The system includes an automatic target recognizer (ATR) system including a database. The ATR includes a feature extractor and processor arranged to detect a plurality of reference features associated with targets within image frames, and calculate a position of the plurality of reference features. The system includes an image processor arranged to receive the position, demosaic the image frames into a plurality of video tiles, iteratively process the video tiles for turbulence correction to generate turbulence corrected video tiles associated with acquired targets; convert the turbulence corrected video tiles into a single video frame tile including turbulence degradation correction; and mosaic each of the single video frame tiles to generate a full field of view turbulence corrected video stream.

A picture processing method and an electronic device are provided. The method includes: receiving a first input by a user in a case that a first interface is displayed. The first interface includes N target identifiers. The first input is an input by the user for M target identifiers in the N target identifiers. N and M are both integers greater than 1. M is less than or equal to N. The method further comprises updating the first interface to a second interface in response to the first input. The second interface includes M pictures indicated by the M target identifiers. The method also includes receiving a second input by the user for the M pictures. In response to the second input, the method additionally includes performing synthesis processing on the M pictures according to a size of each of the M pictures to obtain a target synthesized picture.

The present disclosure provides an image processing apparatus and an image processing method. The method comprising: performing a target detection on a first image to generate a first to-be-processed image, the first to-be-processed image has a size smaller than the size of the first image; performing the target detection on a second image to generate a second to-be-processed image, the second to-be-processed image has a size smaller than the size of the second image, the first image and the second image constitute an image pair containing the target, and a disparity exists between the first image and second image; and calculating, based on the first to-be-processed image and the second to-be-processed image, a disparity value of the target in the first to-be-processed image and the second to-be-processed image.

There is provided an information processing method, an information processing apparatus, and a program, by which the accuracy of facial authentication can be improved even in a case where there is an occluded region. The information processing apparatus includes a determination unit and a generation unit. The determination unit determines, on the basis of an occluded region of a face in an input facial image for authentication, a trimming facial range from the input facial image for authentication and a resolution. The generation unit generates a facial image for authentication in/at the trimming facial range and the resolution determined by the determination unit.