Systems and methods are disclosed for selecting target objects within digital images. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user indicators to select targeted objects in digital images. Specifically, the disclosed systems and methods can transform user indicators into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

A display device and an operating method thereof are provided. The display device may include: a display; a camera; a memory configured to store one or more instructions; and a processor configured to execute the instructions to obtain an image captured by the camera, transform the image based on visual condition information of a user, the visual condition information including information about a type of visual impairment of the user, and display the transformed image on the display.

An apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to obtain an input visual stimulus, to detect feature points in the input visual stimulus, and to identify densities of the detected feature points in each of two or more distinct regions of the input visual stimulus. The at least one processing device is also configured to determine relative information density in the two or more distinct regions of the input visual stimulus. The at least one processing device is further configured to modify a design of the input visual stimulus to adjust the relative information density among at least two of the two or more distinct regions of the input visual stimulus.

A device may receive a video and corresponding camera information associated with a camera that captured the video, and may select an object in the video and a wire model for the object. The device may adjust an orientation, location, or size of the wire model to align the wire model on the object in a frame of the video, based on the corresponding camera information and to generate an adjusted wire model. The device may identify the object in another frame of the video, and may align the adjusted wire model on the object in the other frame. The device may interpolate the adjusted wire model for the object for intermediate frames of the video between the first and other frames, and may generate three-dimensional annotations for the video based on the adjusted wire models. The device may train a machine learning model based on the three-dimensional annotations.

A method includes: obtaining a first image of an object including a surface having a non-flat shape; identifying a region corresponding to the surface as a region of interest by applying the first image to a first artificial intelligence model; obtaining data about a three-dimensional (3D) shape type of the object by applying the first image to a second AI model; obtaining a set of values of a 3D parameter related to the object, the surface, or the first camera, based on the region and the data; estimating the non-flat shape of the surface, based on the set of values of the 3D parameter; and obtaining a flat surface image in which the non-flat shape of the surface is flattened, by performing a perspective transformation on the surface.

Systems and methods for training an artificial intelligence (AI) classifier of scanned items. The items may include a training set of sample raw scans. The set may include in-class objects and not-in-class raw scans. An AI classifier may be configured to sample raw scans in the training set, measure errors in the results, update classifier parameters based on the errors, and detect completion of training.

An input display control device includes: a curve information acquisition unit for acquiring curve information indicating a curve; a character string acquisition unit for acquiring a character string; and a display control unit for generating display information for displaying the character string acquired by the character string acquisition unit along the curve indicated by the curve information acquired by the curve information acquisition unit, in which when a part of the character string acquired by the character string acquisition unit protrudes out of a display area of a display since a length of the character string acquired by the character string acquisition unit is long, the display control unit disposes the part of the character string in a virtual area that is an area surrounding the display area.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing scale-diverse segmentation neural networks to analyze digital images at different scales and identify different target objects portrayed in the digital images. For example, in one or more embodiments, the disclosed systems analyze a digital image and corresponding user indicators (e.g., foreground indicators, background indicators, edge indicators, boundary region indicators, and/or voice indicators) at different scales utilizing a scale-diverse segmentation neural network. In particular, the disclosed systems can utilize the scale-diverse segmentation neural network to generate a plurality of semantically meaningful object segmentation outputs. Furthermore, the disclosed systems can provide the plurality of object segmentation outputs for display and selection to improve the efficiency and accuracy of identifying target objects and modifying the digital image.

An image data classification method which includes distributing image data to n users is provided. N marks corresponding to the image data are collected by collecting the mark made by each of the n users on the image data. Once target marks are determined from the n marks and a rate of the target marks is calculated, a quality of the image data is determined according to the rate of the target marks.

A surveillance camera system includes a camera that acquires images and that has an adjustable field of view. A processing device is operably coupled to the camera. The processing device allows a user to define a virtual mask within the acquired images. The processing device also tracks a moving object of interest in the acquired images with a reduced level of regard for areas of the acquired images that are within the virtual mask.