Provided is a method for extracting an image salient curve. The method comprises the following steps: drawing an approximate curve along a salient edge of an image from which a salient curve is to be extracted; obtaining short edges in the image; calculating a harmonic vector field by using the drawn curve as a boundary condition; filtering the short edges in the image by using the harmonic vector field; updating the vector field by using the short edges left in the image as boundary conditions; and obtaining an optimal salient curve of the image by using the energy of a minimized spline curve in the vector field. Also provided is a system for extracting an image salient curve. The image salient curve can ensure the smoothness and a bending characteristic.

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.

Disclosed is a computer implemented method for identifying an object in a plurality of images. The method may include a step of receiving, through an input device, a delineation of the object in at least one image of the plurality of images. Further, the method may include a step of identifying, using the processor, an image region corresponding to the object in the at least one image based on the delineation. Furthermore, the method may include a step of tracking, using the processor, the image region across the plurality of images.

This disclosure describes methods and systems for improving quality in video production. A video production device receives inputs from a user for only a subset of image frames presented for use in producing a video. Each of the inputs indicates a point of interest (POI) in a corresponding image frame, indicative of a region of interest for inclusion as a scene in the video. A video processor evaluates a spatial path of the indicated POIs relative to a bounding scene of interest (BSI), which represents an extent of a field of view of a corresponding camera, and dynamically adjusts the field of view to steer subsequently acquired image frames to be within the BSI. The video processor produces the video with all scenes arranged successively at a periodic interval. Use of the spatial path for scene or camera-viewpoint selection is designed to optimize quality of the produced video.

A computing system includes a processor; and a memory having stored thereon an adjustment application comprising computer-executable instructions that, when executed, cause the computing system to: display a graphical user interface including a digital medical image of a patient; superimpose a bounding box; receive an adjustment of an area of interest; and provide an adjusted digital medical image. A non-transitory computer-readable medium includes computer-executable instructions that, when executed via one or more processors, cause a computer to: display a graphical user interface including a digital medical image of a patient; superimpose a bounding box; receive an adjustment of an area of interest; and provide an adjusted digital medical image. A computer-implemented method includes: displaying a graphical user interface including a digital medical image of a patient; superimposing a bounding box; receiving an adjustment of an area of interest; and providing an adjusted digital medical image.

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.

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.

Embodiments are directed to use of a rapid-test-validation computing device to determine if a result of a rapid test device is valid and identify the result. The rapid-test-validation computing device captures images of the rapid test device and employs a first artificial intelligence mechanism to determine if the rapid test device is properly aligned in the images. The rapid-test-validation computing device then employs a second artificial intelligence mechanism to determine if a result of the rapid test device is valid or invalid. If the result is valid, the rapid-test-validation computing device employs a third artificial intelligence mechanism to determine and present an objective output of the rapid test device result to a user; otherwise, the rapid-test-validation computing device presents a notification to the user that the rapid test device result is invalid.

Systems and methods for navigating a vehicle within an environment are provided. In one aspect, a method comprises: (a) selecting, with aid of a processor, a subset of a plurality of sensors to be used for navigating the vehicle within the environment based on one or more predetermined criteria, wherein the plurality of sensors are arranged on the vehicle such that each sensor of the plurality of sensors is configured to obtain sensor data from a different field of view; (b) processing, with aid of the processor, the sensor data from the selected sensor(s) so as to generate navigation information for navigating the vehicle within the environment; and (c) outputting, with aid of the processor, signals for controlling the vehicle based on the navigation information.

A medical scanning system and method for determining scanning parameters based on a scout image, the system includes: a scanned object description module for describing the shape of a scanned object on an initial image; an adjustment module for aligning the shape of the scanned object with the pre-stored average shape; a principal component analysis module for extracting the principal component for the aligned shape of the scanned object; a desired shape acquisition module for imparting weight parameters to said principal component, acquiring a plurality of new shapes, and from said plurality of new shapes, determining the new shape with the maximum cost function value as the desired shape and a scanning parameter setting module for setting scanning parameters based on the desired shape.