Systems and methods for image processing for determining a registration map between a first image of a scene with a second image of the scene, include solving an optimal transport (OT) problem to produce the registration map by optimizing a cost function that determines a minimum of a ground cost distance between the first and the second images modified with an epipolar geometry-based regularizer including a distance that quantifies the violation of an epipolar geometry constraint between corresponding points defined by the registration map. The ground cost compares a ground cost distance of features extracted within the first image with a ground cost distance of features extracted from the second image.

The described aspects and implementations enable fast and accurate verification of radar detection of objects in autonomous vehicle (AV) applications using combined processing of radar data and camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar data characterizing intensity of radar reflections from an environment of the AV, identifying, based on the radar data, a candidate object, obtaining a camera image depicting a region where the candidate object is located, and processing the radar data and the camera image using one or more machine-learning models to obtain a classification measure representing a likelihood that the candidate object is a real object.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

A receiver receives a radio wave transmitted to a target and scattered by the target to acquire a signal. An imaging unit generates a 3D complex image of the target based on the signal. A value extraction unit extracts intensity information and phase in including an intensity matrix and a phase matrix, the extracted intensity information constituting the intensity matrix and the extracted phases information constituting the phase matrix. A subset selection unit selects a subset from the value set. A transformation unit changes a representation of the subset to generate a 2D real image. A detection unit detects whether there is an undesired object on the target based on the 2D real image.

In one embodiment, a method includes determining an operational status of a vehicle including a radar antenna. The operational status is related to autonomous-driving operations of the vehicle in an environment. The method includes determining an expected amount of signaling resources associated with the radar antenna to be utilized by the vehicle while the vehicle performs the autonomous-driving operations, based at least on the operational status of the vehicle and the environment. The method includes determining to switch one or more of the signaling resources associated with the radar antenna from a first mode to a second mode based on the expected amount of signaling resources to be utilized by the radar antenna while the vehicle performs the autonomous-driving operations. The method includes causing the one or more of the signaling resources associated with the radar antenna to switch from the first mode to the second mode.

Systems and methods for satellite Synthetic Aperture Radar (SAR) artifact suppression for enhanced three-dimensional feature extraction, change detection, and/or visualizations are described. In some aspects, the described systems and methods include a method for suppressing artifacts from complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for creating a photo-realistic 3D model of a scene based on complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for identifying three-dimensional (3D) features and changes in SAR imagery.

A peak label object detection system (PLODS) includes an object size database configured to store information related to object size for a plurality of objects. The PLODS further includes a three-dimensional (3D) sensor database configured to store information related to parameters of a 3D sensor. The PLODS further includes an annotation database configured to store ground truth annotation information for images. The PLODS further includes a peak shape parameter calculator configured to determine a peak label size based on object size from the object size database and the parameters of the 3D sensor. The PLODS further includes a label generator configured to generate a peak labels map based on label size and the ground truth annotation information.

A computing system includes at least one processor, and a memory communicatively coupled to the at least one processor. The processor is configured to receive at least two successive radar images of precipitation data, generate a motion vector field using the at least two successive radar images, forecast linear prediction imagery of future precipitation using the motion vector field, and generate corrected output imagery corresponding to the forecasted linear prediction imagery of the future precipitation corrected by a first neural network. In addition, the processor is further configured to receive, by a second neural network, the linear prediction imagery, and one of observed imagery and the corrected output imagery, and distinguish, by the second neural network, between the corrected output imagery and the observed imagery to produce conditioned output imagery. The processor is also configured to display the conditioned output imagery on a display.

A preprocessing technique for synthetic radar images. An embodiment of a method for preprocessing synthetic aperture radar images includes: receiving range-compressed radar data generated from raw radar image data on-board a satellite or an airborne vehicle; generating a preliminary SAR image by performing a pre-focusing on the range-compressed radar data; extracting image subsectors from the preliminary SAR image; transmitting the extracted image subsectors to an on-ground portion; reconstructing the range-compressed radar data pertaining to the extracted image subsectors; and making the range-compressed radar data pertaining to the extracted image subsectors available for a Nominal synthetic aperture radar processor, wherein the Nominal synthetic aperture radar processor is configured to generate a focused SAR image having a nominal value of image resolution that is higher than the resolution of the preliminary SAR image.

A method comprising: obtaining an image; identifying a rotation angle for the image by processing the image with a first neural network; rotating the image by the identified rotation angle to generate a rotated image; classifying the image with a second neural network; and outputting an indication of an outcome of the classification, wherein the first neural network is trained, at least in part, based on a categorical distance between training data and an output that is produced by the first neural network.