Abstract: Methods and systems for processing data to distinguish between a plurality of conditions in lung ultrasound images and, in particular, lung ultrasound images containing B lines. Neural network systems and methods, in which the processor is trained using lung ultrasound images to distinguish between acute respiratory distress syndrome due to COVID-19, acute respiratory distress syndrome due to non-COVID-19 causes, and hydrostatic pulmonary edema.

Abstract: A system and method for online projector-camera calibration from one or more images is provided. The system comprises: a projector, a camera, and a calibration device configured to: determine a map between pixels of each of a projector image and a camera image in fewer than one hundred percent of pixels of the projector image using feature extraction; determine an initial estimate of a fundamental matrix from the map; determine an initial guess of intrinsic properties of the projector and camera using one or more closed-form solutions; iteratively determine an error-function based on the map using the fundamental matrix while adding constraints on the intrinsic properties using the one or more closed-form solutions, and the initial estimate and guess as initial input; and, when the error-function reaches an acceptance value, determine intrinsic and extrinsic properties of the projector and the camera from current values of iterative estimates.

Abstract: A system and method for online projector-camera calibration from one or more images is provided. The system comprises: a projector, a camera, and a calibration device configured to: determine a map between pixels of each of a projector image and a camera image in fewer than one hundred percent of pixels of the projector image using feature extraction; determine an initial estimate of a fundamental matrix from the map; determine an initial guess of intrinsic properties of the projector and camera using one or more closed-form solutions; iteratively determine an error-function based on the map using the fundamental matrix while adding constraints on the intrinsic properties using the one or more closed-form solutions, and the initial estimate and guess as initial input; and, when the error-function reaches an acceptance value, determine intrinsic and extrinsic properties of the projector and the camera from current values of iterative estimates.