Abstract: Certain aspects of the present disclosure provide techniques for performing surgical ophthalmic procedures, such as cataract surgeries. An example method of determining one or more intraocular lens (IOL) parameters for an IOL to be used in a cataract surgery procedure includes generating, using a fused machine learning model, recommendations including one or more IOL parameters for the IOL to be used in the cataract surgery based, at least in part, on first predicted lens behavior for each of one or more IOLs by an emulated finite element analysis (EFEA) model and the second predicted lens behavior for each of the one or more IOLs by an IOL power calculator (IPC) machine learning model.

Abstract: A passive automatic target recognition (ATR) system includes a range map processor configured to generate range-to-pixel map data based on digital elevation map data and parameters of a passive image sensor. The passive image sensor is configured to passively acquire image data. The passive ATR system also includes a detection processor configured to identify a region of interest (ROI) in the passively acquired sensor image data based on the range-to-pixel map data, and an ATR processor configured to generate an ATR decision for the ROI.

Abstract: A passive automatic target recognition (ATR) system includes a range map processor configured to generate range-to-pixel map data based on digital elevation map data and parameters of a passive image sensor. The passive image sensor is configured to passively acquire image data. The passive ATR system also includes a detection processor configured to identify a region of interest (ROI) in the passively acquired sensor image data based on the range-to-pixel map data, and an ATR processor configured to generate an ATR decision for the ROI.

Abstract: A method and apparatus for tracking an object in an image. An image of a scene comprising an object is received. The image is processed with a first tracker to produce a first estimate of a position of the object in the image and a first confidence value. The image is processed with a second tracker to produce a second estimate of the position of the object in the image and a second confidence value. The second tracker processes the image concurrently with the first tracker. An estimate of the position of the object in the image is selected using the first and second confidence values. The object is then tracked using the selected estimate.

Abstract: A method and apparatus for tracking an object in an image. An image of a scene comprising an object is received. The image is processed with a first tracker to produce a first estimate of a position of the object in the image and a first confidence value. The image is processed with a second tracker to produce a second estimate of the position of the object in the image and a second confidence value. The second tracker processes the image concurrently with the first tracker. An estimate of the position of the object in the image is selected using the first and second confidence values. The object is then tracked using the selected estimate.