Raytheon Company

Jeffrey S Klein

Franklin R Tanner

Jody Dale Verret

Philip A Sallee

Stephen J Raif

James N Head

Jonathan Goldstein

James Talamonti

Steven A Israel

Shane A Zabel

Lisa A Mccoy

Wenkai Qin

Grayson D Gunderson

Systems, devices, methods, and computer-readable media for horizon-based navigation. A method can include receiving image data corresponding to a geographical region in a field of view of an imaging unit and in which the device is situated, based on the received image data, generating, by the processing unit, an image horizon corresponding to a horizon of the geographical region and from a perspective of the imaging unit, projecting three-dimensional (3D) points of a 3D point set of the geographical region to an image space of the received image data resulting in a synthetic image, generating, by the processing unit, a synthetic image horizon of the synthetic image, and responsive to determining the image horizon sufficiently correlates with the synthetic image horizon, providing a location corresponding to a perspective of the synthetic image as a location of the processing unit.

Horizon-based navigation

A machine receives a large image having large image dimensions that exceed memory threshold dimensions. The large image includes metadata. The machine adjusts an orientation and a scaling of the large image based on the metadata. The machine divides the large image into a plurality of image tiles, each image tile having tile dimensions smaller than or equal to the memory threshold dimensions. The machine provides the plurality of image tiles to an artificial neural network. The machine identifies, using the artificial neural network, at least a portion of the target in at least one image tile. The machine identifies the target in the large image based on at least the portion of the target being identified in at least one image tile.

Target identification in large image data

A machine receives, for a given target in a given image, a plurality of annotations, each annotation indicating a range of pixels corresponding to the given target. The machine computes a number of pixels in an intersection of the plurality of annotations. The machine computes an average pixels on target (APOT) for the given target by computing, for each annotation, a number of pixels that corresponds to the given target and taking a mean of those numbers. The machine computes a precision value of the plurality of annotations as the number of pixels in the intersection divided by the APOT. The machine provides an output representing the precision value.

Calculating the precision of image annotations

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Jeffrey S Klein