Science Applications International has been granted a patent for a method involving video sources, sensors, and a computer to calculate and adjust the location of images from weapon-mounted and goggles-mounted sources. The technology uses inertial data and a database to generate graphics related to real-world objects. GlobalData’s report on Science Applications International gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Science Applications International, MITM attack solutions was a key innovation area identified from patents. Science Applications International's grant share as of May 2024 was 45%. Grant share is based on the ratio of number of grants to total number of patents.
Weapon and goggles video image alignment system
A recently granted patent (Publication Number: US11965714B2) outlines a method for predicting positions in images from different video sources, adjusting motion data, and generating modified versions of images based on the predicted positions. The method involves comparing data associated with portions of images from different sources, determining motion data adjustments, and generating modified images with correct positions and rotations. The patent also includes provisions for receiving video data from different sources, such as a Heads-Up Display (HUD) associated with a helmet and a weapon, and generating modified images based on the received data.
Furthermore, the patent describes a system and a non-transitory machine-readable medium with instructions for performing the method outlined in the patent. The system includes a processor and memory with instructions for predicting positions, adjusting motion data, and generating modified images. It also covers scenarios where the video sources are mounted on a helmet and a weapon, and where graphic indicia associated with identified objects in the external environment are generated in the modified images. The non-transitory machine-readable medium contains instructions for performing the method, including predicting positions based on motion data, determining motion data adjustments, and generating modified images with correct positions and rotations, based on data associated with different regions of the images from various sources.
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