Výsledky vyhledávání - "Ward, Chris M"

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Autor: Ward, Chris M., Harguess, Josh, Tao, Julia, Christman, Daniel, Spicer, Paul, Tan, Mike

We introduce the AI Security Pyramid of Pain, a framework that adapts the cybersecurity Pyramid of Pain to categorize and prioritize AI-specific threats. This framework provides a structured approach to understanding and addressing various levels of

Externí odkaz: http://arxiv.org/abs/2402.11082

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Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)

Autor: Ward, Chris M., Harguess, Josh, Crabb, Brendan, Parameswaran, Shibin

Publikováno v: Proceedings Volume 10396, Applications of Digital Image Processing XL; 1039605 (2017)

Traditional metrics for evaluating the efficacy of image processing techniques do not lend themselves to understanding the capabilities and limitations of modern image processing methods - particularly those enabled by deep learning. When applying im

Externí odkaz: http://arxiv.org/abs/1905.05373

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Leveraging synthetic imagery for collision-at-sea avoidance

Autor: Ward, Chris M., Harguess, Josh, Corelli, Alexander G.

Publikováno v: Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 1064507 (4 May 2018)

Maritime collisions involving multiple ships are considered rare, but in 2017 several United States Navy vessels were involved in fatal at-sea collisions that resulted in the death of seventeen American Servicemembers. The experimentation introduced

Externí odkaz: http://arxiv.org/abs/1905.04828

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Ship classification from overhead imagery using synthetic data and domain adaptation

Autor: Ward, Chris M., Harguess, Josh, Hilton, Cameron

In this paper, we revisit the problem of classifying ships (maritime vessels) detected from overhead imagery. Despite the last decade of research on this very important and pertinent problem, it remains largely unsolved. One of the major issues with

Externí odkaz: http://arxiv.org/abs/1905.03894

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