VY CORP — Department of Defense SBIR Phase I: N193-A02

Description

Automated pattern and anomaly recognition remains a daunting problem for many use cases. USV and UUV missions ingest large volumes of data that must be continuously evaluated. Artificial intelligence and deep learning systems used for image analysis and pattern recognition have a significant limitation: there is no way to explain how their decisions are made. Conventional edge detection techniques result in too many false positives; this is because they are not cognizant of shape and have no awareness of the photometric characteristics of physical objects or topographical features. An automated system that could reliably monitor data streams for “normal” or expected behavior, and more importantly, reliably identify anomalies or unusual objects would be highly desirable. Vy has developed a powerful set of algorithms collectively called Shape Based Modeling Segmentation (SBMS.) We apply transparent and auditable mathematical models (Bézier curves and decision trees) to collect hard data from visual imagery that significantly increases the speed and accuracy of object recognition. The premise of this proposal is that by applying Vy’s SBMS shape detection to analyze normal output from a conventional waterfall diagram an automated system can be be extended to reliably identify normal output, anomalous output and other targeted objects.