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Assessing Data Quality of Annotations with Krippendorff Alpha For Applications in Computer Vision
In this paper we studied this problem for object detection and recognition. We conducted several data annotation experiments to measure inter annotator agreement and consistency, as well as how the selection of ground truth impacts the perceived algorithm performance. We propose a methodology to monitor the quality of annotations during the labeling of images and how it can be used to measure performance.
Contextual Sense Making by Fusing Scene Classification, Detections, and Events in Full Motion Video
We have investigated and designed a system capable of detecting events and activities of interest that deviate from the baseline patterns of observation given FMV feeds.
A Common Operating Picture Framework Leveraging Data Fusion and Deep Learning
These datasets (e. g. geocoded features, video streams, raw text, social network data, etc.)
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