Search Results for author:
Found 9 papers, 2 papers with code
Improving Emergency Response during Hurricane Season using Computer Vision
We have developed a framework for crisis response and management that incorporates the latest technologies in computer vision (CV), inland flood prediction, damage assessment and data visualization.
Improving Community Resiliency and Emergency Response With Artificial Intelligence
New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases.
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.)
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.
Semantic Stereo for Incidental Satellite Images
The increasingly common use of incidental satellite images for stereo reconstruction versus rigidly tasked binocular or trinocular coincident collection is helping to enable timely global-scale 3D mapping; however, reliable stereo correspondence from multi-date image pairs remains very challenging due to seasonal appearance differences and scene change.
Sensor Adaptation for Improved Semantic Segmentation of Overhead Imagery
In this paper we present our work on semantic segmentation with applications to overhead imagery.
Improved Image Segmentation via Cost Minimization of Multiple Hypotheses
We scan the segmentation parameter space and generate a collection of image segmentation hypotheses (from highly over-segmented to under-segmented).
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning
We have experimented with several state-of-the-art architectures.
