Award Data

The ubiquity of image sensors for data collection creates a glut of data, which leads to bottlenecks in the processing capabilities of modern systems. In order to process this data, meticulously labeled datasets are required and that must be reviewed by humans in order to guarantee state-of-the-art performance. In this effort we endeavor to create a system that can automatically exploit salient in ...

TP Engineering personnel have extensive experience with electro-optic systems and high Pulse Repetition Frequency (PRF) Laser systems. We have detailed knowledge of Pockels cell systems enabling active gated imaging through foliage at PRF 100 kHz PRF. Such systems can dramatically improve and protect Geiger-mode LIDAR by both controlling the transmitter output and gating out unwanted return lig ...

In the pattern of life problem space, data is often represented via mathematical graphs, in which a variety of algorithms may be employed to conduct semi-autonomous analysis. While successful empirical application of graph-domain algorithms on ABI problems has been achieved, most of these algorithms require a tuning parameter, which is often set heuristically in real-world scenarios. Arete has dev ...

Object detection datasets for overhead imagery are typically generated using bootstrapping methods to reduce annotator effort and cost. These methods iteratively train a detector from a limited set of user-provided and model-predicted labels. Such approaches bias detectors toward the initial object set, limiting their capacity to handle object variations or discover novel objects classes. MONET ov ...

NGA uses deep networks for many tasks including image registration, land cover segmentation, and object detection. Current deep learning approaches develop specialist networks for each task and type of data. Not only is this inefficient, because networks can’t be reused across tasks, this approach ignores correlations between tasks and data sources that can improve performance. In response, we w ...

As the Intelligence Community’s experts in geospatial analytics, the National Geospatial-Intelligence Agency (NGA) has a long-standing interest in conducting research and development in analyzing overhead imagery. With the commercialization of space, the need to analyze greater volumes of imagery much more quickly continues to progress. Fortunately, recent advances in computer vision (CV) have m ...

One of the most pressing problems at the forefront of machine learning and computer vision research is image recognition when high quality labeled data is difficult to acquire, typically due to the prohibitive cost of data acquisition and annotation. Clarifai has teamed with L3Harris to combine their unparalleled experience in simulating geospatial sensors with our award-winning computer vision ca ...

To address the NGA’s need to fully automate recovery of camera orientation parameters from ground-level imagery, Physical Optics Corporation (POC) proposes to develop new Automated Camera Orientation Recovery Software (ACORS). It is based on a new, multicue combination of algorithms for finding true horizon lines in images. Specifically, the innovation in locating occluded true horizon lines bel ...

For this proposal, Clarifai would utilize internal expertise in computer vision and deep learning to pursue a CNN to camera orientation estimation. Specifically, we would adapt existing Clarifai models to extract image features and use these features as input to a camera orientation regressor. The horizon filter information might be combined with information from the encoder to produce a joint emb ...

Arete will integrate the Mover Intelligence Extraction Engine with the Social Structured Framework (SSF) to produce a capability to generate narratives of MOVINT track data with context derived from associated Geographic Information Systems (GIS) and other foundation data. The Extraction Engine will be derived from the Arete Cognitive Behavior Classifier (CBC) to characterize MOVINT tracks into co ...