J. Harlan Yates

Autonomous selection of PDE inpainting techniques vs. exemplar inpainting techniques for void fill of high resolution digital surface models

High resolution Digital Surface Models (DSMs) may contain voids (missing data) due to the data collection process used to obtain the DSM, inclement weather conditions, low returns, system errors/malfunctions for various collection platforms, and other factors. DSM voids are also created during bare earth processing where culture and vegetation features have been extracted. The Harris LiteSiteTM Toolkit handles these void regions in DSMs via two novel techniques. We use both partial differential equations (PDEs) and exemplar based inpainting techniques to accurately fill voids. The PDE technique has its origin in fluid dynamics and heat equations (a particular subset of partial differential equations). The exemplar technique has its origin in texture analysis and image processing. Each technique is optimally suited for different input conditions. The PDE technique works better where the area to be void filled does not have disproportionately high frequency data in the neighborhood of the boundary of the void. Conversely, the exemplar based technique is better suited for high frequency areas. Both are autonomous with respect to detecting and repairing void regions. We describe a cohesive autonomous solution that dynamically selects the best technique as each void is being repaired.

A qualitative and quantitative method for predicting sentiment toward deployed U.S. forces

The ability to automatically predict likelihood of reaction to specific events and situational awareness is important to many military and commercial applications. Gauging population sentiment for targeted response areas and having the ability to predict or control sentiment within these areas is invaluable. Review of reception towards deployed forces must be analyzed, especially in areas vital for U.S. national interests. Predicting population behavior is critical for success and must include a qualitative as well as a quantitative solution. Additionally, a feedback mechanism is needed for periodically updating reception towards presence of U.S. Forces over time. We propose a method for predicting sentiment towards deployed U.S. Forces in near real time, to efficiently propitiate manpower resources, allocate equipment assets, and reduce cost of analyses. Sentiment prediction is becoming an increasingly important and feasible task based on social media, open source data, physical imagery and abundance of video data feeds. Predicting reaction to events can be time consuming. Locating the most likely affected areas is very tedious, requiring much human labor effort, and it is often difficult to obtain the best information on a timely basis. An efficient tool would be helpful to rapidly parse text that has been extracted from an intelligent algorithm in order to evaluate the population sentiment for the targeted area. Multiple data inputs and artificial intelligence (AI) algorithms are required in order to support sound decision making theory. The goal of our system, called GlobalSite, is to deliver trustworthy threat analysis systems and services that understand situations, while being a vital tool for continuing mission operations information.

An efficient visualization method for analyzing biometric data

We introduce a novel application for biometric data analysis. This technology can be used as part of a unique and systematic approach designed to augment existing processing chains. Our system provides image quality control and analysis capabilities. We show how analysis and efficient visualization are used as part of an automated process. The goal of this system is to provide a unified platform for the analysis of biometric images that reduce manual effort and increase the likelihood of a match being brought to an examiner’s attention from either a manual or lights-out application. We discuss the functionality of FeatureSCOPE™ which provides an efficient tool for feature analysis and quality control of biometric extracted features. Biometric databases must be checked for accuracy for a large volume of data attributes. Our solution accelerates review of features by a factor of up to 100 times. Review of qualitative results and cost reduction is shown by using efficient parallel visual review for quality control. Our process automatically sorts and filters features for examination, and packs these into a condensed view. An analyst can then rapidly page through screens of features and flag and annotate outliers as necessary.