Dilip R. Patlolla

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The data captured by existing standoff biometric systems typically has lower biometric recognition performance than their close range counterparts due to imaging challenges, pose challenges, and other factors. To assist in overcoming these limitations systems typically perform in a multi-modal capacity such as Honeywell's Combined Face and Iris (CFAIRS)(More)
Managing a wide variety of mobile devices across multiple mobile operating systems is a security challenge for any organization [1, 2]. With the wide adoption of mobile devices to access work-related apps, there is an increase in third-party apps that might either misuse or improperly handle user's personal or sensitive data [3]. HTML5 has been receiving(More)
Computer vision algorithms for image analysis are often computationally demanding. Application of such algorithms on large image databases--- such as the high-resolution satellite imagery covering the entire land surface, can easily saturate the computational capabilities of conventional CPUs. There is a great demand for vision algorithms running on high(More)
Explosion of spatial data from satellite to citizen sensors has posed the critical challenge of Big Spatial Data integration, analysis, and visualization. This article focuses on research and development activities at Oak Ridge National Laboratory (ORNL) that are addressing end-user applications utilizing high performance computing based geospatial science(More)
Spatiotemporal analytics derived from large-scale analysis of satellite imagery is a key component in the models used for global population mapping, risk analysis, and critical infrastructure assessment. Making informed national-level decisions based on image derived analytics require processing petabytes of high resolution satellite image data. The(More)
We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level(More)
Design of data structures for high performance computing (HPC) is one of the principal challenges facing researchers looking to utilize heterogeneous computing machinery. Heterogeneous systems derive cost, power, and speed efficiency by being composed of the appropriate hardware for the task. Yet, each type of processor requires a specific organization of(More)
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