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Computational Intelligence

Vulnerabilities of Deep Learning in Adversarial and/or Degraded Imagery

on June 14, 2017 from 6:00 PM to 6:30 PM

Speaker Dr Joshua Harguess, SPAWAR Systems Center Pacific
Topic Info
Speaker Bio

*PhD Electrical and Computer Engineering, 2011, University of Texas Austin,

Portfolio in Applied Statistical Modeling

 

*MS Computational & Applied Mathematics, 2007, University of Texas Austin,

 

*esearch Scientist, SPAWAR Systems Center Pacific, San Diego,

 

*Successfully managed and executed on the continuing Independent Applied Research project Video notation, Search and Tagging (VAST).

 

*Active member of the Motion Imagery Standards Board (MISB) and image and video quality working group.

 

*Project manager and Augmented Reality (AR) SME for the Gunner's Mate AR project (GunnAR).

polynomials (PLANS 2014), and sensor fusion for the Ground Robotics Group.

 

* In the Automated Imagery Analysis group, performed research on ship classification using state-of-the-art mthods and the effects of pre-processing (SPIE 2012), anomaly detection for ship detection and tracking from full-motion video from UAV. Most recently, developed new algorithm using sparse representation for anomaly detection with applications in many domains.

 

*Performed research into using evolutionary techniques to train a deep learning network to learn features for classification of maritime imagery (ICLR 2014 & GECCO 2014).

 

* In the Unmanned Systems group, performed research on automatic calibration between stereo and infrared (IR) cameras for robot vision and navigation (SPIE2014), motion estimation and optical/range flow for robot ego-motion estimation (PLANS2014), and studying the effects of optical flow on degraded imagery.

 

*Co-founder of both the Computer Vision and the Machine Learning Series group at SSC Pacific which consists of several researchers across the center that meet biweekly to discuss computer vision and machine learning research

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