2018 Gulf of Mexico Spring School (GMSS)Deep Learning and Applications
Abstract
FAMU will We address the challenge of turbulent environment that often disturbs the space andtime registrations of multiple mobile UXV platforms by taking an approach similar to that used in NaturalIntelligence.While current deep learning and artificial neural networks have provided many solutions in differentdomains, e.g. driverless cars, we believe the proposed persistent surveillance Aided Target Recognition(AiTR) in marine environment must emulate the Human Brain Natural Intelligence (NI). For example, NIcan provide the explanation of why "the beauty in the eyes of beholder" for we know the beauty is definedin the feature domain, not in the atoms at the pixel level. The Biological Neural Network (BNN) solutionseparates the steps of Unsupervised Deep Learning (UDL) feature extraction for colors, edges, shapes,contours textures, performed in multiple layers V1~V5 Cortex 17, from Supervised Deep Learning (SDL)pattern classification performed by the Hippocampus Associative Memory.In short, we shall indirectly register every input image and extract each individually their orthonormalfeatures in the Feature vector domains. Those imprecise space-time registered mobile sensory images willbe less sensitive and approximately registered at the feature vector domain. Given a clearer combinedfeatures of all overlapped images with some missing parts we can have a complete reconstruction in thefeature domain, then inversely we can map back to a complete and clear image or video for the persistentsurveillance. We expect to provide a tutorial to expose the computational intelligence (CI) challenges facedby ocean sensing. Collaborators at the workshop will include the IEEE Computational Intelligence SocietyPresident-Elect Prof. Jacek Zurada, the International Neural Network Society President Prof.Bob Kozma, and Korea Advanced Institute of Science & Technology (KAIST)~s Prof. Soo-Young Lee.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 10, 2018
- Source ID
- N000141812342
Entities
People
- Simon Foo
Organizations
- Florida A&M University
- Office of Naval Research
- United States Navy