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UID:calendar.348191.field_event_date.0@www.wright.edu
DTSTAMP:20260219T203842Z
CREATED:20181205T205313Z
DESCRIPTION:Committee:  Drs. Thomas Wischgoll\, Advisor\, Michael Raymer\, 
 and John GallagherABSTRACT: Tracking of ships in satellite imagery is a ch
 allenging problem in remote sensing since it requires both object detectio
 n and object recognition. Most of the resources available only cover one o
 f these problems and are often filled with machine learning techniques whi
 ch are costly to train. Additionally\, the techniques covered in these res
 ources are often difficult to replicate or may be hard to combine with oth
 er solutions to get a full tracking algorithm.  The proposed framework off
 ers a transparent and efficient alternative to machine learning approaches
  and includes preprocessing\, detection\, and recognition needed for track
 ing. All components of the framework were created based on open source lib
 raries to provide a transparent solution which can be easily modified for 
 specific use cases.
DTSTART;TZID=America/New_York:20181213T100000
DTEND;TZID=America/New_York:20181213T120000
LAST-MODIFIED:20181205T210536Z
LOCATION:278 Joshi - Visualization Lab
SUMMARY:Masters Thesis Defense “Fully Transparent Computer Vision Framework
  for Ship Detection and Tracking in Satellite Imagery” By Jason Gottweis
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 fully-transparent-computer-vision-framework-ship-detection-tracking
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