Automatic object detection is a challenging field which has been evolving over decades. The application areas span many domains such as robotics inspection, medical imaging, military targeting, and reconnaissance. Some of the most concentrated efforts in automatic object detection have been in the military domain, where most of the problems deal with automatic target detection and scene analysis in the outdoors using a variety of sensors.
One of the critical problems in Automatic Target Detection (ATD) systems is multiscenario adaptation. Most of the ATD systems developed until today perform unpredictably i.e. perform well in certain scenarios, and poorly in others. Unless
ATD systems can be made adaptable, their utility in battlefield missions remains questionable.
This thesis describes a methodology that adapts parameterized ATD systems with image metrics as the scenario changes so that ATD system can maintain better
performance. The methodology uses experimentally obtained performance models, which are functions of image metrics and system parameters, to optimize performance measures of the ATD system. Optimization is achieved by adapting system parameters with incoming image metrics based on performance models as the system works in field. A simple ATD system is also proposed in this work to describe and test the methodology.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12604966/index.pdf |
| Date | 01 June 2004 |
| Creators | Kurekli, Kenan |
| Contributors | Akar, Gozde Bozdagi |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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