A fundamental automatic target recognition (ATR) system can be composed of an object segmentation stage, followed by feature extraction from those objects produced by segmentation, and finally classification of these object features. The capability of such a system in terms of classification success is therefore limited not only by the quality of the feature extraction and classification methods used, but also by the quality of the initial object segmentation. In this thesis, a novel architecture is described which uses two stages of segmentation. This allows image features derived after a primary segmentation stage to influence the parameters of a secondary segmentation stage which is applied to the same image area. This is aimed at allowing improved, and locally optimised, segmentation of those objects which were poorly segmented by the primary segmentation stage. To enable the implementation of the system, a probability density estimate function is used as a method of detecting novelty in objects presented for classification. This is found to be a non-ideal solution, although useful in the context of the application concerned. The development of all the system components, and ultimately the full ATR system, is described with experimental results derived from real-world infrared imagery. From this work, conclusions are drawn as to the usefulness of a such a two-stage segmentation architecture; specifically, the clutter rejection flexibility and the potential ability for the system to locally optimise segmentation on a per object basis are highlighted.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:660980 |
| Date | January 1999 |
| Creators | Reavy, Richard Wilson |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/12840 |
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