Abstract
Recognizing a target in synthetic-aperture radar (SAR) images is an important, yet challenging, application of the model-based vision technique. This paper describes a model-based SAR recognition system based on invariant histograms and deformable template matching techniques. An invariant histogram is a histogram of invariant values defined by geometric features such as points and lines in SAR images. Although a few invariants are sufficient to recognize a target, we use a histogram of all invariant values given by all possible target feature pairs. This redundant histogram enables robust recognition under severe occlusions typical in SAR recognition scenarios. Multi-step deformable template matching examines the existence of an object by superimposing templates over potential energy field generated from images or primitive features. It determines the template configuration which has the minimum deformation and the best alignment of the template with features. The deformability of the template absorbs the instability of SAR features. We have implemented the system and evaluated the system performance using hybrid SAR images, generated from synthesized model signatures and real SAR background signatures.
| Original language | English |
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| Pages | 100-105 |
| Number of pages | 6 |
| Publication status | Published - 1996 |
| Externally published | Yes |
| Event | Proceedings of the 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - San Francisco, CA, USA Duration: Jun 18 1996 → Jun 20 1996 |
Other
| Other | Proceedings of the 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| City | San Francisco, CA, USA |
| Period | 6/18/96 → 6/20/96 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Software
- Control and Systems Engineering
- Electrical and Electronic Engineering