An Active Vision Based SAR-FLIR Fusion ATR System for Detection and Recognition of Ground Targets
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Summary
A constant False Alarm Rate (CFAR) has been developed and implemented for real time performance on an inexpensive Single Instruction Multiple Data (SIMD) hardware and an model-based recognition scheme using a model-image alignment approach has been implemented.
- Type
- article
- Published
- 1996-11-30
- Cited by
- 1
- References
- 46
- OpenAlex
- https://openalex.org/W10672625
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53861644
Keywords
Computer science, Constant false alarm rate, Artificial intelligence, Computer vision, Automatic target recognition
References
- USC image understanding research: 1988–89
- Non-Monotonic Decision Rules for Sensor Fusion
- Study of Digital Matching of Dissimilar Images.
- Digital image warping
- Automated multisensor registration - Requirements and techniques
- A Structural Analysis of Complex Aerial Photographs
- Multiresolution scene segmentation using MLPs
- Hybrid systems-a key to intelligent pattern recognition
- Image understanding research at SRI International
- A survey of image registration techniques
- Robust Multi-Sensor Fusion: A Decision-Theoretic Approach
- Detecting runways in complex airport scenes
- Self-Organization and Associative Memory, Third Edition
- Separating Figure from Ground with a Parallel Network
- Fusion of monocular cues to detect man-made structures in aerial imagery
- Multi sensor data fusion within hierarchical neural networks
- Model-based recognition in robot vision
- Adaptive 3-D Object Recognition from Multiple Views
- A biologically motivated algorithm for image interpretation based on multi-pass multi-resolution techniques
- Automatic Target Recognition: State of the Art Survey
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