Abstract
This paper presents an empirical evaluation of a number of recently developed Automatic Target Recognition Algorithms for Forward-Looking InfraRed(FLIR) imagery using a large database of real second-generation FLIR images. The algorithms evaluated are based on convolution neural networks (CNN), principal component analysis (PCA), linear discriminant analysis (LDA), learning vector quantization (LVQ), and modular neural networks (MNN). Two model-based algorithms, using Hausdorff metric based matching and geometric hashing, are also evaluated. A hierarchical pose estimation system using CNN plus either PCA or LDA, developed by the authors, is also evaluated using the same data set.
Original language | English (US) |
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Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
Editors | F.A. Sadjadi |
Pages | 388-397 |
Number of pages | 10 |
Volume | 3371 |
DOIs | |
State | Published - 1998 |
Externally published | Yes |
Event | Automatic Target Recognition VIII - Orlando, FL, United States Duration: Apr 13 1998 → Apr 17 1998 |
Other
Other | Automatic Target Recognition VIII |
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Country/Territory | United States |
City | Orlando, FL |
Period | 4/13/98 → 4/17/98 |
Keywords
- Automatic target recognition
- Performance evaluation
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics