Share Email Print

Proceedings Paper

Experimental evaluation of neural, statistical, and model-based approaches to FLIR ATR
Author(s): Baoxin Li; Qinfen Zheng; Sandor Z. Der; Rama Chellappa; Nasser M. Nasrabadi; Lipchen Alex Chan; LinCheng Wang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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 hierarchial pose estimation system using CNN plus either PCA or LDA, developed by the authors, is also evaluated using the same data set.

Paper Details

Date Published: 18 September 1998
PDF: 10 pages
Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998); doi: 10.1117/12.323856
Show Author Affiliations
Baoxin Li, Univ. of Maryland/College Park (United States)
Qinfen Zheng, Univ. of Maryland/College Park (United States)
Sandor Z. Der, Army Research Lab. (United States)
Rama Chellappa, Univ. of Maryland/College Park (United States)
Nasser M. Nasrabadi, Army Research Lab. (United States)
Lipchen Alex Chan, SUNY/Buffalo (United States)
LinCheng Wang, SUNY/Buffalo (United States)

Published in SPIE Proceedings Vol. 3371:
Automatic Target Recognition VIII
Firooz A. Sadjadi, Editor(s)

© SPIE. Terms of Use
Back to Top