Experimental evaluation of neural, statistical, and model-based approaches to FLIR ATR

Baoxin Li; Qinfen Zheng; Sandor Z. Der; Rama Chellappa; Nasser M. Nasrabadi; Lipchen Alex Chan; LinCheng Wang

doi:10.1117/12.323856

18 September 1998 Experimental evaluation of neural, statistical, and model-based approaches to FLIR ATR

Baoxin Li, Qinfen Zheng, Sandor Z. Der, Rama Chellappa, Nasser M. Nasrabadi, Lipchen Alex Chan, LinCheng Wang

Proceedings Volume 3371, Automatic Target Recognition VIII; (1998) https://doi.org/10.1117/12.323856
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Baoxin Li, Qinfen Zheng, Sandor Z. Der, Rama Chellappa, Nasser M. Nasrabadi, Lipchen Alex Chan, and LinCheng Wang "Experimental evaluation of neural, statistical, and model-based approaches to FLIR ATR", Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998); https://doi.org/10.1117/12.323856

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