Abstract
A statistical signal processing approach to multisensor image fusion is presented. This approach is based on an image formation model in which the sensor images are described as the true scene corrupted by additive non-Gaussian distortion. A hidden Markov model (HMM) is fitted to the wavelet transforms of the sensor images to describe the correlations between the coefficients across wavelet decomposition scales. A set of iterative equations was developed using the expectation-maximization (EM) algorithm to estimate the model parameters and produce the fused images. We demonstrated the efficiency of this approach by applying this method to visual and radiometric images in concealed weapon detection (CWD) cases and night vision applications.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 4563-4567 |
| Number of pages | 5 |
| Journal | IEEE Vehicular Technology Conference |
| Volume | 60 |
| Issue number | 6 |
| State | Published - 2004 |
| Externally published | Yes |
| Event | 2004 IEEE 60th Vehicular Technology Conference, VTC2004-Fall: Wireless Technologies for Global Security - Los Angeles, CA, United States Duration: Sep 26 2004 → Sep 29 2004 |
Keywords
- Expectation-maximizaton (EM) algorithm
- Hidden Markov model (HMM)
- Image fusion
- Wavelet transform
ASJC Scopus subject areas
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics