Abstract
In this paper we present a novel algorithm for fusion of multimodal surveillance images, based on ICA, which has an improved performance over sensor networks. Improvements have been demonstrated through separate training process for different modalities and the use of a fusion metric to maximise the quality of the fused image. Sparse coding of the coefficients in ICA domain is used to minimize noise transferred from input images into the fused output. Experimental results confirm that the proposed method outperforms other state-of-the-art methods in the sensor network environment, characterized by JPEG 2000 compression and data packetization.
| Translated title of the contribution | Improving fusion of surveillance images in sensor networks using independent component analysis |
|---|---|
| Original language | English |
| Pages (from-to) | 1029 - 1035 |
| Number of pages | 7 |
| Journal | IEEE Transactions on Consumer Electronics |
| Volume | 53 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Aug 2007 |
Bibliographical note
Publisher: Institute of Electrical and Electronics EngineersRose publication type: Journal article
Sponsorship: This work has been funded by the UK Data and Information Fusion Defence Technology Centre (DIF DTC).
Terms of use: Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Consumer Electronics.
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Keywords
- image fusion
- fusion metrics
- sensor networks
- JPEG 2000
- component analysis