@inproceedings{2bb5e99fd5184c1d8e9e29379cbb8a02,
title = "Knowledge-based multi-spectral pixel level fusion for surveillance",
abstract = "Multi- Spectral Fusion focused on the task of image enhancement by processing raw data collected at various electromagnetic bands via passive sensors. Passive spectral sensors collect information about the scene based on the inherently reflected or emitted energy of the scene and is represented by spectral distributions and intensities. These systems can be designed so that they provide collocated information through common optics or other means (algorithms). This paper addresses the issue of Multi-Spectral Pixel-Level Fusion to enhance the visual quality of the {"}combined{"} (fused) image data as compared to that of each single spectral band image data. We propose a Knowledgebased Fusion methodology. We also make the implicit assumption that visually superior image data are also better image data for processing by most types of algorithms",
author = "Ma Yunqian and Jiri Rojicek and Zdenek Beran and Jaromir Kukal and Mike Bazakos",
year = "2006",
language = "English (US)",
isbn = "0780394909",
series = "IEEE International Conference on Neural Networks - Conference Proceedings",
pages = "4435--4440",
booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",
note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",
}