Knowledge-based multi-spectral pixel level fusion for surveillance

Ma Yunqian, Jiri Rojicek, Zdenek Beran, Jaromir Kukal, Mike Bazakos

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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

Original languageEnglish (US)
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Number of pages6
StatePublished - 2006
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576


OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
CityVancouver, BC


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