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Erschienen in: International Journal of Computer Vision 2/2014

01.11.2014

Acquisition of High Spatial and Spectral Resolution Video with a Hybrid Camera System

verfasst von: Chenguang Ma, Xun Cao, Xin Tong, Qionghai Dai, Stephen Lin

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2014

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Abstract

We present a hybrid camera system for capturing video at high spatial and spectral resolutions. Composed of an red, green, and blue (RGB) video camera, a grayscale video camera and a few optical elements, the hybrid camera system simultaneously records two video streams: an RGB video with high spatial resolution, and a multispectral (MS) video with low spatial resolution. After registration of the two video streams, our system propagates the MS information into the RGB video to produce a video with both high spectral and spatial resolution. This propagation between videos is guided by color similarity of pixels in the spectral domain, proximity in the spatial domain, and the consistent color of each scene point in the temporal domain. The propagation algorithm, based on trilateral filtering, is designed to rapidly generate output video from the captured data at frame rates fast enough for real-time video analysis tasks such as tracking and surveillance. We evaluate the proposed system using both simulations with ground truth data and on real-world scenes. The accuracy of spectral capture is examined through comparisons with ground truth and with a commercial spectrometer. The utility of this high resolution MS video data is demonstrated on the applications of dynamic white balance adjustment, object tracking, and separating the appearance contributions of different illumination sources. The various high resolution MS video datasets that we captured will be made publicly available to facilitate research on dynamic spectral data analysis.

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Metadaten
Titel
Acquisition of High Spatial and Spectral Resolution Video with a Hybrid Camera System
verfasst von
Chenguang Ma
Xun Cao
Xin Tong
Qionghai Dai
Stephen Lin
Publikationsdatum
01.11.2014
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 2/2014
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-013-0690-4

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