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Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal
  DOI :  10.2352/issn.2694-118X.2020.LIM-06  Published OnlineSeptember 2020
Abstract

Removing the effect of illumination variation in images has been proved to be beneficial in many computer vision applications such as object recognition and semantic segmentation. Although generating illumination-invariant images has been studied in the literature before, it has not been investigated on real 4-channel (4D) data. In this study, we examine the quality of illumination-invariant images generated from red, green, blue, and near-infrared (RGBN) data. Our experiments show that the near-infrared channel substantively contributes toward removing illumination. As shown in our numerical and visual results, the illumination-invariant image obtained by RGBN data is superior compared to that obtained by RGB alone.

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Sorour Mohajerani, Mark S. Drew, Parvaneh Saeedi, "Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removalin Proc. IS&T London Imaging Meeting 2020: Future Colour Imaging,  2020,  pp 82 - 86,  https://doi.org/10.2352/issn.2694-118X.2020.LIM-06

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Copyright © Society for Imaging Science and Technology 2020
75011771
London Imaging Meeting
2694-118X
2694-118x
Society for Imaging Science and Technology