Abstract
Under extreme light conditions, a conventional colour CCD camera would fail to render the colours of an object properly as the visible spectrum is either faintly observable in the scene or the presence of glare corrupts the colours sensed. On the other hand, for darkly-illuminated areas, a near-infrared (NIR) camera would sense stronger more discriminable signals, but could only render the scene monochromatically. The underlying challenge in this research is how to adaptively integrate a monochromatic NIR image with a faintly rendered colour image of the same darkly or very brightly lit scene to give rise to improved colour classification results that discriminate colours more effectively. This research proposes a Fuzzy-Genetic colour processing algorithm that adaptively marries together the visible and near-infrared spectra signals for the purpose of colour object recognition. The experiments were done on a scene with spatially varying illumination intensities, using Fujifilm’s UV/IR Super CCD camera with a sensitivity range between 380nm to 1000nm in conjunction with NIR filters. Results prove that the proposed multi-spectrum technique yields better colour classification results than utilizing the pure visible spectrum alone.
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Shin, H., Reyes, N.H., Barczak, A.L., Chan, C.S. (2010). Colour Object Classification Using the Fusion of Visible and Near-Infrared Spectra. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_46
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DOI: https://doi.org/10.1007/978-3-642-15246-7_46
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