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    Area of Science:

    • Computer Vision
    • Spectroscopy
    • Image Processing

    Background:

    • Multispectral imaging captures data across numerous spectral bands.
    • Achieving illuminant-invariant representations is crucial for reliable analysis.
    • Existing methods face challenges with varying illumination conditions.

    Purpose of the Study:

    • To experimentally investigate multispectral constancy.
    • To evaluate its use in reconstructing surface reflectance.
    • To assess its performance with different multispectral camera types.

    Main Methods:

    • Utilizing a multispectral camera as a spectrophotometer.
    • Capturing images under three varying illumination conditions.
    • Applying diagonal transform and spectral adaptation transform for canonical representation.

    Main Results:

    • Multispectral constancy proved beneficial for both filter-wheel and snapshot cameras.
    • The method demonstrated robustness against illuminant estimation errors.
    • Effective performance was observed with linear spectral reconstruction.

    Conclusions:

    • Multispectral constancy offers a viable approach for illuminant-invariant analysis.
    • The proposed method enhances surface reflectance reconstruction accuracy.
    • This research advances the application of multispectral imaging in computer vision.