Publications by authors named "Mika Flinkman"

Hyperspectral imaging has become a common technique in many different applications, enabling accurate identification of materials based on their optical properties; however, it requires complex and expensive technical implementation. A less expensive way to produce spectral data, spectral estimation, suffers from complex mathematics and limited accuracy. We introduce a novel, to the best of our knowledge, method where spectral reflectance curves can be reconstructed from the measured camera responses without complex mathematics.

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In this research, three illuminants that improve color discrimination ability of people with red-green color vision deficiency were developed. The illuminants are close to daylight-colored and were produced by using spectral optimization. Deutans were the focus of this research, but a few protans were also tested for reference.

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Article Synopsis
  • PCA and weighted PCA were used on color spectra related to the object's surface and MacAdam limits, analyzing a circulant correlation matrix.
  • The analysis revealed that the largest eigenvalue is distinct with a constant eigenvector, while the other eigenvalues are double, expressed with trigonometric functions, which can reconstruct smooth reflectance spectra.
  • By applying a specific weight function, the main color information is condensed into three components that relate to one achromatic and two chromatic response functions, similar to Munsell opponent-hue directions.
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In this study, we have analyzed statistical properties of the values of the first- and second-order derivatives of spectral reflectance curves. We show that values of all four tested spectral data sets have very similar statistical properties. We set outer limits that bound the clear majority of the values of the first- and second-order derivatives.

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