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Finding representatives in a large dataset of spectral reflectances

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We propose a new method to construct representative spectra from a large database of spectral reflectances. The key is the optimisation of a Support Vector type functional. The representatives are constructed such that they sit at positions of high density in the set of spectra. At the same time they are constructed to be as orthogonal as possible. The representatives are expressible as a linear combination of data samples with positive coefficients. Therefore, they are positive and physically realisable. We show the differences of these representatives to representatives found with well-known methods like principal component analysis and k–means clustering.
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Document Type: Research Article

Publication date: January 1, 2004

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  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual papers for details.

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