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Adaptive spatio-colorimetric classification

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Unlike most color classification methods, which consist in partitioning the image according to the pixels color attributes exclusively, spatio-colorimetric techniques bring some spatial information directly among the data to classify. However, they usually involve some heavy data structures and a large amount of trichromatic data.

To answer this issue, this article proposes a color spatio-classification method performing two successive stages. First of all, the number of colors is lowered through an analysis of the connectedness degrees on the three marginal components independently. Since the number of colors is significantly reduced, it becomes reasonable, in a complexity point of view, to analyze the vectorial connectedness degrees of the trichromatic intervals. Several experimental results will be shown on different images and the method parameters will be discussed.
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Document Type: Research Article

Publication date: January 1, 2008

More about this publication?
  • 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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