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Classification of Images for Automatic Colour Correction

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The ways in which image classification can be utilised in automatic colour correction are discussed. Before images can be classified, a restricted set of numeric features must be extracted from image data. Many of these features can be defined on the basis of the statistical distribution of the colour values of pixels. In some cases, however, spatial image properties are also needed. In automatic colour correction, image classification guides the selections made within and between different correction elements including the adjustment of primary colour components, adjustment of tone rendering in Lsa colour space, grey balance adjustment and skin colour correction.
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Document Type: Research Article

Publication date: January 1, 1995

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  • CIC is the premier annual technical gathering for scientists, technologists, and engineers working in the areas of color science and systems, and their application to color imaging. Participants represent disciplines ranging from psychophysics, optical physics, image processing, color science to graphic arts, systems engineering, and hardware and software development. While a broad mix of professional interests is the hallmark of these conferences, the focus is color. CICs traditionally offer two days of short courses followed by three days of technical sessions that include three keynotes, an evening lecture, a vibrant interactive (poster) papers session, and workshops. An endearing symbol of the meeting is the Cactus Award, given each year to the author(s) of the best interactive paper; there are also Best Paper and Best Student Paper awards.

    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 paper for details.

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