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Computational Color Imaging: Third International Workshop, by Erik Reinhard, Tania Pouli (auth.), Raimondo Schettini,

By Erik Reinhard, Tania Pouli (auth.), Raimondo Schettini, Shoji Tominaga, Alain Trémeau (eds.)

This e-book constitutes the refereed court cases of the 3rd Computational colour Imaging Workshop, CCIW 2010, held in Milan, Italy, in April 2010. The sixteen revised complete papers, awarded including 3 invited papers, have been rigorously reviewed and chosen from quite a few submissions. The papers are geared up in topical sections on computational images, colour and belief, colour imaging, and computational imaging.

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Extra info for Computational Color Imaging: Third International Workshop, CCIW 2011, Milan, Italy, April 20-21, 2011. Proceedings

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Since then, several studies have confirmed and extended their results [20,21]. A computational model of colour naming can be very useful for several tasks such as segmentation, retrieval, tracking, or human-machine interaction. Although some models based on a pure tessellation of a colour space have been proposed [22,23], the most accepted framework has been to consider colour naming as a fuzzy process, that is, any colour stimulus has a membership value between 0 and 1 to each colour category. Kay and McDaniel [24] were the first in proposing a theoretical fuzzy model for colour naming.

3 Colour Induction Colour induction refers to the perceptual change in the colour of a stimulus due to the interactions with its surrounding region. When the perceived colour of a stimulus shifts towards the colour of its surround it is termed ”assimilation”. Conversely, contrast occurs when the perceived colour of the stimulus diverges from that of its surroundings. These two well-known effects are illustrated in Figure 1, which also shows the dependency of the effects on the local spatial frequency of the stimulus surround.

The M halftone colorant is complementary to the G channel, but the G channel scan clearly displays within it not only the halftone structure of the desired M separation, but also undesired halftone structures for the C, Y, and K separations with varying intensities that arise from the absorption of these colorants in the G scanner channel band. To develop an effective solution to estimate C, M, Y, and K, halftones from the R, G, and B scans, we begin with the physical insight into the halftone process provided by the mathematical tool of Fourier analysis: the individual colorant halftone images are, phase modulation notwithstanding, narrow-band bandpass images comprised of components located on the fundamental halftone periodicity frequencies and at integer multiples thereof.

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