In this article, we are combining minimization criteria in the colorant separation process for spectral color reproduction. The colorant separation is performed by inverting a spectral printer model: the spectral Yule-Nielsen modified Neugebauer model. The inversion of the spectral printer model is an optimization operation in which a criterion is minimized an each iteration. The approach we proposed minimizes a criterion defined by the weighted sum of a spectral difference and a perceptual color difference. The weights can be tuned with a parameter alpha epsilon [0, 1]. Our goal is to decrease the spectral difference between the original data and its reproduction and also to consider perceptual color difference under different illuminant conditions. In order to find the best alpha value, we initially compare a pure colorimetric criterion and a pure spectral criterion for the reproduction, then we combine them. We perform four colorant separations: the first separation will minimize the 1976 CIELAB color difference where four illuminants are tested, the second separation will minimize an equally weighted summation of 1976 CIELAB color difference with the four illuminants tested independently, the third colorant separation will minimize a spectral difference, and the fourth colorant separation will combine a weighted sum of a spectral difference and one of the two first colorimetric differences previously introduced. This last colorant separation can be tuned with a parameter in order to emphasize on spectral or colorimetric difference. We use a six colorants printer with artificial inks for our experiments. The prints are simulated by the spectral Yule-Nielsen modified Neugebauer model. Two groups of data are used for our experiments. The first group describes the data printed by our printing system, which is represented by a regular grid in colorant space of the printer and the second group describes the data which is not originally produced by our printing system but mapped to the spectral printer gamut. The Esser test chart and Macbeth Color Checker test chart have been selected for the second group. Spectral gamut mapping of this data is carried out before performing colorant separation. Our results show improvement for the colorant separations combining a sum of 1976 CIELAB color difference for a set of illuminants and for the colorant separation combining a sum of 1976 CIELAB color difference and spectral difference, especially in the case of spectral data originally produced by the printer

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We report here our results in a multi-sensor setup reproducing the conditions of an automated focused ultrasound surgery environment. The aim is to continuously predict the position of an internal organ (here the liver) under guided and non-guided free breathing, with the accuracy required by surgery. We have performed experiments with 16 healthy human subjects, two of those taking part in full-scale experiments involving a 3 Tesla MRI machine recording a volume containing the liver. For the other 14 subjects we have used the optical tracker as a surrogate target. All subjects where volunteers who agreed to participate in the experiments after being thoroughly informed about it. For the MRI sessions we have analyzed semi-automatically offline the images in order to obtain the ground truth, the true position of the selected feature of the liver. The results we have obtained with continuously updated random forest models are very promising, we have obtained good prediction-target correlation coefficients for the surrogate targets (0.71 ± 0.1) and excellent for the real targets in the MRI experiments (over 0.91), despite being limited to a lower model update frequency, once every 6.16 seconds

In the context of spectral color image reproduction by multi-channel inkjet printing a key challenge is to accurately model the colorimetric and spectral behavior of the printer. A common approach for this modeling is to assume that the resulting spectral reflectance of a certain ink combination can be modeled as a convex combination of the so-called Neugebauer Primaries (NPs); this is known as the Neugebauer Model. Several extensions of this model exist, such as the Yule-Nielsen Modified Spectral Neugebauer (YNSN) model. However, as the number of primaries increases, the number of NPs increases exponentially; this poses a practical problem for multi-channel spectral reproduction.In this work, the well known Kubelka-Munk theory is used to estimate the spectral reflectances of the Neugebauer Primaries instead of printing and measuring them, and subsequently we use these estimated NPs as the basis of our printer modeling. We have evaluated this approach experimentally on several different paper types and on the HP Deskjet 1220C CMYK inkjet printer and the Xerox Phaser 7760 CMYK laser printer, using both the conventional spectral Neugebauer model and the YNSN model. We have also investigated a hybrid model with mixed NPs, half measured and half estimated.Using this approach we find that we achieve not only cheap and less time consuming model establishment, but also, somewhat unexpectedly, improved model precision over the models using the real measurements of the NPs.

We propose a technique for adjusting displays to compensate for deficiencies in viewers' color perception. The method proposed finds graphical elements of the image and recolors them using a color palette designed specifically for colorblind viewers. We present results of user studies demonstrating the advantages of this approach.