"... In PAGE 3: ... The results are shown for three marking technologies, color electrophotographic, ink jet and photographic. Summary of Characterization Results Table1 (at the end of this report) summarizes the characterization results from three marking technologies for the two profiling products. The same scanner and characterization data was used for evaluating both GretagMacbeth and Monaco characterization programs.... In PAGE 3: ... Sharma(6) described similar results. Table1 also shows the results of a cross rendering study where the ICC profile from the photographic media was used to convert RGB values to LAB values for other media. The results for individual technologies and cross rendering will be described in the next sections.... In PAGE 3: ... Figure 5 illustrates the coverage using the CMYK target of Figure 2. The Delta E averages (see Table1 ) were slightly larger than for other technology because of slightly unsmooth paper media (Hammermill Copy Print Photo Bright 96), slight non- uniformities in the patches due to marking and slight dithering effects from multi-bit halftoning. 0 2 4 6 0 5 10 15 20 25 CIELAB DELTA E Frequency Color Laser RMS Delta E.... In PAGE 4: ...7/2 target was measured and scanned as described above (the provided reference data was not used). As indicated in Table1 , the average error for all 288 patches of this target was very small. It was this ICC profile that was used in the cross media study described below.... ..."

"... In PAGE 11: ... The difference in color between these two processing conditions is then measured in dE* units using the CIE L*a*b* color scale. Table7 shows the process color change in dE* units mapped between the grey and yellow color subclasses. Visually, a larger change in color due to processing conditions can be tolerated in a light yellow color than a grey color as shown by this mapping based on historical data.... In PAGE 23: ... The difference in color between these two processing conditions is then measured in dE* units using the CIE L*a*b* color scale. Table7 shows the process color change in dE* units mapped between the grey and yellow color subclasses. Visually, a larger change in color due to processing conditions can be tolerated in a light yellow color than a grey color as shown by this mapping based on historical data.... In PAGE 23: ... Having a function that can accuratly evaluate the effect of an adaptation is the key to performing the correct adaptation. Table7 : Fuzzy Measure for Color Shift Fuzzy Rating Grey dE* Yellow dE* Excellent 0.05 0.... ..."

"... In PAGE 11: ... The difference in color between these two processing conditions is then measured in dE* units using the CIE L*a*b* color scale. Table7 shows the process color change in dE* units mapped between the grey and yellow color subclasses. Visually, a larger change in color due to processing conditions can be tolerated in a light yellow color than a grey color as shown by this mapping based on historical data.... In PAGE 11: ... Having a function that can accuratly evaluate the effect of an adaptation is the key to performing the correct adaptation. Table7 : Fuzzy Measure for Color Shift Fuzzy Rating Grey dE* Yellow dE* Excellent 0.05 0.... In PAGE 23: ... The difference in color between these two processing conditions is then measured in dE* units using the CIE L*a*b* color scale. Table7 shows the process color change in dE* units mapped between the grey and yellow color subclasses. Visually, a larger change in color due to processing conditions can be tolerated in a light yellow color than a grey color as shown by this mapping based on historical data.... ..."

"... In PAGE 92: ...07/24/00 Evaluation of Bispectral Spectrophotometry for Accurate Colorimetry of Printing Materials -IX- Table47 .... ..."

"... In PAGE 3: ... Instead, the proposed M-RSST approach yields much faster execution time, while simultaneously keeping the same performance. Comparison of the computational load of the RSST and M-RSST at di!erent image sizes is depicted in Table1 , using a C implementation on a Sun Ultra 10 (333 MHz) workstation. Another bene quot;t of the M-RSST is that it eliminates regions of small segments, which, in general, are not preferable in the context of con- tent-based indexing and retrieval.... In PAGE 14: ... How- ever, the execution times remain very small, just few ms, even for large partition numbers (Q quot;5). A comparison of the execution times for fuzzy rep- resentation (in the case of Q quot;3 and triangular membership functions) with the required times for color segmentation is presented in Table1 at di!er- ent image sizes. It seems that the required time for segmentation is much greater compared to the time of fuzzy representation especially in cases of images of large size.... In PAGE 17: ... The computational cost for binary classi quot;cation is very small and the total cost is mainly a!ected by the segmentation load, resulting in a similar cost to the fuzzy approach (Tables 1 and 4). Instead, the color histogram method de- mands smaller computational load compared to segmentation as Table1 shows. However, the addi- tional cost of the proposed method is justi quot;ed by its better performance.... ..."

"... In PAGE 6: ... The weightings are varied depending on the applications used. Table3 shows the second set of results when the values in Table 2 are assigned to the network. In Case (a), even though the signal strength for UMTS is lower than that of ... ..."

"... In PAGE 2: ... More details on target-based decision models as well as their potential applications and advantages could be referred to Abbas and Matheson (2005, 2004), Bordley (2002), Bordley and Kirkwood (2004), Castagnoli and LiCalzi (2006) and LiCalzi (1999). In this paper,1 we consider the problem of decision making in the face of uncertainty that can be most effectively described using the decision matrix shown in Table1 ; see, e.g.... In PAGE 4: ... 6. 2 Target-based model of the expected value Let us consider the decision problem as described in Table1 with assuming a proba- bility distribution PS over S. Here, we restrict ourselves to a bounded domain of the payoff variable that D =[cmin, cmax], i.... In PAGE 6: ... 3.1 State-independent targets Let us turn back to the problem of decision making in the face of uncertainty shown in Table1 . We now discuss this problem using fuzzy targets.... In PAGE 17: ...1 A target-based procedure for fuzzy decision making We now consider the problem of decision making under uncertainty where payoffs may be given imprecisely. Let us turn back to the general decision matrix shown in Table1 , where cij can be a crisp number, an interval value or a fuzzy quantity. Clearly in this case we have an inhomogeneous decision matrix and traditional methods can not be applied directly.... In PAGE 23: ... 8. Then we obtain the result corresponding to this target as shown in Table1 3, which yields the ranking order as A1 follows A4 follows A2 follows A3. In other words, with a more pessimistic attitude the DM is enough risk averse to refuse alternative A2 and select A1 for surely getting best profit.... ..."

"... In PAGE 19: ... The KFC-L showed the best performance for the image data set, but for the ringnorm data set, the KFC-L showed a large error compared to other methods. Table4 shows the best recognition rates of the ringnorm validation data sets. The recognition rates of the KFC-L are very low com- pared to those of the KFC.... In PAGE 20: ...Table4 . Cross validation results (%) for the ringnorm data set.... In PAGE 29: ... The Precision and Recall metrics computed by the J4.8 algorithm over the same testing set are also shown in Table4 . It can be seen that the fuzzy query results over the testing set correspond roughly to the ones computed from the training set.... In PAGE 48: ...ig. 7. Comparison between heuristic rule extraction and genetics- bases multiobjective rule selection with respect to the average error rates on training patterns (Wine). Wisconsin Breast Cancer Data: Experimental results by heuristic rule extraction are summarized in Table4 where the average error rate on test patterns over three iterations of the 10CV procedure is shown for each combination of a heuristic rule extraction criterion and the number of extracted rules for each class. The best error rate in each row is indicated by bold face.... In PAGE 48: ...5 algorithm with respect to the generalization ability. Table4 . Average error rates on test patterns of extracted rules by heuristic rule extraction (Breast W).... In PAGE 86: ...007 isdn .009 Table4 . Continuation of onramp/offramp traf- fic distribution for Figure 5.... ..."

"... In PAGE 91: ...able 6. Fujix Pictography thermal printer sample....................................................................................... 36 Table7 .... ..."