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N Serves as the interface between the hardware (attached devices) and the
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1.2.7 8: Clustering
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The remaining coverage of filters assumes that you re not applying them to Smart Objects. This is for two reasons simplicity in describing their use and displaying the Photoshop interface as we discuss the filters themselves, and because you may not always apply a filter as a Smart Filter and may simply apply it to a selection or layer with no conversion of that layer to a Smart Object. Of course, all the filters we re about to demonstrate in this chapter and the subsequent two chapters on filters can be applied as Smart Filters, with the exception of Extract, Liquify, Pattern Maker, and Vanishing Point, which, as we ve discussed, are dimmed in the Filter menu when a layer s been converted to a Smart Object. When you choose a command from the Filter menu, Photoshop applies the filter to the selected portion of the image on the current layer. If no portion of the image is selected, Photoshop applies the filter to the entire layer. Therefore, if you want to filter every nook and cranny of the current layer, press Ctrl+D ( +D on the Mac) to cancel any existing selection outline and then choose the desired command.
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In the analysis of the GNQNs discussed so far, we have used intermediate quantities such as normalising constants (in the case of the convolution algorithm) or steady-state probabilities (in the case of a direct computation at the CTMC level) to compute user-oriented measures of interest such as average response times or utilisations at the nodes of the QN. This is illustrated in Figure 11.3. Although this approach as such is correct, there may be instances in which it is inefficient or computationally unattractive. This happens when one is really only interested in average performance measures. Then we do not need the steady-state probabilities nor the normalising constants, but resort to an approach known as mean-value analysis (MVA) w h ic h was developed by Reiser and Lavenberg in the late 1970s. With MVA, the average performance measures of interest are directly calculated at the level of the QN without resorting to an underlying CTMC and without using normalising constants. The advantage of such an approach is that the quantities that are employed in the computations have a physical interpretation in terms of the GNQN and hence the computations can be better understood and verified for correctness. Note that in 4 we have already encountered a special case of the MVA approach in the discussion of the terminal model. Here we will generalise that approach.
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All these mode-changing techniques are really doing is changing the visibility of the image and mask channels. If you view the Channels palette (Window Channels) while using these techniques, you can see the eye icons change as you flip between the modes.
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9.5.3 Negotiating with your constituency
The Quick Mask dialog box allows you to change the color and opacity of the mask or to invert it.
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