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Detecting the extents of a color is a powerful image analysis technique, especially when the colors of an image have been simpli ed a bit (as this method, like floodFill() discussed earlier, has zero tolerance). For example, once you have determined that a solid-colored feature you would like to recognize is within some region of interest, you can nd the smallest bounding region that contains the color and determine its position from this (perhaps with temporal interpolation, taking its prior locations into account). To nd out the smallest region that encloses the solid-colored area of a bitmap, use the

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getColorBoundsRect() method. The method has some things in common with threshold()

(16.3)

Since Z (the new Q1) is not used in the calculation of any other Q i , no other X i must be modified. The bottom line here is that we now have deterministically satisfied the conditions on step 16, while maintaining all of the conditions on steps 0

in the way it compares pixels of a bitmap to a target color. It accepts the following parameters:

mask Causes pixel values to be masked prior to comparison. The comparison pixel & mask == color & mask is used. Particularly useful if you re only inter-

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through 15 that resulted from the single-step modifications. The multi-step modification considered here is the sirnplest case. Several other methods have been developed in an effort t o slightly reduce the work factor of Wang s attack. The evolution of Wang s attack seems t o have reached the point wherc the attack is so efficient (about two minutes for Stevens [144] implementation) and the difficulty and complexity of finding improved multi-step modifications is now so high and many of the more advanced modification techniques only hold probabilistically-that it appears likely that further improvement along these lines will be incremental, a t best.

ested in the alpha channel (use 0xFF000000), or not interested in the alpha channel (use 0x00FFFFFF). Use 0xFFFFFFFF to consider all channels.

color A color to detect the bounds of. findColor If true, returns the area containing the color; otherwise, returns the area in which the color is not found. Optional; defaults to true.

Stevens [144] gives the algorithm in Table 5.18 for finding a message block n/l, satisfying Wang s differential conditions. This attack is based on the set of output conditions given in Tables A-6 and A-7, which appear in the Appendix. Table 5.18: Efficient Algorithm t o Find h ( f, Find 1 1 = ( X ,X I , .. . , X I S )where all 1110 conditions refers to: 10 , all Table A-7 conditions, all IV conditions for M I (see Table A-8), both ( T 2 1 = 0)14 and ( T J = O),G ~ Find Al repeat Choose Q o ,Q 2 , Q 3 , . . . , Q15 satisfying conditions in Table A-6 Compute X o , X s ,X i , . . . , Xi5 repeat Choose Q ~ satisfying conditions G Compute X I using j = 16 Compute Q1 and X 2 , X : $X4, X s , Compute Q17, Q I ~ QN, Q ~ o , u n t i l QlH, Q 1 7 , . . . , Q 2 0 satisfy conditions in Table A-6 f o r ( Q 8 , Q9) consistent with X I1 Compute X x g , Xio, Xi2, Xi:$ X Compute Q21, Q 2 2 , . . . , Qss i f all A40 conditions arc satisfied then return M end i f next ( Q x , Q g ) u n t i l all 1110 conditions are satisfied end Find Strveris also presents ail efficient algorithm for the second block, All,

j

The method returns a Rectangle object enclosing the region that contains the color. In Example 36-13, left-aligned text is wrapped around an image by detecting, for each line, where that line overlaps the image, and then within that overlap, how much of the image is transparent (or solid white, in an opaque image). EXAMPLE 36-13

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