Usability of Digital Cameras for Verifying Physically Based ...

(a) (b)
Figure 30: (a) ColorCheckerSG after correcting for distortion; (b) the subareas of
the patches that were used for averaging (blue squares).
digital photography and can be solved easily. Software for identifying such pixels
is available as freeware (e.g. Dead/Hot Pixel Test [dea06]). Pixels that are labeled
as dead or hot pixels are excluded from further calculations. With the third case
we have to deal separately, though. We analyzed the deviation of pixel values of
several patches, in order to find a method for picking the most representative pixel
value. As we cannot pick only one pixel of a patch, we have to take a region of
pixels into account. The areas we chose for selecting a representative pixel value
can be seen in figure 30 (b). The size of the area depends on the size of the image.
For the image seen in figure 30 each area covers 990 (33x30) pixel. We excluded
the border of each patch to avoid inaccuracies due to noise and interpolation at the
edges.
Figure 31 shows histograms of exemplary distributions of pixel values of one
channel of a patch. In the first histogram (see figure 31(a)) the values are hardly
effected by noise and it is therefore obvious that 229 is a representative value
for this kind of distribution. The median (229) and the mean value (228.95) are
very close in this case. For the distribution seen in figure 31(b) the representative
value is more ambiguous. A considerable amount of pixels has the value 216,
which indicates that the value we are searching for lies in between 215 and 216.
The median of 215 and the mean value of 215.28 differ noticeably. Here, the
mean value seems to better represent the relevance of the particular pixel values.
Figure 31(c) shows a distribution were this is not the case, though. We can see
that there is a huge amount of noise present and that there are even some severe
outliers. These outliers were caused by a light dust particle on a dark patch. The
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