void KLTSelectGoodFeatures(
KLT_TrackingContext tc,
KLT_PixelType *img,
int ncols,
int nrows,
KLT_FeatureList fl);
img. (NOTE: KLT_PixelType
is, unless modified by the user, an unsigned char.) If
tc->smoothBeforeSelecting is set to TRUE, then the
image is smoothed by
convolving with a Gaussian of sigma = tc->smooth_sigma_fact *
max(tc->window_width, tc->window_height); otherwise, the image
is not smoothed. In either case,
gradients are computed from the resulting image by convolving with the
derivative of a Gaussian of sigma = tc->grad_sigma. These
gradients (one in
the x direction and the other in the y direction) are used
to select the features.
Pixels throughout the image are then measured as to their "goodness", which
is a measure of their trackability.
Generally each pixel within the image's interior is considered, where the
interior is defined by tc->borderx and
tc->bordery (i.e., setting these
parameters to zero causes the interior to equal the whole image).
The parameter tc->nSkippedPixels can be used to speed up the process in
the following way: its default value is zero, in which case every pixel within
the interior is considered; if it is set to one,
then every other pixel within the interior is considered; setting
it to two causes every third pixel to be considered; and similarly for
higher values. Since neighboring
pixels generally have similar goodness values, then skipping every
other one will probably not noticeably decrease performance.
The goodness of each pixel is measured as the minimum eigenvalue of
the 2 by 2 gradient matrix computed from the tc->window_width by
tc->window_height window around the pixel. After all the pixels have been
considered, they are sorted in descending order according to goodness.
Then, one by one the top fl->nFeatures
features (or pixels) whose minimum eigenvalue is at least
tc->min_eigenvalue are selected, ensuring that each new feature is at
least tc->mindist pixels away from all the other features.
If tc->writeInternalImages is TRUE, then
the smoothed image and the image derivatives are written to
"kltimg_sgfrlf.pgm",
"kltimg_sgfrlf_gx.pgm", and
"kltimg_sgfrlf_gy.pgm", respectively.
This allows the user to more intelligently select the parameters
for smoothing and differentiating.