* green/mln/clustering/kmean3d.hh: Fix mistakes in documentation. --- trunk/milena/sandbox/ChangeLog | 6 +++ .../milena/sandbox/green/mln/clustering/kmean3d.hh | 33 +++++++++----------- 2 files changed, 21 insertions(+), 18 deletions(-) diff --git a/trunk/milena/sandbox/ChangeLog b/trunk/milena/sandbox/ChangeLog index eac8803..ea973fa 100644 --- a/trunk/milena/sandbox/ChangeLog +++ b/trunk/milena/sandbox/ChangeLog @@ -1,5 +1,11 @@ 2009-10-15 Yann Jacquelet <jacquelet@lrde.epita.fr> + Correct kmean3d documentation. + + * green/mln/clustering/kmean3d.hh: Fix mistakes in documentation. + +2009-10-15 Yann Jacquelet <jacquelet@lrde.epita.fr> + Make Théo projection for histogram available. * green/mln/display: New directory. diff --git a/trunk/milena/sandbox/green/mln/clustering/kmean3d.hh b/trunk/milena/sandbox/green/mln/clustering/kmean3d.hh index 6aebefc..fb1a8df 100644 --- a/trunk/milena/sandbox/green/mln/clustering/kmean3d.hh +++ b/trunk/milena/sandbox/green/mln/clustering/kmean3d.hh @@ -31,11 +31,11 @@ /// \brief Implements the optimized kmean algorithm. /// /// This algorithm is optimized in the way it proceeds directly with -/// the greylevel attribute inspite of the pixel attribute. The +/// the rgb values inspite of the pixel attribute. The /// algorithm is independant from the image dimension. But, we have to /// compute one time the histogram. In fact, we move a recurrent cost -/// to a fix cost in the complexity. This version is very adapted to -/// images with small quantification. +/// to a fix cost in the complexity. This version is adapted to +/// image with small quantification. #include <limits.h> #include <iostream> @@ -86,12 +86,12 @@ namespace mln { /// \brief Implements the kmean algorithm in a specific way. /// - /// This version of the kmean algorithm uses a greyscale image as input, + /// This version of the kmean algorithm uses a rgb image as input, /// temporary images for computations and produces images as result. Images - /// play the role of matrix or vector in standard statistic algoritm. + /// play the role of matrix or vector in standard statistic algorithm. /// /// T is the type used for computations (float or double). - /// n is the quantification for the image grayscale. + /// n is the quantification for the rgb image. template <typename T, unsigned n> struct kmean3d { @@ -208,7 +208,7 @@ namespace mln /// \brief Two ways: Regular greylevel tick or random greylevel value or. /// /// There is two way to proceed the initialization. First of all, we - /// divide the greyscale in regular tick and we assigne them to the mean + /// divide the rgb space in regular tick and we assigne them to the mean /// of the centers. Finaly, we can ask random initialization along the /// greyscale axis. The second process is needed to launch_n_times the /// kmean and converge to the best descent. @@ -388,17 +388,17 @@ namespace mln /// \} - /// Greylevels description. + /// rgb image description. /// \{ - /// \brief The information are concerned with the greylevel input image. + /// \brief The information are concerned with the rgb input image. /// - /// The group image allow us to decide which greylevel (and of course + /// The group image allow us to decide which rgb color (and of course /// which pixel) is assigned to a center. The distance image give us a /// clue on how a greylevel could contribute to a center. The summation - /// over the greylevels of a center give us the within variance. + /// over the rgb space of a center give us the within variance. - t_group_img _group; // g x 1 because dim(t_value) = 1 - t_distance_img _distance; // label x graylevel + t_group_img _group; // g x 3 because dim(t_value) = 3 + t_distance_img _distance; // label x rgb space /// \} @@ -1008,8 +1008,6 @@ namespace mln { trace::entering("mln::clustering::kmean3d::update_mean"); - /// FIXME VERIFIER QUE L'ON PEUT OBTENIR UNE IMAGE EN NDG SIGNE - // avec g le niveau de gris (signed or not signed) // w[g] la classe de g sous forme d'image // h[g] l'histogramme de l'image sous forme d'image @@ -1035,7 +1033,6 @@ namespace mln for_all(rgb) { - // peut être faut-il le decomposer par composantes _mean[_group(rgb)][0] += rgb.row() * _histo(rgb); _mean[_group(rgb)][1] += rgb.col() * _histo(rgb); _mean[_group(rgb)][2] += rgb.sli() * _histo(rgb); @@ -1275,8 +1272,8 @@ namespace mln // Debugging code update_cnv(); - std::cout << "_current_step : " << _current_step << std::endl; - std::cout << "_within_variance : " << _within_variance << std::endl; + //std::cout << "_current_step : " << _current_step << std::endl; + //std::cout << "_within_variance : " << _within_variance << std::endl; ++_current_step; } -- 1.5.6.5