THESE/codes/meanshift/Ms_segmenter/Fit_spline.m

   1 function Dense_pts = Fit_spline (Pts)\r
   2 \r
   3 Xdata = Pts(1, :);\r
   4 Ydata = Pts(2, :);\r
   5 \r
   6 % making splines separately for X and Y, but with common parameter\r
   7 Length = 0;\r
   8 Param(1) = Length;\r
   9 for i=1:length(Xdata)-1\r
  10         Length = Length + norm(Pts(:, i+1) - Pts(:, i));\r
  11         Param(i+1) = Length;\r
  12 end\r
  13 \r
  14 % sampling at a dense set of points\r
  15 Gridpts = [min(Param):0.1:max(Param)];\r
  16 Xspline = round(spline(Param, Xdata, Gridpts));\r
  17 Yspline = round(spline(Param, Ydata, Gridpts));\r
  18 \r
  19 % if not sufficiently dense, adjusting those\r
  20 k = 0;  Dense_pts = zeros(2, 0);\r
  21 for i=1:length(Xspline)-1\r
  22         if max(abs(Xspline(i+1)-Xspline(i)), abs(Yspline(i+1)-Yspline(i))) > 1\r
  23                 for j=0:abs(Xspline(i+1)-Xspline(i))\r
  24                         k = k+1;\r
  25                         Dense_pts(:, k) = [Xspline(i) + j*sign(Xspline(i+1)-Xspline(i)); ...\r
  26                                 Yspline(i)];\r
  27                 end\r
  28                 for j=0:abs(Yspline(i+1)-Yspline(i))\r
  29                         k = k+1;\r
  30                         Dense_pts(:, k) = [Xspline(i+1); ...\r
  31                                 Yspline(i) + j*sign(Yspline(i+1)-Yspline(i))];\r
  32                 end\r
  33          else\r
  34                 k = k+1;\r
  35                 Dense_pts(:, k) = [Xspline(i); Yspline(i)];\r
  36         end\r
  37 end\r