www.pudn.com > Ms_segmenter.rar > Color_segment_svd3.m

function [New_image, Modes] = Color_segm_sps (Luv_img, Wind, Min_group_size) 
% Color_segment - segment an Luv image according to Meer's algorithm 
% gets image from the caller, finds significant maxima by mean shift algo  
% groups all the pixels  
% whose color falls within the given interval (with peak being the  
% center, and Window_radius being the radius),  
% Min_group_size : need at least that many pixels to qualify for a color 
 
Step = 4; 
% Step = 1; 
Max_axis = 255; 
Rows = size(Luv_img, 1); 
Cols = size(Luv_img, 2); 
Luv_image = Luv_img(1:Step:Rows, 1:Step:Cols, :);% taking part of it only 
Min_image_size = 0.005*Rows/Step*Cols/Step;% to stop when image is small 
 
Luv_vect = reshape(shiftdim(Luv_image, 2), ... 
	[3, size(Luv_image, 1)*size(Luv_image, 2)]); 
Max_luv = max(Luv_vect, [], 2);% scaling it 
for i=1:3 
   Luv_vect(i, :) = floor(Luv_vect(i, :)/(Max_luv(i)+1)*Max_axis)+1; 
end 
 
Wind = round(Wind/255*Max_axis);% as if the max was 255 
 
% creating a new array - color based 
for i=1:Max_axis+1 
   Cumulative{i} = sparse(Max_axis+1, Max_axis+1); 
end 
 
Nz = sparse(1, Max_axis+1); 
for i=1:size(Luv_vect, 2) 
   Cumulative{Luv_vect(1, i)}(Luv_vect(2, i), Luv_vect(3, i)) = ... 
      Cumulative{Luv_vect(1, i)}(Luv_vect(2, i), Luv_vect(3, i))+1; 
   Nz(Luv_vect(1, i)) = Nz(Luv_vect(1, i))+1; 
end 
 
% for background detection only 
Cumulative{1} = sparse(Max_axis+1, Max_axis+1); 
Nz(1) = 0; 
 
k = 0; L = Max_axis+1; 
while sum(Nz) > Min_image_size 
   Non_zero_pos = find(Nz > 0); 
   if length(Non_zero_pos) == 0 
      break; 
   end 
   Init(1) = Non_zero_pos(1); 
   Non_zero_pos_slice = find(Cumulative{Init(1)} > 0); 
   if length(Non_zero_pos_slice) == 0 
      fprintf('Something is wrong\n'); 
		break; 
   end 
    
   [Init(2), Init(3)] = ind2sub(size(Cumulative{Init(1)}), ... 
		Non_zero_pos_slice(1)); 
    
   [Mode, Number_values, Flag] = M_shift3_sps(Cumulative, Wind, ... 
		Init, Min_image_size/5); 
    
   if not(Flag)% good group 
		k = k+1; 
      Modes(:, k) = Mode'; 
      Nz = Nz-Number_values; 
      for i=max(1, Mode(1)-Wind):min(L, Mode(1)+Wind) 
         Cumulative{i}(... 
            max(1, Mode(2)-Wind):min(L, Mode(2)+Wind), ... 
            max(1, Mode(3)-Wind):min(L, Mode(3)+Wind)) = ... 
          zeros(size(Cumulative{i}(... 
            max(1, Mode(2)-Wind):min(L, Mode(2)+Wind), ... 
            max(1, Mode(3)-Wind):min(L, Mode(3)+Wind)))); 
      end 
   else 
      Nz = Nz-Number_values; 
      for i=max(1, Mode(1)-Wind):min(L, Mode(1)+Wind) 
         Cumulative{i}(... 
            max(1, Mode(2)-Wind):min(L, Mode(2)+Wind), ... 
            max(1, Mode(3)-Wind):min(L, Mode(3)+Wind)) = ... 
          zeros(size(Cumulative{i}(... 
            max(1, Mode(2)-Wind):min(L, Mode(2)+Wind), ... 
            max(1, Mode(3)-Wind):min(L, Mode(3)+Wind)))); 
      end 
   end 
end 
 
Luv_vect = reshape(shiftdim(Luv_img, 2), [3, Rows*Cols]); 
for i=1:3 
   Luv_vect(i, :) = floor(Luv_vect(i, :)/(Max_luv(i)+1)*Max_axis)+1; 
end 
 
Ones = ones(1, size(Luv_vect, 2)); 
New_image = zeros(1, size(Luv_vect, 2)); 
for i=1:size(Modes, 2) 
   Center = Modes(:, i) * Ones; 
   Norms = max(abs(Center - Luv_vect)); 
   To_take = find(Norms <= Wind); 
   New_image(To_take) = i; 
end 
 
% detecting background 
To_zero = find(Luv_vect(1, :) == 0); 
New_image(To_zero) = 0; 
New_image = reshape(New_image, [Rows, Cols]);