-medfilt1m.m: adding

git-svn-id: svn+ssh://lumo.ucsd.edu/projects/p1/svnroot/pdollar/toolbox@14534 52fe0c90-79fe-0310-8a18-a0b98ad248f8

external/history.txt

@@ -9,6 +9,8 @@ Version NEW
  -generalized seqIo.m>to/frimgs
  -generalized bbApply>random
  -seqIo.m: added case for making seq file from array of images
+ -moved modefilt1.m form /image to /filters
+ -added filters/medfilt1m.m: adaptive median filtering with missing values
 
 Version 2.31 (03-Jun-09)
  -tweaked images/imrectLite.m 

filters/Contents.m

@@ -17,3 +17,7 @@
 %   FbMake           - Various 1D/2D/3D filterbanks (hardcoded).
 %   FbReconstruct2d  - Use to see how much image information is preserved in filter outputs.
 %   FbVisualize      - Used to visualize a series of 1D/2D/3D filters. 
+%
+% Simple nonlinear filters:
+%   medfilt1m        - One-dimensional adaptive median filtering with missing values.
+%   modefilt1        - One-dimensional mode filtering.

filters/medfilt1m.m

@@ -0,0 +1,79 @@
+function y = medfilt1m( x, r, z )
+% One-dimensional adaptive median filtering with missing values.
+%
+% Applies a width s=2*r+1 one-dimensional median filter to vector x, which
+% may contain missing values (elements equal to z). If x contains no
+% missing values, y(j) is set to the median of x(j-r:j+r). If x contains
+% missing values, y(j) is set to the median of x(j-R:j+R), where R is the
+% smallest radius such that sum(valid(x(j-R:j+R)))>=s, i.e. the number of
+% valid values in the window is at least s (a value x is valid x~=z). Note
+% that the radius R is adaptive and can vary as a function of j.
+%
+% This function uses a modified version of medfilt1.m from Matlab's 'Signal
+% Processing Toolbox'. Note that if x contains no missing values,
+% medfilt1m(x) and medfilt1(x) are identical execpt at boundary regions.
+%
+% USAGE
+%  y = medfilt1m( x, r, [z] )
+%
+% INPUTS
+%  x      - [nx1] length n vector with possible missing entries
+%  r      - filter radius
+%  z      - [NaN] element that represents missing entries
+%
+% OUTPUTS
+%  y      - [nx1] filtered vector x
+%
+% EXAMPLE
+%  x=repmat((1:4)',1,5)'; x=x(:)'; x0=x;
+%  n=length(x); x(rand(n,1)>.8)=NaN;
+%  y = medfilt1m(x,2); [x0; x; y; x0-y]
+%
+% See also MODEFILT1, MEDFILT1
+%
+% Piotr's Image&Video Toolbox      Version NEW
+% Copyright 2008 Piotr Dollar.  [pdollar-at-caltech.edu]
+% Please email me if you find bugs, or have suggestions or questions!
+% Licensed under the Lesser GPL [see external/lgpl.txt]
+
+% apply medfilt1 (standard median filter) to valid locations in x
+if(nargin<3 || isempty(z)), z=NaN; end; x=x(:)'; n=length(x);
+if(isnan(z)), valid=~isnan(x); else valid=x~=z; end; v=sum(valid);
+if(v==0), y=repmat(z,1,n); return; end
+if(v<2*r+1), y=repmat(median(x(valid)),1,n); return; end
+y=medfilt1(x(valid),2*r+1);
+
+% get radius R needed at each location j to span s=2r+1 valid values
+% get start (a) and end (b) locations and map back to location in y
+C=[0 cumsum(valid)]; s=2*r+1; R=find(C==s); R=R(1)-2; pos=zeros(1,n);
+for j=1:n, R0=R;
+  R=R0-1; a=max(1,j-R); b=min(n,j+R);
+  if(C(b+1)-C(a)<s), R=R0; a=max(1,j-R); b=min(n,j+R);
+    if(C(b+1)-C(a)<s), R=R0+1; a=max(1,j-R); b=min(n,j+R); end
+  end
+  pos(j)=(C(b+1)+C(a+1))/2;
+end
+y=y(floor(pos));
+
+end
+
+function y = medfilt1( x, s )
+% standard median filter (copied from medfilt1.m)
+n=length(x); r=floor(s/2); indr=(0:s-1)'; indc=1:n;
+ind=indc(ones(1,s),1:n)+indr(:,ones(1,n));
+x0=x(ones(r,1))*0; X=[x0'; x'; x0'];
+X=reshape(X(ind),s,n); y=median(X,1);
+end
+
+% function y = medfilt1( x, s )
+% % standard median filter (slow)
+% % get unique values in x
+% [vals,disc,inds]=unique(x); m=length(vals); n=length(x);
+% if(m>256), warning('x takes on large number of diff vals'); end %#ok<WNTAG>
+% % create quantized representation [H(i,j)==1 iff x(j)==vals(i)]
+% H=zeros(m,n); H(sub2ind2([m,n],[inds; 1:n]'))=1;
+% % create histogram [H(i,j) is count of x(j-r:j+r)==vals(i)]
+% H=localSum(H,[0 s],'same');
+% % compute median for each j and map inds back to original vals
+% [disc,inds]=max(cumsum(H,1)>s/2,[],1); y=vals(inds);
+% end

images/Contents.m

@@ -66,4 +66,3 @@
 %   maskEllipse    - Creates a binary image of an ellipse.
 %   maskGaussians  - Divides a volume into softly overlapping gaussian windows.
 %   maskSphere     - Creates an 'image' of a n-dimensional hypersphere.
-%   modefilt1      - One-dimensional mode filtering.