What are the number 5,25,20,13,1 refer too. How to adjust the number when different picture been used.

CircularHough_Grd(red, [5 25], 20, 13, 1);

i found the immBinning Function above .But i couldnt find the detail code of the above mentioned function. I am in need od the similar type of function to binn the Image Data.Would you please help me by providing the details of the above function

??? Undefined function or method ‘CircularHough_Grd’ for input arguments of type ‘uint8′.

Error in ==> code4 at 61
[accum, circen, cirrad] = CircularHough_Grd(red,[15 60]);

http://www.mathworks.com/matlabcentral/answers/11963-detecting-complex-circular-object-in-2d-images

I really2 appreciate your effort.
Thank you in advance.

Yours faithfully

NISHAD. V.H
M.TECH 3RD SEMESTER
DEPT. OF ELECTRONICS
CUSAT
COCHIN, KERALA
nishad.haneefa@cusat.ac.in
+91-9486107096

***************************************
Error in ==> detectCircle at 27
[accum, circen, cirrad] = CircularHough_Grd(red,[15 60]);
***************************************

img = imread('cell4.jpg');
imshow(img);

%% Discarding color information
% Tao's function works directly on grayscale images. Rather than converting
% the color image to grayscale with the Image Processing Toolbox's RGB2GRAY
% function, I elected to  simply use the first (red) color plane, and to
% use adaptive histogram equalization:
%togglefig('Red Plane, Adjusted')
red = img(:,:,2);
red = adapthisteq(red);
imtool(red)

%% Parameters for the segmentation
% Before I segment, I needed to know how big the cells were in the image;
% CircularHough_Grd takes as an input a range of radii to search for. Using
% the IMDISTLINE function in the Image Processing Toolbox's IMTOOL, I
% estimated that the radii of interest range from about 5 to 25 pixels.
%
% You can play around with the other input arguments to modify the
% function's sensitivity to less-than-perfect circles, for instance, or to
% change the way it deals with concentric circles. This makes the program
% pretty flexible!

%% Now for the segmentation...
tic;
[accum, circen, cirrad] = CircularHough_Grd(red,[15 60]);
toc

%% ...and a bit of post-processing
% Note to Tao: occasionally, your algorithm returns zero-radii "hits":
any(cirrad <= 0)
%%
% This is easy to address (for instance, to keep the RECTANGLE command below
% from erroring), but might be an opportunity for enhancement.
if any(cirrad 0);
    cirrad = cirrad(inds);
    circen = circen(inds,:);
end

%% View the results
% Now let's see how well the algorithm performed:
togglefig Results
imshow(img);
hold on;
plot(circen(:,1), circen(:,2), 'r+');
for ii = 1 : size(circen, 1)
    rectangle('Position',[circen(ii,1) - cirrad(ii), circen(ii,2) - cirrad(ii), 2*cirrad(ii), 2*cirrad(ii)],...
        'Curvature', [1,1], 'edgecolor', 'b', 'linewidth', 1.5);
end
hold off;
%%
% That's pretty remarkable, especially given the simplicity of my
% pre-processing. (Adaptive histogram equilization helped a lot; Tao's
% suggested filters improve the performance even more.)

%% Final comments
% The time it takes to run this algorithm varies markedly, depending on the
% user settings. In this case, it took my computer approximately 4
% seconds--but did a pretty amazing job of segmentating the image. Note how
% well it handled overlapping cells (circles), for instance:
togglefig Blowup
imshow(img)
xlims = [406 520];
ylims = [52 143];
set(gca,'xlim',xlims,'ylim',ylims)
inImageCircles = find(inpolygon(circen(:,1), circen(:,2), xlims([1 2 2 1]), ylims([1 1 2 2])));
for ii = 1 : numel(inImageCircles)
    rectangle('Position',...
        [circen(inImageCircles(ii),1) - cirrad(inImageCircles(ii)),...
        circen(inImageCircles(ii),2) - cirrad(inImageCircles(ii)),...
        2*cirrad(inImageCircles(ii)),...
        2*cirrad(inImageCircles(ii))],...
        'Curvature', [1,1], 'edgecolor', 'b', 'linewidth', 1.5);
end