数字图象处理冈萨雷斯MATLAB源程序工具箱,包含数字图像处理各种算法

				function I = colorseg(varargin)
				%COLORSEG Performs segmentation of a color image.
				%   S = COLORSEG('EUCLIDEAN', F, T, M) performs segmentation of color
				%   image F using a Euclidean measure of similarity. M is a 1-by-3
				%   vector representing the average color used for segmentation (this
				%   is the center of the sphere in Fig. 6.26 of DIPUM). T is the
				%   threshold against which the distances are compared.
				%
				%   S = COLORSEG('MAHALANOBIS', F, T, M, C) performs segmentation of
				%   color image F using the Mahalanobis distance as a measure of
				%   similarity. C is the 3-by-3 covariance matrix of the sample color
				%   vectors of the class of interest. See function covmatrix for the
				%   computation of C and M. 
				%
				%   S is the segmented image (a binary matrix) in which 0s denote the
				%   background. 
				
				%   Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins
				%   Digital Image Processing Using MATLAB, Prentice-Hall, 2004
				%   $Revision: 1.5 $  $Date: 2003/11/21 14:28:34 $
				
				% Preliminaries.
				% Recall that varargin is a cell array.
				f = varargin{2};
				if (ndims(f) ~= 3) | (size(f, 3) ~= 3)
				   error('Input image must be RGB.');
				end
				M = size(f, 1); N = size(f, 2);
				% Convert f to vector format using function imstack2vectors.
				[f, L] = imstack2vectors(f);
				f = double(f);
				% Initialize I as a column vector.  It will be reshaped later
				% into an image.
				I = zeros(M*N, 1); 
				T = varargin{3};
				m = varargin{4};
				m = m(:)'; % Make sure that m is a row vector.
				
				if length(varargin) == 4 
				   method = 'euclidean';
				elseif length(varargin) == 5 
				   method = 'mahalanobis';
				else 
				   error('Wrong number of inputs.');
				end
				
				switch method
				case 'euclidean'
				   % Compute the Euclidean distance between all rows of X and m. See
				   % Section 12.2 of DIPUM for an explanation of the following
				   % expression. D(i) is the Euclidean distance between vector X(i,:)
				   % and vector m. 
				   p = length(f);
				   D = sqrt(sum(abs(f - repmat(m, p, 1)).^2, 2));
				case 'mahalanobis'
				   C = varargin{5};
				   D = mahalanobis(f, C, m);
				otherwise 
				   error('Unknown segmentation method.')
				end
				
				% D is a vector of size MN-by-1 containing the distance computations
				% from all the color pixels to vector m. Find the distances 				J = find(D 				
				% Set the values of I(J) to 1.  These are the segmented
				% color pixels.
				I(J) = 1;
				
				% Reshape I into an M-by-N image.
				I = reshape(I, M, N);