图像边缘提取

function [cg,theta] = cgmo(im,radius,norient,varargin) % function [cg] = cgmo(im,radius,norient,...) % % Compute the color gradient at a single scale and multiple % orientations. % % INPUT % im Grayscale or RGB image, values in [0,1]. % radius Radius of disc for cg. % norient Number of orientations for cg. % 'nbins' Number of bins; should be > 1/sigmaSim. % 'sigmaSim' For color similarity function. % 'gamma' Gamma correction for LAB [2.5]. % 'smooth' Smoothing method, one of % {'gaussian','savgol','none'}, default 'none'. % 'sigmaSmo' Sigma for smoothing, default to radius. % % OUTPUT % cg Size [h,w,d,norient] array of cg images, % where d is the dimensionality of the image. % % The input parameters {radius,nbins,sigmaSim,sigmaSmo} should be % scalars when the input image is grayscale, and can be either scalars % or 3-element vectors when the image is RGB. % % See also cgmo. % % David R. Martin <dmartin@eecs.berkeley.edu> % April 2003 % process options nbins = 32; sigmaSim = 0.1; gamma = 2.5; smooth = 'none'; sigmaSmo = radius; for i = 1:2:numel(varargin), opt = varargin{i}; if ~ischar(opt), error('option names not a string'); end if i==numel(varargin), error(sprintf('option ''%s'' has no value',opt)); end val = varargin{i+1}; switch opt, case 'nbins', nbins=val; case 'sigmaSim', sigmaSim=val; case 'gamma', gamma=val; case 'smooth', switch val, case {'none','gaussian','savgol'}, smooth=val; otherwise, error(sprintf('invalid option smooth=''%s''',val)); end case 'sigmaSmo', sigmaSmo=val; otherwise, error(sprintf('invalid option ''%s''',opt)); end end % check arguments if ndims(im)==2, % grayscale image if numel(radius)~=1, error('radius should have 1 element'); end if numel(nbins)~=1, error('nbins should have 1 element'); end if numel(sigmaSim)~=1, error('sigmaSim should have 1 element'); end if numel(sigmaSmo)~=1, error('sigmaSim should have 1 element'); end elseif ndims(im)==3, % RGB image if numel(radius)==1, radius = radius*ones(3,1); end if numel(nbins)==1, nbins = nbins*ones(3,1); end if numel(sigmaSim)==1, sigmaSim = sigmaSim*ones(3,1); end if numel(sigmaSmo)==1, sigmaSmo = sigmaSmo*ones(3,1); end if numel(radius)~=3, error('radius should have 1 or 3 elements'); end if numel(nbins)~=3, error('nbins should have 1 or 3 elements'); end if numel(sigmaSim)~=3, error('sigmaSim should have 1 or 3 elements'); end if numel(sigmaSmo)~=3, error('sigmaSmo should have 1 or 3 elements'); end radius = radius(:); nbins = nbins(:); sigmaSim = sigmaSim(:); sigmaSmo = sigmaSmo(:); else error('image not of valid dimension'); end norient = max(1,norient); nbins = max(1,nbins); % min and max values for a,b channels of LAB % used to scale values into the unit interval abmin = -73; abmax = 95; % make sure nbins is large enough with respect to sigmaSim if any( nbins < 1./sigmaSim ), warning('nbins < 1/sigmaSim is suspect'); end % check pixel valies if min(im(:)) < 0 | max(im(:))>1, error('pixel values out of range [0,1]'); end if ndims(im)==2, % grayscale image % compute cg from gray values cmap = max(1,ceil(im*nbins)); csim = colorsim(nbins,sigmaSim); [cg,theta] = tgmo(... cmap,nbins,radius,norient,... 'tsim',csim,'smooth',smooth,'sigma',sigmaSmo); else, % RGB image % convert gamma-corrected image to LAB and scale values into [0,1] lab = RGB2Lab(im.^gamma); lab(:,:,1) = lab(:,:,1) ./ 100; lab(:,:,2) = (lab(:,:,2) - abmin) ./ (abmax-abmin); lab(:,:,3) = (lab(:,:,3) - abmin) ./ (abmax-abmin); lab(:,:,2) = max(0,min(1,lab(:,:,2))); lab(:,:,3) = max(0,min(1,lab(:,:,3))); % compute cg from LAB values cg = zeros([size(im) norient]); for i = 1:3, cmap = max(1,ceil(lab(:,:,i)*nbins(i))); csim = colorsim(nbins(i),sigmaSim(i)); [cg(:,:,i,:),theta] = tgmo(... cmap,nbins(i),radius(i),norient,... 'tsim',csim,'smooth',smooth,'sigma',sigmaSmo(i)); end end % compute color similarity matrix assuming colors are in [0,1] function m = colorsim(nbins,sigma) bc = ((1:nbins)-0.5)/nbins; % bin centers [x,y] = meshgrid(bc,bc); m = 1.0 - exp(-abs(x-y).^2./(2*sigma.^2));