LOCALGLOV2D_BASE - Base function for LOCALGLOV2D.

Contents

Syntax

   O = LOCALGLOV2D_BASE(I, feat, res, win, wei, n, sig, mask );

See also

Ressembles: LOCALGLCM2D_BASE, LOCALGLSDV2D_BASE. Requires: NEIPOSKERNEL, EUCLIDKERNEL, ACCUMARRAY.

Function implementation

function O = localglov2d_base(I, feat, res, win, wei, n, sig, mask )

warning('localglov2d_base:warning', ...
    'currently computing the entropy feature only');

parsing parameters

if ~isnumeric(I)
    disp('a matrix is required in input'); return;
end
[M,N,C] = size(I);

checking: ensure that the window size is odd

if mod(win,2)==0,  win = win + 1;  end
ws = floor(win / 2); % half size window

dealing with multichannel images

O = zeros(M,N);
if C>1
    for c=1:C
        O(:,:,c) =  localglov2d_base(I(:,:,c), feat, res, win, wei, n, sig, mask );
    end
end

initializing

if strcmp(n,'global');
    Imin = min(I(:));
    Idelt = max(I(:)) - Imin;
end

pad the input image

A = padarray(I, [ws ws],'replicate','both');
[X,Y,~] = size(A);

create the (temp) output image

O = zeros(X,Y);

indexes

pixindex = reshape(1:X*Y,X,Y);
pixin = reshape(pixindex(ws+1:ws+M,ws+1:ws+N),1,M*N);

index of neighbour window of analysis

indI = neiposkernel(ws, X);
indI = indI(:);

initialize the weights

switch wei
    case 'ave'
        W = fspecial('average',win);
    case 'gaus'
        W = fspecial('gaussian',win,sig);
    case 'inv'
        W = euclidkernel([win win], 1, false, true);
        W(ws+1,ws+1) = 1;
        if sig~=1, W = W.^sig; end
end
W = W(:);
W = W / sum(W);

main computation

W = [0; W; 0];

for in=pixin

    % extract current values in window
    inI = A(indI + in);
    if strcmp(n,'local');
        Imin = min(inI(:));
        Idelt = max(inI(:)) - Imin;
    end
    % quantize
    inI = ceil((res-1) * (inI - Imin) / Idelt + 0.5); % in range [1,nbin]

fast method ensure that the output vector is of size nbin, and not shorter, by padding the greylevels 1 and res with null probability (see W padding above), so that accumarray take them into account in the 1D vector construction

    inI = [1; inI; res];                                               %#ok
    % accumulate using the weights in W to create the 1D vector of size
    % nbin
    pI = accumarray(inI,W);
    % compute the feature
    O(in) =  entropy2(pI(:));

end

O = reshape(O(pixin),M,N);

end % end of localglov2d_base

Subfunctions

%--------------------------------------------------------------------------
function E = entropy2(pI)
pI(pI==0) = 1;
E = - sum(pI .* log2(pI));
end % end of entropy2


%--------------------------------------------------------------------------
function H = homogeneity(pI)                                           %#ok
H = sum(pI ./ I);
end % end of homogeneity