【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码

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一、Combined Separability Filter

 

【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码?

【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码

【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码

【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码

【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码

 【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码?

 

二、部分代码



clear;

X = imread(‘testimages/sample1.png‘); % sample1.png is a gray-scale CG generated face image

[H, W] = size(X);
S1 = cat(3,X,X,X); % used for displaying final result (Geometric mean)
S2 = cat(3,X,X,X); % used for displaying final result (Arithmetic mean)
X = double(X); % convert data type to double
I1 = cvtIntegralImage(X);       % calculate integral image
P1 = cvtIntegralImage(X.^2);    % calculate integral image of squared pixel value
I2 = cvtIntegralImage45(X);     % calculate 45 degrees integral image
P2 = cvtIntegralImage45(X.^2);  % calculate 45 degrees integral image of squared pixel value

nR = 3; % filter size parameter
nTH = 0.55; % threshold for finding local peaks

P = zeros(H,W,4);  % variable to store separability map
P(:,:,1:2) = cvtCombSimpRectFilter(I1,P1,nR);   % apply vertical and horizontal rectangular filters
P(:,:,3:4) = cvtCombSimpRectFilter45(I2,P2,nR); % apply diagonal left and right filters
P(P<0) = 0;
finalMap1 = prod(P(:,:,:),3).^(1/4.0);
finalMap2 = mean(P(:,:,:),3);

figure(10);clf;

for i=1:6
    subplot(2,4,i);
    if (i < 5)
        imagesc(P(:,:,i));
        axis equal tight;
        title([‘separability map #‘ num2str(i)]);
    elseif (i==5)
        imagesc(finalMap1);
        axis equal tight;
        title(‘Geometric mean‘);
    elseif (i==6)
        imagesc(finalMap2);
        axis equal tight;
        title(‘Arithmetic mean‘);
    end
end

% find local peaks (Geometric mean)
PL1 = cvtFindLocalPeakX(finalMap1,1,nTH);
% draw circle and cross at each local peak with radius of the filter (nR)
for H=1:size(PL1,2)
    S1 = cvtDrawCircle(S1, PL1(2,H),PL1(1,H),nR,[255,0,0],20);
    S1 = cvtDrawCross(S1,PL1(2,H),PL1(1,H),nR,[255,255,255]);
end
subplot(2,4,7); 
image(S1); % display original
title({[‘Local peaks > ‘ num2str(nTH)]; ‘Geometric mean‘});
axis equal tight;

% find local peaks (Arithmetic mean)
PL2 = cvtFindLocalPeakX(finalMap2,1,nTH);
% draw circle and cross at each local peak with radius of the filter (nR)
for H=1:size(PL2,2)
    S2 = cvtDrawCircle(S2, PL2(2,H),PL2(1,H),nR,[255,0,0],20);
    S2 = cvtDrawCross(S2,PL2(2,H),PL2(1,H),nR,[255,255,255]);
end
subplot(2,4,8);
image(S2); % display original with marks for the local peak
axis equal tight;
title({[‘Local peaks > ‘ num2str(nTH)]; ‘Arithmetic mean‘});
【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码

三、仿真结果

【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码?

 

四、参考文献

 [1] Y. Ohkawa, C. H. Suryanto, K. Fukui,  "Fast Combined Separability Filter for Detecting Circular Objects", The twelfth IAPR conference on Machine Vision Applications (MVA) pp.99-103, 2011.

 [2] K. Fukui, O. Yamaguchi,  "Facial feature point extraction method based on combination of shape extraction  and pattern matching", Systems and Computers in Japan 29 (6), pp.49-58, 1998.

【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码?

 

 

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【图像检测】基于Combined Separability Filter实现鼻孔和瞳孔检测matlab源码

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