✅作者简介：热爱科研的Matlab仿真开发者，修心和技术同步精进，matlab项目合作可私信。
🍎个人主页：Matlab科研工作室
🍊个人信条：格物致知。
更多Matlab仿真内容点击👇
⛄ 内容介绍
In this paper, we propose a fast combined separabil-ity filter, which can selectively detect circular features such as pupils and nostrils in an image of the human face. The proposed filter is designed as a combination of multiple rectangle separability filters so that it can achieve high-speed processing and high positioning ac-curacy at the same time. The evaluation experiments using synthetic images and real face images show that the proposed filter is 70 times faster than the conven-tional circular separability filter.
⛄ 部分代码
function MAP = cvtCombSimpRectFilter45(I,P,sh)
% Function to generate separability map for diagonal filters (diagonal left and right)
% Input: I: 45 degrees integral image, obtained by using cvtIntegralImage45(X);
% P: 45 degrees integral image of the square pixel values, obtained by using cvtIntegralImage45(X.^2);
% sh: size of the filter
% Output: MAP: two separability maps (diagonal left and diagonal right), with size: [Height, Width, 2].
%
% If you use this code, we would appreciate if you cite the following paper(s):
%
% [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.
%
% This code is written by Yasuhiro Ohkawa and distributed under BSD License.
% Computer Vision Laboratory (CVLAB)
% Graduate school of Systems and Information Engineering
% University of Tsukuba
% 2016
%
% Email: tsukuba.cvlab@gmail.com
% HP: http://www.cvlab.cs.tsukuba.ac.jp/
%
r = round(sh/sqrt(2));
w = ceil(sh/3/sqrt(2));
br = 2*r;
MAP(:,:,1) = tmpFnc(I,P,r,br,w,w);
MAP(:,:,2) = tmpFnc(I,P,w,w,r,br);
end
%%
function MAP = tmpFnc(I,P,r,br,w,bw)
MAP = zeros(size(I)-1);
[H, W] = size(MAP);
h = bw+br+2;
N =(2*bw+1)*(2*(1+2*br));
N1=(2*w+1)*(2*(1+2*r));
N2=N-N1;
HH = bw + br +1+1:H-(bw + br +2);
WW = bw + br +3:W-(bw + br+1);
P1 = I(HH - bw - br -1 ,WW + bw - br - 1);
P2 = I(HH + bw - br -1+1 ,WW - bw - br - 1-1 );
P3 = I(HH + bw + br +1 ,WW - bw + br - 1);
P4 = I(HH - bw + br ,WW + bw + br );
S = (P4+P2-P3-P1);
P1 = P(HH - bw - br -1 ,WW + bw - br - 1);
P2 = P(HH + bw - br -1+1 ,WW - bw - br - 1-1 );
P3 = P(HH + bw + br +1 ,WW - bw + br - 1);
P4 = P(HH - bw + br ,WW + bw + br );
T= (P4+P2-P3-P1);
M = S./N;
Y = T./N;
St = Y - M.^2;
P1 = I(HH - w - r -1 ,WW + w - r - 1);
P2 = I(HH + w - r -1+1 ,WW - w - r - 1-1 );
P3 = I(HH + w + r +1 ,WW - w + r - 1);
P4 = I(HH - w + r ,WW + w + r );
S1= (P4+P2-P3-P1);
S2=S-S1;
M1=S1/N1;
M2=S2/N2;
Sb = ((N1*((M1-M).^2)) + (N2*((M2-M).^2)))/N;
MAP(h:end-h,h:end-h)=(Sb./St).*sign(M2-M1);
MAP(isnan(MAP))=0;
MAP(isinf(MAP))=0;
end
⛄ 运行结果
⛄ 参考文献
[1]谢琛. 基于主动形状模型的人脸特征提取技术研究与应用[D]. 国防科学技术大学, 2009.
[2] 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.
[3] 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.
