Gabor特征提取、LBP特征提取、DCT特征提取，高手们帮帮忙~

[xlx890121]

现在做毕业设计，是关于人脸识别的。其中要对三种不同的灰度图像分别实现 Gabor特征提取、LBP特征提取、DCT特征提取。
我这段日子研究了很久仍然一筹莫展，请问哪里能找到现成可用的程序呢？那个OpenCV我也看了下，但不会用啊。
求高手们帮帮我。

[fengbingchun]

opencv上手很快的，先看看opencv吧

[wendy425]

matlab版gabor特征：
%The Gabor filter is basically a Gaussian (with variances sx and sy along x and y-axes respectively)
%modulated by a complex sinusoid (with centre frequencies U and V along x and y-axes respectively)
%described by the following equation
%%
% 1 -1 x ^ y ^
%%% G(x,y) = ---------- * exp ([----{(----) 2+(----) 2}+2*pi*i*(Ux+Vy)])
% 2*pi*sx*sy 2 sx sy

%% Describtion :

%% I : Input image
%% Sx & Sy : Variances along x and y-axes respectively
%% U & V : Centre frequencies along x and y-axes respectively

%% G : The output filter as described above
%% gabout : The output filtered image

%% Author : Ahmad poursaberi e-mail : [e-mail]
%% Faulty of Engineering, Electrical&Computer Department,Tehran
%% University,Iran,June 2004

function [G,gabout] = gaborfilter(I,Sx,Sy,U,V);

if isa(I,'double')~=1
I = double(I);
end

for x = -fix(Sx):fix(Sx)
for y = -fix(Sy):fix(Sy)
G(fix(Sx)+x+1,fix(Sy)+y+1) = (1/(2*pi*Sx*Sy))*exp(-.5*((x/Sx)^2+(y/Sy)^2)+2*pi*i*(U*x+V*y));
end
end

Imgabout = conv2(I,double(imag(G)),'same');
Regabout = conv2(I,double(real(G)),'same');

gabout = uint8(sqrt(Imgabout.*Imgabout + Regabout.*Regabout));

[wendy425]

matlab版LBP特征：
%LBP returns the local binary pattern image or LBP histogram of an image.
% J = LBP(I,R,N,MAPPING,MODE) returns either a local binary pattern
% coded image or the local binary pattern histogram of an intensity
% image I. The LBP codes are computed using N sampling points on a
% circle of radius R and using mapping table defined by MAPPING.
% See the getmapping function for different mappings and use 0 for
% no mapping. Possible values for MODE are
% 'h' or 'hist' to get a histogram of LBP codes
% 'nh' to get a normalized histogram
% Otherwise an LBP code image is returned.
%
% J = LBP(I) returns the original (basic) LBP histogram of image I
%
% J = LBP(I,SP,MAPPING,MODE) computes the LBP codes using n sampling
% points defined in (n * 2) matrix SP. The sampling points should be
% defined around the origin (coordinates (0,0)).
%
% Examples
% --------
% I=imread('rice.png');
% mapping=getmapping(8,'u2');
% H1=LBP(I,1,8,mapping,'h'); %LBP histogram in (8,1) neighborhood
% %using uniform patterns
% subplot(2,1,1),stem(H1);
%
% H2=LBP(I);
% subplot(2,1,2),stem(H2);
%
% SP=[-1 -1; -1 0; -1 1; 0 -1; -0 1; 1 -1; 1 0; 1 1];
% I2=LBP(I,SP,0,'i'); %LBP code image using sampling points in SP
% %and no mapping. Now H2 is equal to histogram
% %of I2.

function result = lbp(varargin) % image,radius,neighbors,mapping,mode)
% Version 0.3.1
% Authors: Marko Heikkil�and Timo Ahonen

% Changelog
% Version 0.3.1: Changed MAPPING input to be a struct containing the mapping
% table and the number of bins to make the function run faster with high number
% of sampling points. Lauge Sorensen is acknowledged for spotting this problem.


% Check number of input arguments.
error(nargchk(1,5,nargin));

image=varargin{1};
d_image=double(image);

if nargin==1
spoints=[-1 -1; -1 0; -1 1; 0 -1; -0 1; 1 -1; 1 0; 1 1];
mapping=0;
mode='h';
end

if (nargin == 2) && (length(varargin{2}) == 1)
error('Input arguments');
end

if (nargin > 2) && (length(varargin{2}) == 1)
radius=varargin{2};
neighbors=varargin{3};

spoints=zeros(neighbors,2);

% Angle step.
a = 2*pi/neighbors;

for i = 1:neighbors
spoints(i,1) = -radius*sin((i-1)*a);
spoints(i,2) = radius*cos((i-1)*a);
end

if(nargin >= 4)
mapping=varargin{4};
if(isstruct(mapping) && mapping.samples ~= neighbors)
error('Incompatible mapping');
end
else
mapping=0;
end

if(nargin >= 5)
mode=varargin{5};
else
mode='h';
end
end

if (nargin > 1) && (length(varargin{2}) > 1)
spoints=varargin{2};

if(nargin >= 3)
mapping=varargin{3};
if(isstruct(mapping) && mapping.samples ~= neighbors)
error('Incompatible mapping');
end
else
mapping=0;
end

if(nargin >= 4)
mode=varargin{4};
else
mode='h';
end
end

% Determine the dimensions of the input image.
[ysize xsize] = size(image);

neighbors=size(spoints,1);

miny=min(spoints(:,1));
maxy=max(spoints(:,1));
minx=min(spoints(:,2));
maxx=max(spoints(:,2));

% Block size, each LBP code is computed within a block of size bsizey*bsizex
bsizey=ceil(max(maxy,0))-floor(min(miny,0))+1;
bsizex=ceil(max(maxx,0))-floor(min(minx,0))+1;

% Coordinates of origin (0,0) in the block
origy=1-floor(min(miny,0));
origx=1-floor(min(minx,0));

% Minimum allowed size for the input image depends
% on the radius of the used LBP operator.
if(xsize < bsizex || ysize < bsizey)
error('Too small input image. Should be at least (2*radius+1) x (2*radius+1)');
end

% Calculate dx and dy;
dx = xsize - bsizex;
dy = ysize - bsizey;

% Fill the center pixel matrix C.
C = image(origy:origy+dy,origx:origx+dx);
d_C = double(C);

bins = 2^neighbors;

% Initialize the result matrix with zeros.
result=zeros(dy+1,dx+1);

%Compute the LBP code image

for i = 1:neighbors
y = spoints(i,1)+origy;
x = spoints(i,2)+origx;
% Calculate floors, ceils and rounds for the x and y.
fy = floor(y); cy = ceil(y); ry = round(y);
fx = floor(x); cx = ceil(x); rx = round(x);
% Check if interpolation is needed.
if (abs(x - rx) < 1e-6) && (abs(y - ry) < 1e-6)
% Interpolation is not needed, use original datatypes
N = image(ry:ry+dy,rx:rx+dx);
D = N >= C;
else
% Interpolation needed, use double type images
ty = y - fy;
tx = x - fx;

% Calculate the interpolation weights.
w1 = (1 - tx) * (1 - ty);
w2 = tx * (1 - ty);
w3 = (1 - tx) * ty ;
w4 = tx * ty ;
% Compute interpolated pixel values
N = w1*d_image(fy:fy+dy,fx:fx+dx) + w2*d_image(fy:fy+dy,cx:cx+dx) + ...
w3*d_image(cy:cy+dy,fx:fx+dx) + w4*d_image(cy:cy+dy,cx:cx+dx);
D = N >= d_C;
end
% Update the result matrix.
v = 2^(i-1);
result = result + v*D;
end

%Apply mapping if it is defined
if isstruct(mapping)
bins = mapping.num;
for i = 1:size(result,1)
for j = 1:size(result,2)
result(i,j) = mapping.table(result(i,j)+1);
end
end
end

if (strcmp(mode,'h') || strcmp(mode,'hist') || strcmp(mode,'nh'))
% Return with LBP histogram if mode equals 'hist'.
result=hist(result(:),0:(bins-1));
if (strcmp(mode,'nh'))
result=result/sum(result);
end
else
%Otherwise return a matrix of unsigned integers
if ((bins-1)<=intmax('uint8'))
result=uint8(result);
elseif ((bins-1)<=intmax('uint16'))
result=uint16(result);
else
result=uint32(result);
end
end

end

[wendy425]

matlab中有dct函数来进行离散余弦变换

[xlx890121]

[Quote=引用 3 楼 wendy425 的回复:]
matlab版LBP特征：
%LBP returns the local binary pattern image or LBP histogram of an image.
% J = LBP(I,R,N,MAPPING,MODE) returns either a local binary pattern
% coded image or the local binary pattern……
[/Quote]
谢谢你了~

[xiaoxiaozhuzhuaa]

那如何在DCT域进行特征提取呢？求高手指教。