www.pudn.com > GaussBackground.rar > GaussBackground.m

function [CDM,Tao,Hist] = GaussBackground(GaussImage) 
% 对图像序列进行高斯背景分析，去除背景和噪声， 
% 参见文章“Automatic Temporal Segmentation for Content-Based Video Coding”。 
% Arguments: 
% 	CDM：去除背景图像序列的变化检测模板 
%  	执行示范：[CDM,Tao,Hist] = GaussBackground(GaussImage); 
 
% 计算直方图 
% Hist = Histgram(GaussImage); 
Hist = HistgramInt(GaussImage); 
 
% 对于每一个T，计算适应准则MAEt，找到最小MAEt对应的T，作为背景检测阈值 
cols = size(Hist,2); 
[MinMAEt,pB] = MAE(Hist,abs(Hist(1,257))); 
Tao = Hist(1,257); 
for i=258:cols 
    [MAEt,pB] = MAE(Hist,abs(Hist(1,i))); 
    if ((MAEt < MinMAEt) && (pB>0)) 
        MinMAEt = MAEt; 
        Tao = Hist(1,i); 
    end 
end 
 
% 生成变化检测模板CDM 
rows = size(GaussImage,1); 
cols = size(GaussImage,2); 
CDM = zeros(rows,cols); 
for i = 1:rows 
    for j = 1:cols 
        if (abs(GaussImage(i,j)) > Tao) 
            CDM(i,j)=1; 
        end 
    end 
end 
% figure;imshow(GaussImage); 
% figure;imshow(CDM); 
 
%%  
function [MAEt,pB] = MAE(Hist,T) 
% 内部函数，适应准则MAEt。 
% 计算p(B) 
pB = 0; 
cols = size(Hist,2); 
for i = 1:cols 
    if (abs(Hist(1,i)) <= T) 
        pB = pB + Hist(2,i); 
    end 
end 
 
MAEt = 0; 
if (pB > 0) 
    %计算theta(B) 
    TmpthetaB = 0; 
    for i = 1:cols 
        if (abs(Hist(1,i)) <= T) 
            TmpthetaB = TmpthetaB + power(Hist(1,i),2) * Hist(2,i); 
        end 
    end  
    thetaB2 = TmpthetaB / pB; 
 
    %计算适应准则MAE(T)，其实SumLevel可以不考虑，因为对所有的T都一样。 
    for i = 1:cols 
        %计算p(dB) 
        pdB = 1 / power(2 * pi * thetaB2,0.5) * exp(-1 * 0.5 * power(Hist(1,i),2) / thetaB2); 
%         MAEt = MAEt + Hist(2,i)*abs(pB * pdB - Hist(2,i)); 
        MAEt = MAEt + abs(pB * pdB - Hist(2,i)); 
    end 
 
end 
 
%% 
function [Hist] = HistgramInt(DifImage) 
% 内部函数，返回图像的整数直方图。 
cols = size(DifImage,1)*size(DifImage,2); 
DifImage = reshape(round(DifImage),1,cols); 
MaxV = max(DifImage);   
MinV = min(DifImage); 
 
Hist = zeros(2,511); 
for i = 1:511 
    Hist(1,i) = i-256; 
end 
for i = 1:cols 
    CurrV = round(double(DifImage(1,i)) + double(256)); 
    Hist(2,CurrV) = Hist(2,CurrV) +1; 
end 
 
%% 
function [Hist] = Histgram(DifImage) 
% 内部函数，返回图像的实数直方图。 
cols = size(DifImage,1)*size(DifImage,2); 
DifImage = reshape(DifImage,1,cols); 
MaxV = max(DifImage);  % 在屏幕上只能看到小数点后4位，简便起见 
MinV = min(DifImage); 
StepV = double(MaxV - MinV) / 510; 
 
Hist = zeros(2,511); 
for i = 1:511 
    Hist(1,i) = roundn((i-1) * StepV + MinV,-4); 
end 
for i = 1:cols 
    CurrV = ceil(double(DifImage(1,i) - MinV)/StepV); 
    Hist(2,CurrV+1) = Hist(2,CurrV+1) +1;   %CurrV+1 是为了防止出现CurrV=0的情况 
end