daima.rar_SIFT算法_sift_遗传算法

%///////////////////////////////////////////////////////////////////////////////////////////// % % refine_features - scale space feature detector based upon difference of gaussian filters. % selects features based upon their maximum response in scale space % % Usage: features = refine_features(img, pyr, scl, imp, pts, radius, min_separation, edgeratio) % % Parameters: % % img : original image % pyr : cell array of filtered image pyramid % scl : scaling factor between levels of the image pyramid % imp : image pyramid cell array % pts : cell array of selected points on each pyramid level % radius : radius for edge rejection test % min_separation : minimum separation distance for maxima rejection. % edgeratio: maximum ratio of eigenvalues of feature curvature for edge rejection. % % Returns: % % features - matrix with one row for each feature consisting of the following: % [x loc, y loc, scale value, size, edge flag, edge orientation, scale space curvature] % % where: % x loc and y loc are the x and y positions on the original image % scale value is the sub level adjusted scale value % size is the size of the feature in pixels on the original image % edge flag is zero if the feature is classified as an edge % edge orientation is the angle made by the edge through the feature point % scale space curvature is a rough confidence measure of feature prominence % % Author: % Scott Ettinger % [email protected] % % May 2002 %///////////////////////////////////////////////////////////////////////////////////////////// function [features,keys] = refine_features(img, pyr, scl, imp,pts, radius, min_separation, edgeratio) [ho,wo]=size(img); [h2,w2]=size(imp{2}); %create offset list for ring of pixels [ry2,rx2]=meshgrid(-20:20,-20:20); z = (rx2.^2+ry2.^2)<=(radius)^2 & (rx2.^2+ry2.^2)>(radius-1)^2; [ry2,rx2]=find(z); rx2=rx2-21; ry2=ry2-21; ndx = 1; %loop through each level of pyramid for j=2:length(imp)-1 p=pts{j}; %get current level filter response img=imp{j}; %get current level image [dh,dw]=size(img); np = 1; min_sep = min_separation*max(max(pyr{j})); %calculate minimum separation for valid maximum for i=1:size(p,1) ptx = p(i,2); pty = p(i,1); if p(i,1) < radius+3 %ensure neighborhood is not outside of image p(i,1) = radius+3; end if p(i,1) > dh-radius-3 p(i,1) = dh-radius-3; end if p(i,2) < radius+3 p(i,2) = radius+3; end if p(i,2) > dw-radius-3 p(i,2) = dw-radius-3; end %adjust to sub pixel maxima location r = pyr{j}(pty-1:pty+1,ptx-1:ptx+1); %get 3X3 neighborhood of pixels [pcy,pcx] = fit_paraboloid(r); %find center of paraboloid fit to points if abs(pcy)>1 %ignore extreme offsets due to singularities in parabola fitting pcy=0; pcx=0; end if abs(pcx)>1 pcx=0; pcy=0; end p(i,1) = p(i,1) + pcy; %adjust center p(i,2) = p(i,2) + pcx; ptx = p(i,2); pty = p(i,1); px=(pts{j}(i,2)+pcx - 1)*scl^(j-2) + 1; %calculate point locations at pyramid level 2 py=(pts{j}(i,1)+pcy - 1)*scl^(j-2) + 1; %calculate Sub-Scale level adjustment y1 = interp(pyr{j-1},(p(i,2)-1)*scl+1, (p(i,1)-1)*scl+1); %get response on surrounding scale levels using interpolation y3 = interp(pyr{j+1},(p(i,2)-1)/scl+1, (p(i,1)-1)/scl+1); y2 = interp(pyr{j},p(i,2),p(i,1)); coef = fit_parabola(0,1,2,y1,y2,y3); % fit neighborhood of 3 scale points to parabola scale_ctr = -coef(2)/2/coef(1); %find max in scale space if abs(scale_ctr-1)>1 %ignore extreme values due to singularities in parabola fitting scale_ctr=0; end %eliminate edge points and enforce minimum separation rad2 = radius * scl^(scale_ctr-1); %adust radius size to account for new scale value o=0:pi/8:2*pi-pi/8; %create ring of points at radius around test point rx = (rad2)*cos(o); ry = (rad2)*sin(o); rmax = 1e-9; %init max and min values rmin = 1e9; gp_flag = 1; pval = interp(pyr{j},ptx,pty); %get response at feature center rtst = []; %check points on ring around feature point for k=1:length(rx) rtst(k) = interp(pyr{j},ptx+rx(k),pty+ry(k)); %get ring point value with bilinear interpolation if pval> 0 %calculate distance from feature point for each point in ring rtst(k) = pval - rtst(k); else rtst(k) = rtst(k) - pval; end gp_flag = gp_flag * rtst(k)>min_sep; %test for valid maxima above noise floor if rtst(k)>rmax %find min and max rmax = rtst(k); end if rtst(k)<rmin rmin = rtst(k); end end fac = scl/(wo*2/size(pyr{2},2)); %calculate size offset due to edge effects of downsampling [cl,or] = f_class(rtst); %classify features and get orientations if rmax/rmin > edgeratio %test for edge criterion gp_flag=0; if cl ~= 2 %keep all intersections (# ridges > 2) gp_flag = 1; else ang = min(abs([(or(1)-or(2)) (or(1)-(or(2)-2*pi))])); if ang < 6.5*pi/8; %keep edges with angles more acute than 145 deg. gp_flag = 1; end end end %save info features(ndx,1) = (px-1)*wo/w2*fac +1; %save x and y position (sub pixel adjusted) features(ndx,2) = (py-1)*wo/w2*fac +1; features(ndx,3) = j+scale_ctr-1; %save scale value (sub scale adjusted in units of pyramid level) features(ndx,4) = ((7-4)*scl^(j-2+scale_ctr-1))*wo/w2*fac; %save size of feature on original image features(ndx,5) = gp_flag; %save edge flag features(ndx,6) = or(1); %save edge orientation angle features(ndx,7) = coef(1); %save curvature of response through scale space if features(ndx,1) >