matlab粒子群运动模拟

1 基于粒子群改进的sift图像配准

模型参考这里。

2 部分代码

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%   close all;  clear all;   %% image path  file_image='';   %% read two images  [filename,pathname]=uigetfile({'*.*','All Files(*.*)'},'Select reference image',...  file_image);  image_1=imread(strcat(pathname,filename));  [filename,pathname]=uigetfile({'*.*','All Files(*.*)'},'Select the image to be registered',...  file_image);  image_2=imread(strcat(pathname,filename));   %% Display the reference image and the image to be registered  figure;  subplot(1,2,1);  imshow(image_1);  title('Reference image');  subplot(1,2,2);  imshow(image_2);  title('Image to be registered');   %% make file for save images  if (exist('save_image','dir')==0)%如果文件夹不存在  mkdir('save_image');  end   t1=clock;%Start time  %% Convert input image format  [~,~,num1]=size(image_1);  [~,~,num2]=size(image_2);  if(num1==3)  image_11=rgb2gray(image_1);  else  image_11=image_1;  end  if(num2==3)  image_22=rgb2gray(image_2);  else  image_22=image_2;  end   %Converted to floating point data between 0-1  image_11=im2double(image_11);  image_22=im2double(image_22);   %% Define the constants used  sigma=1.6;%Bottom Gauss Pyramid scale  dog_center_layer=3;%Defines the DOG Pyramid intermediate layer, the default is 3  contrast_threshold_1=0.04;%Contrast threshold of reference image  contrast_threshold_2=0.04;%Contrast threshold of the image to be registered  edge_threshold=10;%Edge threshold  is_double_size=false;%Whether the image size is enlarged,the default is 'false',to get more points, set it to 'true'  change_form='similarity';%Select geometric transformation type,it can be 'similarity','affine'   %% The number of groups in Gauss Pyramid  nOctaves_1=num_octaves(image_11,is_double_size);  nOctaves_2=num_octaves(image_22,is_double_size);   %% Generation of the first layer of the Gauss scale image  image_11=create_initial_image(image_11,is_double_size,sigma);  image_22=create_initial_image(image_22,is_double_size,sigma);   %% Generating Gauss Pyramid of reference image  tic;  [gaussian_pyramid_1,gaussian_gradient_1,gaussian_angle_1]=...  build_gaussian_pyramid(image_11,nOctaves_1,dog_center_layer,sigma);  disp(['Reference image generation Gauss Pyramid spent time is：',num2str(toc),'s']);   %% Generating DOG Pyramid of reference image  tic;  dog_pyramid_1=build_dog_pyramid(gaussian_pyramid_1,nOctaves_1,dog_center_layer);  disp(['Reference image generation DOG Pyramid spent time is：',num2str(toc),'s']);  clear gaussian_pyramid_1;   %% Search for extreme points in the DOG Pyramid of the reference image  tic;  [key_point_array_1]=find_scale_space_extream...  (...  dog_pyramid_1,...  nOctaves_1,...  dog_center_layer,...  contrast_threshold_1,...  sigma,...  edge_threshold,...  gaussian_gradient_1,...  gaussian_angle_1...  );  disp(['The extreme points of the reference image detection spend time is：',num2str(toc),'s']);  clear dog_pyramid_1;   %% The feature point descriptor generation,Reference image  tic;  [descriptors_1,locs_1]=calc_descriptors(gaussian_gradient_1,gaussian_angle_1,.....  key_point_array_1,is_double_size);  disp(['Reference image feature point descriptor generation spend time is：',num2str(toc),'s']);  clear gaussian_gradient_1;  clear gaussian_angle_1;    %% Generating Gauss Pyramid of the image to be registered  tic;  [gaussian_pyramid_2,gaussian_gradient_2,gaussian_angle_2]=...  build_gaussian_pyramid(image_22,nOctaves_2,dog_center_layer,sigma);  disp(['The image to be registered generation Gauss Pyramid spent time is：',num2str(toc),'s']);   %% Generating DOG Pyramid of the image to be registered  tic;  dog_pyramid_2=build_dog_pyramid(gaussian_pyramid_2,nOctaves_2,dog_center_layer);  disp(['The image to be registered generation DOG Pyramid spent time is：：',num2str(toc),'s']);  clear gaussian_pyramid_2;   %% Search for extreme points int the DOG Pyramid of the image to be registered  tic;  [key_point_array_2]=find_scale_space_extream...  (...  dog_pyramid_2,...  nOctaves_2,...  dog_center_layer,...  contrast_threshold_2,...  sigma,...  edge_threshold,...  gaussian_gradient_2,...  gaussian_angle_2...  );  disp(['The extreme points of the image to be registered detection spend time is：',num2str(toc),'s']);  clear dog_pyramid_2;   %% The feature point descriptor generation,the image to be registered  tic;  [descriptors_2,locs_2]=calc_descriptors(gaussian_gradient_2,gaussian_angle_2,...  key_point_array_2,is_double_size);  disp(['The image to be registered feature point descriptor generation spend time is：',num2str(toc),'s']);  clear gaussian_gradient_2;  clear gaussian_angle_2;   %% Calculation of geometric transformation parameters  tic;  [solution,~,cor1,cor2]=...  match(image_2, image_1,descriptors_2,locs_2,descriptors_1,locs_1,change_form);  disp(['Feature point matching spend time is：',num2str(toc),'s']);   tform=maketform('projective',solution');  [M,N,P]=size(image_1);  ff=imtransform(image_2,tform, 'XData',[1 N], 'YData',[1 M]);  button=figure;  subplot(1,2,1);  imshow(image_1);  title('Reference image');  subplot(1,2,2);  imshow(ff);  title('Image after registration');  str1=['.\save_image\','Results after registration','.jpg'];  saveas(button,str1,'jpg');  t2=clock;  disp(['Total spending time is：',num2str(etime(t2,t1)),'s']);   %% Display the detected feature points on the image  [button1,button2]=showpoint_detected(image_1,image_2,locs_1,locs_2);  str1=['.\save_image\','Reference image detection point','.jpg'];  saveas(button1,str1,'jpg');  str1=['.\save_image\','Points detected in the image to be registered','.jpg'];  saveas(button2,str1,'jpg');   %% Image fusion  image_fusion(image_1,image_2,solution);