Question

我有两张图片：手动分割（红线）和自动分割（蓝线）。

我想做的是

重叠它们，使第一个中的点对应于第二个中的点。
裁剪点的图像（自动分割）具有相同的尺寸和相同的比例，以便我可以通过比较两个轮廓来验证分割。

虽然我使用下面的代码尝试了MATHWORKS registering-an-image-using-normalized-cross-correlation的类似解决方案，但是我收到了错误。

我做错了什么？为什么xbegin和offset为负？

 % Algorithm for image validation
    % Open the two images which will be compared
    name2=input('Image name ( automated segmentation)     ','s');
    img_automated=imread(name2,'png');
    figure (1), imshow(img_automated), title('Image automated')
    name=input('Image name ( manual segmentation)     ','s');
    img_manual=imread(name,'png');
    img_manual_gray=rgb2gray(img_manual);
    figure (2), imshow (img_manual),title('Image manual')
    img_automated_gray=rgb2gray(img_automated);
    %img_double=im2double(img_automated_gray);
    figure (3), imshow (img_automated_gray), title (' Image converted to double ');
    imcontrast
    %uiwait(img_automated_gray)
    img_automated_eq=adapthisteq(img_automated_gray);
    figure (5), imshow (img_automated_eq), title (' Image after histogram equalization ');
    img_automated_gray=rgb2gray(img_automated);
    figure (6), imshowpair(img_manual,img_automated_eq)
    title('Images overlap')

    %Step 2: Choose Subregions of Each Image
    %It is important to choose regions that are similar.The image sub_automated
    %will be the template, and must be smaller than the image sub_manual. 
    % interactively

    [sub_manual,rect_manual] = imcrop(img_manual); % choose the pepper below the onion
    [sub_automated,rect_automated] = imcrop(img_automated_gray); % choose the whole onion
    % display sub images
    figure(8), imshow(sub_automated)
    figure(9), imshow(sub_automated)

    %Step 3: Do Normalized Cross-Correlation and Find Coordinates of Peak
    %Calculate the normalized cross-correlation and display it as a surface plot.
    % The peak of the cross-correlation matrix occurs where the sub_images are
    % best correlated. normxcorr2 only works on grayscale images, so we pass it
    % the red plane of each sub image.

    c = normxcorr2(sub_automated(:,:,1),sub_manual(:,:,1));
    figure (10), surf(c), shading flat

    %Step 4: Find the Total Offset Between the Images
    %The total offset or translation between images depends on the location
    %of the peak in the cross-correlation matrix, and on the size and position 
    %of the sub images.
    % offset found by correlation

    [max_c, imax] = max(abs(c(:)));
    [ypeak, xpeak] = ind2sub(size(c),imax(1));
    corr_offset = [(xpeak-size(sub_automated,2))
                   (ypeak-size(sub_automated,1))];
    % relative offset of position of subimages
    rect_offset = [(rect_manual(1)-rect_automated(1))
                   (rect_manual(2)-rect_automated(2))];
    % total offset
    offset = corr_offset + rect_offset;
    xoffset = offset(1);
    yoffset = offset(2);

    %Step 5: See if the Onion Image was Extracted from the Peppers Image
    %Figure out where onion falls inside of peppers.

    xbegin = round(xoffset+1);
    xend   = round(xoffset+ size(img_automated_gray,2));
    ybegin = round(yoffset+1);
    yend   = round(yoffset+size(img_automated_gray,1));
    % extract region from peppers and compare to onion
    extracted_automated =img_manual(ybegin:yend,xbegin:xend,:);
    if isequal(img_automated_gray,extracted_automated)
       disp('extracted_automated.png was extracted from img_automated.png')
    end

    %Step 6: Pad the Onion Image to the Size of the Peppers Image
    %Pad the automated image to overlay on manual, using the offset determined above.

    recovered_automated = uint8(zeros(size(img_manual)));
    recovered_onion(ybegin:yend,xbegin:xend,:) = img_automated_gray;
    figure(11), imshow(recovered_automated)
  figure (12), imshowpair(img_manual(:,:,1),recovered_automated,'blend')

Answer 1

这个答案可能不完整，但根据您已经包含的信息量，我可以分享一下：

了解您尝试注册的两个图像会出现什么样的失真会很有趣。我看到你已经使用了相似性＆＃39;作为transformation type。还有其他一些转换类型可能更适合您的图像，尽管您选择了足够的，准确定位的控制点，但错误的结果并不能给您带来好的结果。
MATLAB documentation列出了注册两个图像的其他方式，并根据图像中的要素类型，选择其中一个选项：即基于强度（使用imregister）或基于特征（使用一组计算机视觉系统工具箱中的功能可能更准确。
要将图像裁剪为相同尺寸，您可以使用imshowpair显示它们，然后使用imcrop达到您想要的尺寸。 imshowpair显示已注册对的所有像素，因此最终结果始终是显示内容的子组件。

如何裁剪和缩放图像相对于另一个？

1 个答案: