Morphological Image Processing

Transcription

1 Morphological Image Processing Examples 1

2 Example 1 Estimate the number of balls (the number won t be exact) You will have to shrink the balls so that they don t touch 2

3 Example 1 (continued) clear all close all I = imread('balls.gif'); imshow(i,[]) B = im2bw(i, graythresh(i)); B = ~B; % Complement image S = strel('disk', 5, 0); I2 = imerode(b,s); figure, imshow(i2); [L, n] = bwlabel(i2); blobs = regionprops(l); for i=1:n rectangle('position', blobs(i).boundingbox, 'EdgeColor', 'r'); end Found 1647 balls 3

4 Plot the density of the balls As a function of x As a function of y Example 1 (continued) 4

5 Example 1 (continued) allcentroids = cat(1,blobs(:).centroid); % Get all centroids % Plot distribution of centroids histx = hist(allcentroids(:,1)); figure, plot(histx) histy = hist(allcentroids(:,2)); figure, plot(histy)

6 Example 2 Task: Segment coins from the background Namely, generate a binary (or logical ) image which is white (1) where there are coins, and black (0) elsewhere Use morphological operators so that: No gaps in the coins No extraneous white pixels in the background Image eight.tif 6

7 clear all close all Example 2 (continued) I = imread('eight.tif'); imshow(i,[]); B = im2bw(i, graythresh(i)); B = imcomplement(b); % threshold % we want black regions S = strel('disk',1); B1 = imopen(b,s); % define a small structuring element % get rid of small white regions S = strel('disk', 5, 0); % Need structuring element bigger than gaps B2 = imclose(b1,s); % Fill in gaps figure, imshow(b2); [L,n] = bwlabel(b2); fprintf('n = %d\n', n); figure, imshow(l, []); RGB = label2rgb(l); figure, imshow(rgb); % find connected components % create false color image for visualization 7

8 original image thresholded image, after morphological operations labeled image labeled image with false colors 8

9 Example 3 The image xray.jpg is an X-ray image of a chicken nugget with some bone fragments inside (Figure 9.18 from the textbook). Create a binary image using the Matlab command B=I>200 (a little later in the course we will see how to pick thresholds automatically). Apply the Matlab function bwlabel to find connected components. How many components are there? 9

10 Example 3 (continued) Get rid of the tiny noise blobs by opening the image with a disk structuring element of radius 1. Now how many components are there? An automatic inspection will reject the nugget if the total area of all large fragments (larger than 100 pixels) is more than 1000 pixels. Using the opened image from step 2, find all blobs with individual areas greater than 100 pixels, and draw a rectangle around each large blob that was found. What is the total area of the large blobs? 10

11 clear all close all I = imread('xray.jpg'); imshow(i, []); B = I>200; figure, imshow(b, []); [L,n] = bwlabel(b); fprintf('n = %d\n', n); SE = strel('disk',1); B = imopen(b,se); figure, imshow(b, []); My answers: Initial number of components = 145 Number after eliminating small components = 18 Total area of large components = [L,n] = bwlabel(b); fprintf('n = %d\n', n); figure, imshow(l, []); blobs = regionprops(l); % Look at blob areas areas = cat(1,blobs(:).area); indices = find(areas > 100); blobslarge = blobs(indices); areaslarge = cat(1,blobslarge(:).area); areatotal = sum(areaslarge); fprintf('total area = %f\n', areatotal); % indices of large blobs % All large blobs for i=1:length(blobslarge) rectangle('position', blobslarge(i).boundingbox, 'EdgeColor', 'r'); end 11