Resize the background image. â« >>backApprox256 = imresize(backApprox, [256,256],. 'bilinear');. â« >>figure, imshow(backApprox256). Show resized image ...
Robot Vision Matlab Exercises
1. Read an image and display it. >>clear, close all >>I=imread(‘pout.tif’); >>imshow(I) >>whos
Read an image, compute and plot its histogram >>figure, imhist(I) %Display a histogram of I in a new figure >>I2=histeq(I); %Equalize the histogram >>figure, imshow(I2) % Display the equalized image Did it enhance it? >>figure, imhist(I2)
Store an image in a file and verify it >>imwrite(I2, ‘pout2.png’); Now check it >>imfinfo(‘pout2.png’) What does Matlab tell you?
Read an image and display it >>clear, close all >>I=imread(‘rice.tif’); >>Imshow(I)
Non-uniform illumination correction >>backApprox = blkproc(I,[32,32],’min(x(:)’); See the result >>backApprox Display the surface >>backApprox=double(backApprox)/255; %Convert to double >>figure, surf(backApprox); >>set(gca,’ydir’,’reverse’); %Reverse the y –axis Rotate surface with toolbar
Resize the background image
>>backApprox256 = imresize(backApprox, [256,256], ‘bilinear’); >>figure, imshow(backApprox256) Show resized image
Subtract the background
>>I=im2double(I); %Convert to double >>I2=I-backApprox256 ; %Subtract the background >>I2=max(min(I2,1),0); %Clip to range >>figure, imshow(I2) Look at demo on subtraction >>ipss003
Adjust the contrast
>>I3=imadjust(I2, [0 max(I2(:))],[0 1]); % Adjust contract Display the new image >>figure, imshow(I3);
Apply threshold to detect edge points >>bw = I3>0.2; % Make I3 a binary image with all points >0.2 white >>figure, imshow(bw) >>whos See the new characteristics
Count the objects >>[labeled,numObjects] = bwlabel(bw,4); %Label components Show the number of objects found by bwlabel >>numObjects The answer should be 80 grains of rice.
Check by displaying color image
>>map=hot(numObjects+1);%Create color map >>imshow(labeled+1,map); %Offset indices to colormap by 1 to avoid index 0 Count again >>max(labeled(:)) Again the answer should be 80
Examine an object
>>grain = imcrop(labeled) % Crop a portion of the image Look at values >>grain
Region of interest
>>rect= [ 15 25 10 10]; Roi = imcrop(labeled, rect) This shows the numbers starting at ((15,25) and of width 10 by 10
Compute image features
>>grain = imfeature(labeled, ‘basic’) Find the area of grain 51 >>grain(51).area Should be 323 pixels Find the smallest possible bounding box and the centroid of grain 51 >>grain(51).BoundingBox >>grain(51).Centroid
Look at all areas
>>allgrains=[grain.Area]; >> whos allgrains >>allgrains(51) Is it still 323?
Statistical properties
>>max(allgrains) >>biggrain = find(allgrains ==749) Should return 68 >>mean(allgrains) >>hist(allgrains,20)
Where to go next
Read Chapter 2 Look at Tutorials, Chapters 4 and 5 All functions are defined in Chapter 12 Help is available using >> help functionname Online Help Toolbox Demos as Templates
Theory and Practice
Next we will start reviewing some theory and see if we can put in in practice
Image Processing Toolbox User Manual
http://www.mathworks.com/access/hel pdesk/help/pdf_doc/images/images_tb. pdf Let’s try the exercises in this user guide
