CODEWITHSUNDEEP
matlabimage-processingcomputer-visionmatlab-cvst
Tomescu George
Tomescu George

Reputation: 155

How do I detect an instance of an object in an image?

I have an image containing several specific objects. I would like to detect the positions of those objects in this image. To do that I have some model images containing the objects I would like to detect. These images are well cropped around the object instance I want to detect.

Here is an example:

In this big image,

enter image description here

I would like to detect the object represented in this model image:

enter image description here

Upvotes: 2

Views: 5777

Answers (3)

yuk
yuk

Reputation: 19870

The Mathworks has a classic demo of image registration using the same technique as in @yoda's answer:

Registering an Image Using Normalized Cross-Correlation

Upvotes: 4

Oli
Oli

Reputation: 16045

Here is the answer that I was about to post when the question was closed. I guess it's similar to yoda's answer.

You can try to use normalized cross corelation:

im=rgb2gray(imread('di-5Y01.jpg'));
imObj=rgb2gray(imread('di-FNMJ.jpg'));
score = normxcorr2(imObj,im);
imagesc(score)

The result is: (As you can see, the whitest point corresponds to the position of your object.)

enter image description here

Upvotes: 9

abcd
abcd

Reputation: 42225

Since you originally posted this as a 'gimme-da-codez' question, showing absolutely no effort, I'm not going to give you the code. I will describe the approach in general terms, with hints along the way and it's up to you to figure out the exact code to do it.

Firstly, if you have a template, a larger image, and you want to find instances of that template in the image, always think of cross-correlation. The theory is the same whether you're processing 1D signals (called a matched filter in signal processing) or 2D images.

  • Cross-correlating an image with a known template gives you a peak wherever the template is an exact match. Look up the function normxcorr2 and understand the example in the documentation.
  • Once you find the peak, you'll have to account for the offset from the actual location in the original image. The offset is related to the fact that cross-correlating an N point signal with an M point signal results in an N + M -1 point output. This should be clear once you read up on cross-correlation, but you should also look at the example in the doc I mentioned above to get an idea.

Once you do these two, then the rest is trivial and just involves cosmetic dressing up of your result. Here's my result after detecting the object following the above.

enter image description here

Here's a few code hints to get you going. Fill in the rest wherever I have ...

%#read & convert the image
imgCol  = imread('https://i.sstatic.net/tbnV9.jpg');
imgGray = rgb2gray(img);
obj     = rgb2gray(imread('https://i.sstatic.net/GkYii.jpg'));

%# cross-correlate and find the offset
corr           = normxcorr2(...); 
[~,indx]       = max(abs(corr(:))); %# Modify for multiple instances (generalize)
[yPeak, xPeak] = ind2sub(...);
corrOffset     = [yPeak - ..., xPeak - ...]; 

%# create a mask
mask      = zeros(size(...));
mask(...) = 1;
mask      = imdilate(mask,ones(size(...)));

%# plot the above result
h1 = imshow(imgGray);
set(h1,'AlphaData',0.4)
hold on
h2 = imshow(imgCol);
set(h2,'AlphaData',mask)

Upvotes: 38

Related Questions