I am new to Matlab and I would like to use Matlab to compare the following pictures and find out the differences between them.
Real picture:
Reference picture:
First, the system should match the real picture and the reference picture
Second, the system should match the modified picture with the reference picture and highlight out the differences.
Please advise on my doubt:
How can I match the similarity from two total different image? Should I selectively compare part of the both images? I have an idea using histogram and normalized to find out the peak match.
Thank you.
There are many things people do. This is an active research area called image matching.
Sometimes people do template matching. Which is to match the entire reference picture to the real picture at many locations and at many scales. You can read more about this particular technique here: http://en.wikipedia.org/wiki/Template_matching
Related
I want to identify all occurrences of a list of images in a screenshot and get the label of the emoji (e.g. "smile").
The list of images holds all emojis (full list):
and so on…
And this is the screenshot (show large):
The screenshots can have different resolutions and different heights where the emoji occur.
My ideas were:
Using OpenCV and a variety of filtering and iterate all emoji images over it (maybe using template matching)
Using neural networks like tensorflow, training your own model with the emojis
How would you do it ?
There are several classic ways to answer your problem:
Simple regular correlation: https://en.wikipedia.org/wiki/Cross-correlation.
The simple correlation is used when you have exactly the image you are looking for, with no change in intensity.
Normalized correlation (math behind template matching): https://en.wikipedia.org/wiki/Template_matching.
The simple correlation is used when you have exactly the image you are looking for, with no change in intensity.
If you have different intensities between your screenshot and your emoji base picture, you should use normalized correlation.
Both these methods will give you an image with peaks, and your emojis will be localized at the local maxima of this image.
As your emojis can be very similar to one another, you will have to use a threshold on the correlation image in order to discriminate between the emoji you are testing and the ones that look nearly like him.
This method can be time consuming, but can be easily speed-up by using an image pyramid.
An image pyramid is a set of image where the first one is your image, the second one is a subsampling of the first by a factor of 2, and so on:
https://en.wikipedia.org/wiki/Pyramid_(image_processing).
Then the correlation is applyed on the top level of the pyramid to find an approximate location, then on the top - 1 level around the approxiamte location and so on.
About the neural network, or other machine learning methods you want to try, they are really heavy solutions and you have a pretty simple problem, so you should normaly don't need them.
You have the exact picture you are looking for, without rotation, deformation or intensity change, and the template matching should be very effective.
first of all, I have to say I'm new to the field of computervision and I'm currently facing a problem, I tried to solve with opencv (Java Wrapper) without success.
Basicly I have a picture of a part from a Model taken by a camera (different angles, resoultions, rotations...) and I need to find the position of that part in the model.
Example Picture:
Model Picture:
So one question is: Where should I start/which algorithm should I use?
My first try was to use KeyPoint Matching with SURF as Detector, Descriptor and BF as Matcher.
It worked for about 2 pcitures out of 10. I used the default parameters and tried other detectors, without any improvements. (Maybe it's a question of the right parameters. But how to find out the right parameteres combined with the right algorithm?...)
Two examples:
My second try was to use the color to differentiate the certain elements in the model and to compare the structure with the model itself (In addition to the picture of the model I also have and xml representation of the model..).
Right now I extraxted the color red out of the image, adjusted h,s,v values manually to get the best detection for about 4 pictures, which fails for other pictures.
Two examples:
I also tried to use edge detection (canny, gray, with histogramm Equalization) to detect geometric structures. For some results I could imagine, that it will work, but using the same canny parameters for other pictures "fails". Two examples:
As I said I'm not familiar with computervision and just tried out some algorithms. I'm facing the problem, that I don't know which combination of algorithms and techniques is the best and in addition to that which parameters should I use. Testing it manually seems to be impossible.
Thanks in advance
gemorra
Your initial idea of using SURF features was actually very good, just try to understand how the parameters for this algorithm work and you should be able to register your images. A good starting point for your parameters would be varying only the Hessian treshold, and being fearles while doing so: your features are quite well defined, so try to use tresholds around 2000 and above (increasing in steps of 500-1000 till you get good results is totally ok).
Alternatively you can try to detect your ellipses and calculate an affine warp that normalizes them and run a cross-correlation to register them. This alternative does imply much more work, but is quite fascinating. Some ideas on that normalization using the covariance matrix and its choletsky decomposition here.
I have two binary image like this. I have a data set with lots of picture like at the bottom but with differents signs.
and
I would like to compare them in order to know if it's the same figure or not (especially inside the triangle). I took a look in Sift and Surf feature but it's doesn't work well on this type of picture (it find matchning point whereas the two picture are different,especially inside).
I also hear about SVM but i don't know if i have to implement it for this type of problem.
Do you have an idea ?
Thank you
I think you should not use SURF features on the binary image as you have already discarded a lot of information at that stage with your edge detector.
You could also use the Linear or Circle Hough Transform that in this case could tell you a lot about image differences.
If you wat to find 2 exactly identical images, simply use hash functions like md5.
But if you want to find related ( not exatcly identical) images, you are running in trouble ;). look for artificial neural network libs...
I am working on a project which gives plots real time traffic status on Google Maps, & make it available to user on an Android phone and web browser.
http://www.youtube.com/watch?v=tcAyMngkzjk
I need to compare 2 images in openCV in order to determine traffic density. Can you please guide me how to compare the images? Should I go for histogram comparison or simple image subtraction?
One common solution is using background subtraction to track moving objects (cars) and then export an image with the moving objects remarked, so you can easily extract the objects from the image. If this is not the case, you will have to detect the vehicles and that's more challenging task because as carlosdc says there are many approaches depending on the angle of the camera, the size of vehicles, light conditions, cluttered backgrounds, etc.
If you specify a little more the problem ...
It really depends, and it would be impossible to determine without looking at your images.
Also, let me point out that it may be quite difficult to make this work adequately in all conditions: day/night, ray/shine, etc. Perhaps you should start by looking at what others have done and how good/bad it works. One such example would be this
try to read this two tutorials about OpenCV versus detect/recognition and find contur.
http://python-catalin.blogspot.ro/search/label/OpenCV
or try to find the color change in your image ... ( for example find colors versus background street )
So I'm trying to run a comparison of different images and was wondering if anyone could point me in the right direction for some basic metrics I can take for the group of images.
Assuming I have two images, A and B, I pretty much want as much data as possible about each so I can later programmatically compare them. Things like "general color", "general shape", etc. would be great.
If you can help me find specific properties and algorithms to compute them that would be great!
Thanks!
EDIT: The end goal here is to be able to have a computer tell me how "similar" too pictures are. If two images are the same but in one someone blurred out a face; they should register as fairly similar. If two pictures are completely different, the computer should be able to tell.
What you are talking about is way much general and non-specific.
Image information is formalised as Entropy.
What you seem to be looking for is basically feature extraction and then comparing these features. There are tons of features that can be extracted but a lot of them could be irrelevant depending on the differences in the pictures.
There are space domain and frequency domain descriptors of the image which each can be useful here. I can probably name more than 100 descriptors but in your case, only one could be sufficient or none could be useful.
Pre-processing is also important, perhaps you could turn your images to grey-scale and then compare them.
This field is so immensely diverse, so you need to be a bit more specific.
(Update)
What you are looking for is a topic of hundreds if not thousands of scientific articles. But well, perhaps a simplistic approach can work.
So assuming that the question here is not identifying objects and there is no transform, translation, scale or rotation involved and we are only dealing with the two images which are the same but one could have more noise added upon it:
1) Image domain (space domain): Compare the pixels one by one and add up the square of the differences. Normalise this value by the width*height - just divide by the number of pixels. This could be a useful measure of similarity.
2) Frequency domain: Convert the image to frequency domain image (using FTT in an image processing tool such as OpenCV) which will be 2D as well. Do the same above squared diff as above, but perhaps you want to limit the frequencies. Then normalise by the number of pixels. This fares better on noise and translation and on a small rotation but not on scale.
SURF is a good candidate algorithm for comparing images
Wikipedia Article
A practical example (in Mathematica), identifying corresponding points in two images of the moon (rotated, colorized and blurred) :
You can also calculate sum of differences between histogram bins of those two images. But it is also not a silver bullet...
I recommend taking a look at OpenCV. The package offers most (if not all) of the techniques mentioned above.