I am trying to use two Gaussian mixtures with EM algorithm to estimate color distribution of a video frame. For that, I want to use two separate peaks in the color distribution as the two Gaussian means to facilitate the EM calculation. I have several difficulties with the implementation of these in OpenCV.
My first question is: how can I determine the two peaks? I've searched about peak estimation in OpenCV, but still couldn't find any seperate function. So I am going to determine two regions, then find their maximum values as peaks. Is this way correct?
My second question is: how to perform Gaussian mixture model with EM in OpenCV? As far as I know, the "cv::EM::predict" function could give me the index of the most probable mixture component. But I have difficulties with training EM. I've searched and found some other codes, but finding the correct parameters is too much difficult for. Could someone provide me any example code for this? Thank you in advance.
#ederman, try {OpenCV library location}\opencv\samples\cpp\em.cpp instead of the web link. I think the sample code in the link is out of date now. I have successfully compiled the sample code in OpenCV 2.3.1. It shouldn't be a problem for 2.4.2.
Good luck:)
My first question is: how can I determine the two peaks?
I would iterate through the range of sample values possible, and test when the does EM.predict(sample)[0] peaks.
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I have been in the project of testing the images of sample products compared with a sample product image. I have come up with two approaches but there are problems encountered in each approach.
Method 1. Remove Background, Realign images according to features and then find the difference of two images by subtraction.
Problem: I am thinking about using template matching to extract the region of interest and save as a new picture. However, is it possible to use template matching to extract? I saw the sample provided by opencv can make a frame or rectangle around the matched object. So, it seems feasible for me to make it at the center in the new picture. If it is possible, what is the way of making a square matched as the center of a new picture? It seems a bit difficult as the matched rectangle may not be horizontal.
Method 2. Cascade Classifier Training: it seems I can train the classifier to know what bad images are and what good images are.
Problem: However, from the classifier detection sample by opencv, it was used to compare during a video. Is it possible to do so on images? Also, how could I adjust the sample error or the precision of the classifier detection?
If you have any other feasible suggestions, please kindly give me some advices on them. Thanks for your kind attention!!
I'm currently working on a project where I have to compare similar histograms of image intensity. These histograms are obtained from photos taken under different illumination conditions.
I know that OpenCV offers the compareHist function. However this function returns a metric of similarity and I'm looking for a method that matches corresponding peaks/valleys between similar histograms.
For instance, if we have two photos of the same subject, one underexposed and one with the "ideal" exposure, their histograms of intensity might look something like the image in the following URL:
http://i.stack.imgur.com/tLIGR.png
As shown by the arrows, the peaks in one histogram also exist in the other. Anyone has a suggestion on how to match corresponding peaks?
Thank you!
You can use an implementation of DTW (https://en.wikipedia.org/wiki/Dynamic_time_warping) to compare the histograms.
Using dynamic programming, you can create a matrix that calculates DTW. Then, you can trace back through the matrix to find the relations between different parts of the histograms.
After that, it's simply a matter of extracting only the peaks.
I would like to compare a picture (with his descriptors) with thousand of pictures inside a database in order to do a matching. (if two pictures are the same,that is to say the same thing but it can bo rotated, a bit blured, has a different scale etc.).
For example :
I saw on StackOverflaw that compute descriptors for each picture and compare them one to one is very a long process.
I did some researches and i saw that i can do an algorithm based on Bag of Words.
I don't know exactly how is works yet, but it seems to be good. But in think, i can be mistaked, it is only to detect what kind of object is it not ?
I would like to know according to you if using it can be a good solution to compare a picture to a thousands of pictures using descriptors like Sift of Surf ?
If yes, do you have some suggestions about how i can do that ?
Thank,
Yes, it is possible. The only thing you have to pay attention is the computational requirement which can be a little overwhelming. If you can narrow the search, that usually help.
To support my answer I will extract some examples from a recent work of ours. We aimed at recognizing a painting on a museum's wall using SIFT + RANSAC matching. We have a database of all the paintings in the museum and a SIFT descriptor for each one of them. We aim at recognizing the paining in a video which can be recorded from a different perspective (all the templates are frontal) or under different lighting conditions. This image should give you an idea: on the left you can see the template and the current frame. The second image is the SIFT matching and the third shows the results after RANSAC.
Once you have the matching between your image and each SIFT descriptor in your database, you can compute the reprojection error, namely the ratio between matched points (after RANSAC) and the total number of keypoints. This can be repeated for each image and the image with the lowest reprojection error can be declared as the match.
We used this for paintings but I think that can be generalized for every kind of image (the android logo you posted in the question is a fair example i think).
Hope this helps!
I am trying to do image detection in C++. I have two images:
Image Scene: 1024x786
Person: 36x49
And I need to identify this particular person from the scene. I've tried to use Correlation but the image is too noisy and therefore doesn't give correct/accurate results.
I've been thinking/researching methods that would best solve this task and these seem the most logical:
Gaussian filters
Convolution
FFT
Basically, I would like to move the noise around the images, so then I can use Correlation to find the person more effectively.
I understand that an FFT will be hard to implement and/or may be slow especially with the size of the image I'm using.
Could anyone offer any pointers to solving this? What would the best technique/algorithm be?
In Andrew Ng's Machine Learning class we did this exact problem using neural networks and a sliding window:
train a neural network to recognize the particular feature you're looking for using data with tags for what the images are, using a 36x49 window (or whatever other size you want).
for recognizing a new image, take the 36x49 rectangle and slide it across the image, testing at each location. When you move to a new location, move the window right by a certain number of pixels, call it the jump_size (say 5 pixels). When you reach the right-hand side of the image, go back to 0 and increment the y of your window by jump_size.
Neural networks are good for this because the noise isn't a huge issue: you don't need to remove it. It's also good because it can recognize images similar to ones it has seen before, but are slightly different (the face is at a different angle, the lighting is slightly different, etc.).
Of course, the downside is that you need the training data to do it. If you don't have a set of pre-tagged images then you might be out of luck - although if you have a Facebook account you can probably write a script to pull all of yours and your friends' tagged photos and use that.
A FFT does only make sense when you already have sort the image with kd-tree or a hierarchical tree. I would suggest to map the image 2d rgb values to a 1d curve and reducing some complexity before a frequency analysis.
I do not have an exact algorithm to propose because I have found that target detection method depend greatly on the specific situation. Instead, I have some tips and advices. Here is what I would suggest: find a specific characteristic of your target and design your code around it.
For example, if you have access to the color image, use the fact that Wally doesn't have much green and blue color. Subtract the average of blue and green from the red image, you'll have a much better starting point. (Apply the same operation on both the image and the target.) This will not work, though, if the noise is color-dependent (ie: is different on each color).
You could then use correlation on the transformed images with better result. The negative point of correlation is that it will work only with an exact cut-out of the first image... Not very useful if you need to find the target to help you find the target! Instead, I suppose that an averaged version of your target (a combination of many Wally pictures) would work up to some point.
My final advice: In my personal experience of working with noisy images, spectral analysis is usually a good thing because the noise tend to contaminate only one particular scale (which would hopefully be a different scale than Wally's!) In addition, correlation is mathematically equivalent to comparing the spectral characteristic of your image and the target.
I am currently working on a data visualization project.My aim is to produce contour lines ,in other words iso-lines, from gridded data.Data can be temperature, weather data or any kind of other environmental parameters but only condition is it must be regularly spaced.
I searched in internet , however i could not find a good algorithm, pseudo-code or source code for producing contour lines from grids.
Does anybody knows a library, source code or an algorithm for producing contour lines from gridded data?
it will be good if your suggestion has a good run time performance, i don't want to wait my users so much :)
Edit: thanks for response but isolines have some constrains like they should not intersects
so just generating bezier curves does not accomplish my goal.
See this question: How to approximate a vector contour from an elevation raster?
It's a near duplicate, but uses quite different terminology. You'll find that cartography and computer graphics solve many of the same problems, but use different terminology for them.
there's some reasonably good contouring available in GNUplot - if you're able to use GPL code that may help.
If your data is placed at regular intervals, this can be done fairly easily (assuming I understand your problem correctly). First you need to determine at what interval you want your contours. Next create the grid you are going to use to store the contour information (i'm assuming just a simple on/off or elevation at this contour level type of data), which should be one interval smaller than the source data.
Now the trick here is to offset the 2 grids by 1/2 an interval (won't actually show up in code like this, but its the concept I'm dealing with here) and compare the 4 coordinates surrounding the current point in the contour data grid you are calculating. If any of the 4 points are in a different interval range, then that 'pixel' in the contour grid should be set to true (or the value of the contour range being crossed).
With this method, there will be a problem when the interval is too fine which will cause several contours to overlap onto each other.
As the link from Paul Tomblin suggests, Bezier curves (which are a subset of B-splines) are a ripe solution for your problem. If runtime performance is an issue, Bezier curves have the added benefit of being constructable via the very fast de Casteljau algorithm, instead of drawing them according to the parametric equations. On the off chance you're working with DirectX, it has a library function for the de Casteljau, but it should not be challenging to brew one yourself using the 1001 web pages that describe it.