I want know what could be the various ways to compare the same image enhanced by various image enhancement techniques not visually but mathematically?
For example: (i) May be (I am not sure) one could look at their histograms and calculate the variance of them. One with the highest variance might be the best technique? or
(ii) Randomly, pick a local region in all the enhanced images and compute again variance or look at the difference of the max. and min. values of that local region. One with highest variance or difference might be the best?
Thanks a lot.
It really depends on the sort of enhancement you are looking at.
For example, for the likes of denoising and deblurring, the PSNR and MSE might be appropriate, especially when you have access to groundtruth images which you can compare the enhanced image against.
Aesthetic enhancement on the other hand might be harder to quantify as it requires a certain degree of subjectivity. A highly cited work in this area is:
Studying Aesthetic in Photographic Images Using a Computational Approach
You can check out the citations therein for relevant references.
Two common metrics for comparing images are mean square error (MSE) and peak signal to noise ratio (PSNR).
Related
I'm trying to develop a way to count the number of bright spots in an image. The spots should be gaussian point sources, but there is a lot of noise. There are probably on the order of 10-20 actual point sources in this image. My first though was to use a gaussian convolution with sigma = 15, which seems to do a good job.
First, is there a better way to isolate these bright spots?
Second, how can I 'detect' the bright spots, i.e. count them? I haven't had any luck with circular hough transforms from opencv.
Edit: Here is the original without gridlines, here is the convolved image without gridlines.
I am working with thermal infrared images which subject to quantity of noises.
I found that low rank based approaches such as approaches based on Singular Value Decomposition (SVD) or Weighted Nuclear Norm Metric (WNNM) give very efficient result in terms of reducing the noise while preserving the structure of the information.
Their main drawback is the fact they are quite slow to compute (several minutes per image)
Here is some litterature:
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7067415
https://arxiv.org/abs/1705.09912
The second paper has some MatLab code available, there is quite a lot of files but the translation to python is should not that complex.
OpenCV implement as well (and it is available in python) a very efficient algorithm on the Non-Local Means algorithm:
https://docs.opencv.org/master/d5/d69/tutorial_py_non_local_means.html
I'm a newbie for machine learning, and I have following question. Suppose that I have implemented a classification algorithm on some data, and recognized the best combination of features for the classification algorithm. If someday I get data from same resource, which lack the target feature in previous classification task, Can I use the best combination of features for classification directly to clustering task? (I know I can use the model I trained to predict the target of data, but I just want to know whether the best combination of features is same between classification and clustering algorithms)
I have searched websites and any resource I know, but I can't find the answer for my question, Could somebody tell me or just give me a link? Thanks!
I would say yes, provided the nature of the target is the same in both cases. What we want ideally is a tractable number of features which are orthogonal (perpendicular) to each other in N space, so that each can contribute maximally to the prediction.
Take a concrete example, that of T shirts and whether they are Large size or Small size. You are given data which shows that in the manufacturing process there is a bit of material shrinkage which means the T shirts come out a bit irregular, and the shrinkage varies between the height and width, but not much. The data shows height, width and colour and you want to decide if they are in the large group or the small. You find that the height and width are important but the colour is not, so you decide to go with the height and width as your classification features.
The important point is that these two features have been identified as the most orthogonal to each other, which should apply in a classification or clustering context. The number of clusters remains a factor to be examined.
It may not be good enough.
For example a decision tree or random forest can be analyzed to get the importance of features. But this will not tell you what kind of preprocessing (in particular scaling and weighting) is necessary to be able to cluster them (in particular, categorical features are difficult to use, anything that is not continuous or that is skewed is hard).
Furthermore, data tends to change over time. Features that were important once (e.g. Facebook likes) are useless now.
I have images of mosquitos similar to these ones and I would like to automatically circle around the head of each mosquito in the images. They are obviously in different orientations and there are random number of them in different images. some error is fine. Any ideas of algorithms to do this?
This problem resembles a face detection problem, so you could try a naïve approach first and refine it if necessary.
First you would need to recreate your training set. For this you would like to extract small images with examples of what is a mosquito head or what is not.
Then you can use those images to train a classification algorithm, be careful to have a balanced training set, since if your data is skewed to one class it would hit the performance of the algorithm. Since images are 2D and algorithms usually just take 1D arrays as input, you will need to arrange your images to that format as well (for instance: http://en.wikipedia.org/wiki/Row-major_order).
I normally use support vector machines, but other algorithms such as logistic regression could make the trick too. If you decide to use support vector machines I strongly recommend you to check libsvm (http://www.csie.ntu.edu.tw/~cjlin/libsvm/), since it's a very mature library with bindings to several programming languages. Also they have a very easy to follow guide targeted to beginners (http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf).
If you have enough data, you should be able to avoid tolerance to orientation. If you don't have enough data, then you could create more training rows with some samples rotated, so you would have a more representative training set.
As for the prediction what you could do is given an image, cut it using a grid where each cell has the same dimension that the ones you used on your training set. Then you pass each of this image to the classifier and mark those squares where the classifier gave you a positive output. If you really need circles then take the center of the given square and the radius would be the half of the square side size (sorry for stating the obvious).
So after you do this you might have problems with sizes (some mosquitos might appear closer to the camera than others) , since we are not trained the algorithm to be tolerant to scale. Moreover, even with all mosquitos in the same scale, we still might miss some of them just because they didn't fit in our grid perfectly. To address this, we will need to repeat this procedure (grid cut and predict) rescaling the given image to different sizes. How many sizes? well here you would have to determine that through experimentation.
This approach is sensitive to the size of the "window" that you are using, that is also something I would recommend you to experiment with.
There are some research may be useful:
A Multistep Approach for Shape Similarity Search in Image Databases
Representation and Detection of Shapes in Images
From the pictures you provided this seems to be an extremely hard image recognition problem, and I doubt you will get anywhere near acceptable recognition rates.
I would recommend a simpler approach:
First, if you have any control over the images, separate the mosquitoes before taking the picture, and use a white unmarked underground, perhaps even something illuminated from below. This will make separating the mosquitoes much easier.
Then threshold the image. For example here i did a quick try taking the red channel, then substracting the blue channel*5, then applying a threshold of 80:
Use morphological dilation and erosion to get rid of the small leg structures.
Identify blobs of the right size to be moquitoes by Connected Component Labeling. If a blob is large enough to be two mosquitoes, cut it out, and apply some more dilation/erosion to it.
Once you have a single blob like this
you can find the direction of the body using Principal Component Analysis. The head should be the part of the body where the cross-section is the thickest.
For my final year project i'l be taking the photographs from the mobile phone and then will be computing the image processing steps. I will the taking the images under various illumination conditions (natural light, poor lightning conditions and so on). Does any one knows any algorithm that I can use to compute it?
Thanks a lot
Good whitebalancing is still an active field of research I guess. From your question, it is hard to tell how "advanced" the sought solution is supposed to be and what you need exactly.
In some other context, I recently encountered this paper. They have a quite complicated approach for Whitebalancing and produce good results:
Hsu, Mertens, Paris, Avidan, Durand. "Light mixture estimation for spatially varying white balance". In: ACM Transactions on Graphics, 2008
Check the related work section for more hints, as usual.
If you are less interested in whitebalancing but rather require to process the images further (sounds a bit like that in your comment), you should possibly aim for techniques that are rather invariant to illumination - or at least robust against changes in illumination. E.g. transforming your image in any colorspace that separates the brightness/luminance (i.e. YUV, HSV) might help, depending on your actual problem. From my experience and intuition, I would suggest that in most cases it is better to make your "recognition"-algorithm robust agains changes in illumination - rather than correcting the illumination first.
One very simple method is to take the mean pixel value of an image, adjust the exposure, take another picture and compute the mean again, continuing until the mean reaches some arbitrary value.
Try the simplest method: histogram equalization first.
I want to develop an application in which user input an image (of a person), a system should be able to identify face from an image of a person. System also works if there are more than one persons in an image.
I need a logic, I dont have any idea how can work on image pixel data in such a manner that it identifies person faces.
Eigenface might be a good algorithm to start with if you're looking to build a system for educational purposes, since it's relatively simple and serves as the starting point for a lot of other algorithms in the field. Basically what you do is take a bunch of face images (training data), switch them to grayscale if they're RGB, resize them so that every image has the same dimensions, make the images into vectors by stacking the columns of the images (which are now 2D matrices) on top of each other, compute the mean of every pixel value in all the images, and subtract that value from every entry in the matrix so that the component vectors won't be affine. Once that's done, you compute the covariance matrix of the result, solve for its eigenvalues and eigenvectors, and find the principal components. These components will serve as the basis for a vector space, and together describe the most significant ways in which face images differ from one another.
Once you've done that, you can compute a similarity score for a new face image by converting it into a face vector, projecting into the new vector space, and computing the linear distance between it and other projected face vectors.
If you decide to go this route, be careful to choose face images that were taken under an appropriate range of lighting conditions and pose angles. Those two factors play a huge role in how well your system will perform when presented with new faces. If the training gallery doesn't account for the properties of a probe image, you're going to get nonsense results. (I once trained an eigenface system on random pictures pulled down from the internet, and it gave me Bill Clinton as the strongest match for a picture of Elizabeth II, even though there was another picture of the Queen in the gallery. They both had white hair, were facing in the same direction, and were photographed under similar lighting conditions, and that was good enough for the computer.)
If you want to pull faces from multiple people in the same image, you're going to need a full system to detect faces, pull them into separate files, and preprocess them so that they're comparable with other faces drawn from other pictures. Those are all huge subjects in their own right. I've seen some good work done by people using skin color and texture-based methods to cut out image components that aren't faces, but these are also highly subject to variations in training data. Color casting is particularly hard to control, which is why grayscale conversion and/or wavelet representations of images are popular.
Machine learning is the keystone of many important processes in an FR system, so I can't stress the importance of good training data enough. There are a bunch of learning algorithms out there, but the most important one in my view is the naive Bayes classifier; the other methods converge on Bayes as the size of the training dataset increases, so you only need to get fancy if you plan to work with smaller datasets. Just remember that the quality of your training data will make or break the system as a whole, and as long as it's solid, you can pick whatever trees you like from the forest of algorithms that have been written to support the enterprise.
EDIT: A good sanity check for your training data is to compute average faces for your probe and gallery images. (This is exactly what it sounds like; after controlling for image size, take the sum of the RGB channels for every image and divide each pixel by the number of images.) The better your preprocessing, the more human the average faces will look. If the two average faces look like different people -- different gender, ethnicity, hair color, whatever -- that's a warning sign that your training data may not be appropriate for what you have in mind.
Have a look at the Face Recognition Hompage - there are algorithms, papers, and even some source code.
There are many many different alghorithms out there. Basically what you are looking for is "computer vision". We had made a project in university based around facial recognition and detection. What you need to do is google extensively and try to understand all this stuff. There is a bit of mathematics involved so be prepared. First go to wikipedia. Then you will want to search for pdf publications of specific algorithms.
You can go a hard way - write an implementaion of all alghorithms by yourself. Or easy way - use some computer vision library like OpenCV or OpenVIDIA.
And actually it is not that hard to make something that will work. So be brave. A lot harder is to make a software that will work under different and constantly varying conditions. And that is where google won't help you. But I suppose you don't want to go that deep.