SHORT: is there a function in OpenCV or a general algorithm which could return an index for image homogenity?
LONG VERSION :-) I am implementing auto-focus based on image-data evaluation. My images are biological cells, which are spread fairly in similar density across the image area. Unfortunatelly, sometimes my algorithm is disturbed by dirt on the cover glass, which are mostly a few bright spots. So my idea is, to discard focus-function peaks caused by inhomogenious images.
Thank you for any suggestions!
Example images as requested: (not the best ones, but should fairly show the problem)
The left image captured at wrong Z-position because of dirt. The right one is OK.
Looking at the image, you could split it up in different parts (say 4x4 subimages), compute variance in each sub image, and see if the difference between lowest and highest variance is big.
Related
I'm writing software for a solar panel inspection system and need to stitch camera images taken in a electroluminescence machine together. These images have only very few features and low contrast (as shown below) and the OpenCV image stitcher does not work by itself.
The approximate overlapping area of both images is known but I need the result as accurate as possible. I tried shifting one image over the other and computed different distance measures over the ROI, but without satisfying results. The SSD distance does not work due to vignetting and differences in the pixel intensity. Normalized Gradients or Cross Correlation were not robust either.
Any idea how to preprocess the images for the stitcher to work? Or is there another way to tackle this? Dark cells are not always present, making them not reliable as a feature.
There are vertical cell edges (nearly invisible due to bad contrast), but I have no method of detecting them either. If these could be detected, I could align the images with the cell edges.
Any help is much appreciated.
I was wondering if its possible to match the exposure across a set of images.
For example, lets say you have 5 images that were taken at different angles. Images 1-3,5 are taken with the same exposure whilst the 4th image have a slightly darker exposure. When I then try to combine these into a cylindrical panorama using (seamFinder with: gc_color, surf detection, MULTI_BAND blending,Wave correction, etc.) the result turns out with a big shadow in the middle due to the darkness from image 4.
I've also tried using exposureCompensator without luck.
Since I'm taking the pictures in iOS, I maybe could increase exposure manually when needed? But this doesn't seem optimal..
Have anyone else dealt with this problem?
This method is probably overkill (and not just a little) but the current state-of-the-art method for ensuring color consistency between different images is presented in this article from HaCohen et al.
Their algorithm can correct a wide range of errors in image sets. I have implemented and tested it on datasets with large errors and it performs very well.
But, once again, I suppose this is way overkill for panorama stitching.
Sunreef has provided a very good paper, but it does seem overkill because of the complexity of a possible implementation.
What you want to do is to equalize the exposure not on the entire images, but on the overlapping zones. If the histograms of the overlapped zones match, it is a good indicator that the images have similar brightness and exposure conditions. Since you are doing more than 1 stitch, you may require a global equalization in order to make all the images look similar, and then only equalize them using either a weighted equalization on the overlapped region or a quadratic optimiser (which is again overkill if you are not a professional photographer). OpenCV has a simple implmentation of a simple equalization compensation algorithm.
The detail::ExposureCompensator class of OpenCV (sample implementation of such a stitiching is here) would be ideal for you to use.
Just create a compensator (try the 2 different types of compensation: GAIN and GAIN_BLOCKS)
Feed the images into the compensator, based on where their top-left cornes lie (in the stitched image) along with a mask (which can be either completely white or white only in the overlapped region).
Apply compensation on each individual image and iteratively check the results.
I don't know any way to do this in iOS, just OpenCV.
I would like to know if it is possible to take low resolution image from street camera, increase it
and see image details (for example a face, or car plate number). Is there any software that is able to do it?
Thank you.
example of image: http://imgur.com/9Jv7Wid
Possible? Yes. In existence? not to my knowledge.
What you are referring to is called super-resolution. The way it works, in theory, is that you combine multiple low resolution images, and then combine them to create a high-resolution image.
The way this works is that you essentially map each image onto all the others to form a stack, where the target portion of the image is all the same. This gets extremely complicated extremely fast as any distortion (e.g. movement of the target) will cause the images to differ dramatically, on the pixel level.
But, let's you have the images stacked and have removed the non-relevant pixels from the stack of images. You are left hopefully with a movie/stack of images that all show the exact same image, but with sub-pixel distortions. A sub-pixel distortion simply means that the target has moved somewhere inside the pixel, or has moved partially into the neighboring pixel.
You can't measure if the target has moved within the pixel, but you can detect if the target has moved partially into a neighboring pixel. You can do this by knowing that the target is going to give off X amount of photons, so if you see 1/4 of the photons in one pixel and 3/4 of the photons in the neighboring pixel you know it's approximate location, which is 3/4 in one pixel and 1/4 in the other. You then construct an image that has a resolution of these sub-pixels and place these sub-pixels in their proper place.
All of this gets very computationally intensive, and sometimes the images are just too low-resolution and have too much distortion from image to image to even create a meaningful stack of images. I did read a paper about a lab in a university being able to create high-resolution images form low-resolution images, but it was a very very tightly controlled experiment, where they moved the target precisely X amount from image to image and had a very precise camera (probably scientific grade, which is far more sensitive than any commercial grade security camera).
In essence to do this in the real world reliably you need to set up cameras in a very precise way and they need to be very accurate in a particular way, which is going to be expensive, so you are better off just putting in a better camera than relying on this very imprecise technique.
Actually it is possible to do super-resolution (SR) out of even a single low-resolution (LR) image! So you don't have to hassle taking many LR images with sub-pixel shifts to achieve that. The intuition behind such techniques is that natural scenes are full of many repettitive patterns that can be use to enahance the frequency content of similar patches (e.g. you can implement dictionary learning in your SR reconstruction technique to generate the high-resolution version). Sure the enhancment may not be as good as using many LR images but such technique is simpler and more practicle.
Photoshop would be your best bet. But know that you cannot reliably inclrease the size of an image without making the quality even worse.
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.
I implemented some adaptive binarization methods, they use a small window and at each pixel the threshold value is calculated. There are problems with these methods:
If we select the window size too small we will get this effect (I think the reason is because of window size is small)
(source: piccy.info)
At the left upper corner there is an original image, right upper corner - global threshold result. Bottom left - example of dividing image to some parts (but I am talking about analyzing image's pixel small surrounding, for example window of size 10X10).
So you can see the result of such algorithms at the bottom right picture, we got a black area, but it must be white.
Does anybody know how to improve an algorithm to solve this problem?
There shpuld be quite a lot of research going on in this area, but unfortunately I have no good links to give.
An idea, which might work but I have not tested, is to try to estimate the lighting variations and then remove that before thresholding (which is a better term than "binarization").
The problem is then moved from adaptive thresholding to finding a good lighting model.
If you know anything about the light sources then you could of course build a model from that.
Otherwise a quick hack that might work is to apply a really heavy low pass filter to your image (blur it) and then use that as your lighting model. Then create a difference image between the original and the blurred version, and threshold that.
EDIT: After quick testing, it appears that my "quick hack" is not really going to work at all. After thinking about it I am not very surprised either :)
I = someImage
Ib = blur(I, 'a lot!')
Idiff = I - Idiff
It = threshold(Idiff, 'some global threshold')
EDIT 2
Got one other idea which could work depending on how your images are generated.
Try estimating the lighting model from the first few rows in the image:
Take the first N rows in the image
Create a mean row from the N collected rows. You know have one row as your background model.
For each row in the image subtract the background model row (the mean row).
Threshold the resulting image.
Unfortunately I am at home without any good tools to test this.
It looks like you're doing adaptive thresholding wrong. Your images look as if you divided your image into small blocks, calculated a threshold for each block and applied that threshold to the whole block. That would explain the "box" artifacts. Usually, adaptive thresholding means finding a threshold for each pixel separately, with a separate window centered around the pixel.
Another suggestion would be to build a global model for your lighting: In your sample image, I'm pretty sure you could fit a plane (in X/Y/Brightness space) to the image using least-squares, then separate the pixels into pixels brighter (foreground) and darker than that plane (background). You can then fit separate planes to the background and foreground pixels, threshold using the mean between these planes again and improve the segmentation iteratively. How well that would work in practice depends on how well your lightning can be modeled with a linear model.
If the actual objects you try to segment are "thinner" (you said something about barcodes in a comment), you could try a simple opening/closing operation the get a lighting model. (i.e. close the image to remove the foreground pixels, then use [closed image+X] as threshold).
Or, you could try mean-shift filtering to get the foreground and background pixels to the same brightness. (Personally, I'd try that one first)
You have very non-uniform illumination and fairly large object (thus, no universal easy way to extract the background and correct the non-uniformity). This basically means you can not use global thresholding at all, you need adaptive thresholding.
You want to try Niblack binarization. Matlab code is available here
http://www.uio.no/studier/emner/matnat/ifi/INF3300/h06/undervisningsmateriale/week-36-2006-solution.pdf (page 4).
There are two parameters you'll have to tune by hand: window size (N in the above code) and weight.
Try to apply a local adaptive threshold using this procedure:
convolve the image with a mean or median filter
subtract the original image from the convolved one
threshold the difference image
The local adaptive threshold method selects an individual threshold for each pixel.
I'm using this approach extensively and it's working fine with images having non uniform background.