I have taken on a project to automatically analyse images taken from a microscope of a specific type micro fractures. The problem is that the camera used was on an "auto" setting and so the micro fractures (which look like pin pricks) are a variety of shades from one photo to the next.
The background is also at various saturation levels and there are some items (which appear very bright in the photos) which look like fractures but are something different which I need to discount.
Could anyone recommend a technique I could investigate to help me solve this issue?
This is quite a normal situation in image recognition -- different lighting conditions, different orientation of objects, different scale, different image resolution. Methods have been developed to extract useful features out of such images. I am not an expert in that area, but I suspect that any general book on the subject contains at least a brief review of image normalization and feature extraction methods.
If the micro fractures are sharp edge transitions, then a combination of simple techniques may allow you to find connected regions of strong edge points that correspond to those fractures. If the fractures also appear dark, then you should be able to distinguish them from the bright fracture-like features.
Briefly:
Generate an edge map
(If necessary) Remove edge pixels corresponding to bright features.
Select an edge strength that separates the fractures from the background
Clean up the edge map image
Find connected regions in the edge map image
If you want to find thin features with strong edges in a background, then one step could be to generate an edge map (or edge image) in which each pixel represents the local edge strength. A medium gray pixel surrounded by other medium gray pixels would have relatively low edge strength, whereas a black pixel surrounded by light gray pixels would have relatively high edge strength. Various edge-finding techniques include Sobel, Prewitt, Canny, Laplacian, and Laplacian of Gaussian (LoG); I won't describe those here since Wikipedia has entries on them if you're not familiar with them.
Once you have an edge map, you could use a binary threshold to convert the edge map into black and white pixels. If you have evidence that fractures have an edge strength of 20, then you would use the value 20 as a binarization threshold on the image. Binarization will then leave you with a black and white edge map with white pixels for strong edges, and black pixels for the background.
Once you have the binarized edge map, you may need to perform a morphological "close" operation to ensure that white pixels that may be close to one another become part of the same connected region.
Once you've performed a close on the binarized edge map you can search for connected components (which may be called "contours" or "blobs"). For most applications it's better to identify 4-connected regions in which a pixel is considered connect to pixels to the top, left, bottom, and right, but not to its neighbors at the top left and other corners. If the features are typically single-pixel lines or meandering cracks, and if there isn't much noise, then you might be able to get away with identifying 8-connected regions.
Once you've identified connected regions you can filter based on the area, length of the longest axis, and/or other parameters.
If both dark and light features can have strong edges, and if you want to eliminate the bright features, then there are a few ways to eliminate them. In the original image you might clip the image by setting all values over a threshold brightness to that brightness. If the features you want to keep are darker than the median gray value of the image, then you could ignore all pixels brighter than the median gray value. If the background intensity varies widely, you might calculate a median for some local region.
Once we see your images I'm sure you'll get more suggestions. If the problem you're trying to solve turns out to be similar to one I worked on, which was to find cracks in highly textured surfaces, then I can be more specific about algorithms to try.
Related
Hi I am trying to figure out whether edge detection depends on image conditions (features).
I know there is a huge mathematical basis for any edge detection operator. Also I know edge detection is sensitive for a noise on a picture.
What about brightness, contrast? The point is I am looking how to estimate quality of the image. Is quality of image important for edge detection?
Edges are detected where there is a change in pixel value in either x or y direction in an image. The maths behind this is simple differentiation. Any variation or noise that can change the pixel value can reduce the chances of detecting an edge but then morphological operations can help.
For example, blur is one operation that can reduce the image quality by changing the pixel values. Figure 1 represents an image and its edges.. As I have already mentioned edges are detected where the pixel value changes in one direction, you can see the white lines as edges corresponding to these changes in pixel value.
Figure 2 is blurred image of input image, the edges of this blur image is much less than the actual number of edges..
That is just one example, noise while capturing the image, illumination of object or dark object can give different edges. Depending upon how the noise is affecting the image the edges can increase or decrease.
There are some basic methods of detecting edges, I have used canny edge detection. You can refer to review of classic edge detectors to understand them further.
The objective is to display the person on a different background (aka background removal).
I'm using the Kinect with Microsoft's Beta Kinect SDK to do so. With help of the depth, the background is filtered and we get only the image of the person.
This is pretty simple to do, and we can find the code that does that everywhere on the Internet. However, the depth signal is noisy, and we get pixels which do not belong to the person that are displayed.
I applied an edge detector to see if it was useful, and I currently get this:
Here's another without edge detection:
My question is: Which way can I get rid of these noisy white pixels around the person?
I tried morphological operations, but some parts of the body are erased and still leave white pixels behind.
The algorithm doesn't need to be real-time, I can just apply it when I press a 'Save image' button.
Edit 1:
I just tried to do background substraction with the closest frames on the shape border. The single pixels you see are flickering, which means it is noise and I can get easily get rid of them.
Edit 2:
The project is now over, and here's what we did: manual calibration of the Kinect by using the OpenNI driver, which provides directly the infrared image. The result is really good, but each calibration is specific to each Kinect.
Then, we applied a little transparency on the borders, and the result looks really nice! I can't provide pictures, however.
Your problem isn't just the noisy white pixels. You're missing significant parts of the person as well, e.g. part of his right hand. I'd recommend being more conservative with your thresholding of the depth data (allow more false positives). This would give you more noisy pixels, but at least you'd have the person in their entirety.
To get rid of the noisy pixels, I can think of a couple of things:
Feather the outer pixels (reduce them in intensity/increase their transparency if you're using an alpha channel)
Smooth the image, perform the edge detection on the smoothed image, then use these edges with your original sharp image.
Do some skin region detection to mark parts that definitely belong to a person. See skin detection in the YUV color space? and Skin Color Detection
For clothes, work with the hue and saturation image. If you know the color of the t-shirt (or that at least that it's not a neutral color), then this will stand out easily. If you don't know this information, then it may be worth building up a model of the person using the other frames (if there's a big gray blob that's moving around in your video, chances are that your subject is wearing a gray shirt)
The approaches aren't mutually exclusive so it may be worth trying to do them in combination. If I think of anything else, I'll post back here.
If there is no other way of resolving the jitter on the edges you could always try anti-alias as post-process.
I uploaded an example image for better understanding: http://www.imagebanana.com/view/kaja46ko/test.jpg
In the image you can see some scanlines and a marker (the white retangle with the circle in it). I want OpenCV to go along a specified area (in the example outlined trough the scanlines) that should be around 5x5. If that area contains a gradient from black to white, I want OpenCV to save the position of that area, so that I can work with it later.
The final result would be to differentiate between the marker and the other retangles separated trough black and white lines.
Is something like that possible? I googled a lot but I only found edge detectors but that's not what I want, I really need the detection of the black to white gradient only.
Thanks in advance.
it would be a good idea to filter out some of the areas by calculating their histogram.
You can use cvCalcHist for the task, then you can establish some threshold to determine if the black-white pixels percentage corresponds to that of a gradient. This will not solve the task but it will help you in reducing complexity.
Then, you can erode the image to merge all the white areas. After applying threshold, it would be possible to find connected components (using cvFindContours) that will separate images in black zones or white zones. You can then detect gradients by finding 5x5 areas that contain both a piece of a white zone and black zone simultaneously.
hope it helps.
Thanks for your answerer dnul, but it didn't really help me work this out. I though about a histogram to approach the problem but it's not quite what I want.
I solved this problem by creating a 40x40 matrix which holds 5x5 matrix's containing the raw pixel data in all 3 channels. I iterated trough each 40px-area and inside iterated trough each border of 5px-area. I checked each pixel and saved the ones which are darker then a certain threshold a storage.
After the iteration I had a rough idea of how many black pixels their are, so I checked each one of them for neighbors with white-pixels in all 3 channels. I then marked each of those pixels and saved them to another storage.
I then used the ransac algorithm to construct lines out of these points. It constructs about 5-20 lines per marker edge. I then looked at the lines which meet each other and saved the position of those that meet in a square angle.
The 4 points I get from that are the edges of the marker.
If you want to reproduce this you would have to filter the image in beforehand and apply a threshold to make it easier to distinguish between black and white pixels.
A sample picture, save after finding the points and before constructing the lines:
http://www.imagebanana.com/view/i6gfe6qi/9.jpg
What you are describing is edge detection. This is exactly how, say, the Canny edge detector works. It looks for dark pixels near light pixels, and based on a threshold that you pass in (There is also the adaptive canny, which figures out the threshold for you), and sets them to all black or all white (aka 'marks' them).
See here:
http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.html
How to get rid of uneven illumination from images, that contain text data, usually printed but may be handwritten? It can have some spots of lights because the light reflected while making picture.
I've seen the Halcon program's segment_characters function that is doing this work perfectly,
but it is not open source.
I wish to convert an image to the image that has a constant illumination at background and more dark colored regions of text. So that binarization will be easy and without noise.
The text is assumed to be dark colored than it's background.
Any ideas?
Strictly speaking, assuming you have access to the image's pixels (you can search online for how to accomplish this in your programming language as the topic is abundantly available), the exercise involves going over the pixels once to determine a "darkness threshold". In order to do this you convert each pixel from RGB to HSL in order to get the lightness level component for each pixel. During this process you calculate an average lightness for the whole image which you can use as your "darkness threshold"
Once you have the image average lightness level, you can go over the image pixels once more and if a pixel is less than the darkness threshold, set it's color to full white RGB(255,255,255), otherwise, set it's color to full black RGB (0,0,0). This will give you a binary image with in which the text should be black - the rest should be white.
Of course, the key is in finding the appropriate darkness threshold - so if the average method doesn't give you good results you may have to come up with a different method to augment that step. Such a method could involve separating the image in the primary channels Red, Green, Blue and computing the darkness threshold for each channel separately and then using the aggressive threshold of the three..
And lastly, a better approach may be to compute the light levels distribution - as opposed to simply the average - and then from that, the range around the maximum is what you want to keep. Again, go over each pixel and if it's lightness fits the band make it black, otherwise, make it white.
EDIT
For further reading about HSL I recommend starting with the Wiky entry on HSL and HSV Color spaces.
Have you tried using morphological techniques? Closure-by-reconstruction (as presented in Gonzalez, Woods and Eddins) can be used to create a grayscale representation of background illumination levels. You can more-or-less standardize the effective illumination by:
1) Calculating the mean intensity of all the pixels in the image
2) Using closure-by-reconstruction to estimate background illumination levels
3) Subtract the output of (2) from the original image
4) Adding the mean intensity from (1) to every pixel in the output of (3).
Basically what closure-by-reconstruction does is remove all image features that are smaller than a certain size, erasing the "foreground" (the text you want to capture) and leaving only the "background" (illumination levels) behind. Subtracting the result from the original image leaves behind only small-scale deviations (the text). Adding the original average intensity to those deviations is simply to make the text readable, so that the resulting picture looks like a light-normalized version of the original image.
Use Local-Thresholding instead of the global thresholding algorithm.
Divide your image(grayscale) in to a grid of smaller images (say 50x50 px) and apply the thresholding algorithm on each individual image.
If the background features are generally larger than the letters, you can try to estimate and subsequently remove the background.
There are many ways to do that, a very simple one would be to run a median filter on your image. You want the filter window to be large enough that text inside the window rarely makes up more than a third of the pixels, but small enough that there are several windows that fit into the bright spots. This filter should result in an image without text, but with background only. Subtract that from the original, and you should have an image that can be segmented with a global threshold.
Note that if the bright spots are much smaller than the text, you do the inverse: choose the filter window such that it removes the light only.
The first thing you need to try and do it change the lighting, use a dome light or some other light that will give you a more diffuse and even light.
If that's not possible, you can try some of the ideas in this question or this one. You want to implement some type of "adaptive threshold", this will apply a local threshold to individual parts of the image so that the change in contrast won't be as noticable.
There is also a simple but effective method explained here. The simple outline of the alrithm is the following:
Split the image up into NxN regions or neighbourhoods
Calculate the mean or median pixel value for the neighbourhood
Threshold the region based on the value calculated in 2) or the value from 2) minus C (where C is a chosen constant)
It seems like what you're trying to do is improve local contrast while attenuating larger scale lighting variations. I'll agree with other posters that optimizing the image through better lighting should always be the first move.
After that, here are two tricks.
1) Use smooth_image() operator to convolve a gaussian on your original image. Use a relaitively large kernel, like 20-50px. Then subtract this blurred image from your original image. Apply scale and offset within sub_image() operator, or use equ_histo() to equalize histogram.
This basically subtracts the low spatial frequency information from the original, leaving the higher frequency information intact.
2) You could try highpass_image() operator, or one of the laplacian operators to extract a gradiant image.
I am trying to teach my camera to be a scanner: I take pictures of printed text and then convert them to bitmaps (and then to djvu and OCR'ed). I need to compute a threshold for which pixels should be white and which black, but I'm stymied by uneven illumination. For example if the pixels in the center are dark enough, I'm likely to wind up with a bunch of black pixels in the corners.
What I would like to do, under relatively simple assumptions, is compensate for uneven illumination before thresholding. More precisely:
Assume one or two light sources, maybe one with gradual change in light intensity across the surface (ambient light) and another with an inverse square (direct light).
Assume that the white parts of the paper all have the same reflectivity/albedo/whatever.
Find some algorithm to estimate degree of illumination at each pixel, and from that recover the reflectivity of each pixel.
From a pixel's reflectivity, classify it white or black
I have no idea how to write an algorithm to do this. I don't want to fall back on least-squares fitting since I'd somehow like to ignore the dark pixels when estimating illumination. I also don't know if the algorithm will work.
All helpful advice will be upvoted!
EDIT: I've definitely considered chopping the image into pieces that are large enough so they still look like "text on a white background" but small enough so that illumination of a single piece is more or less even. I think if I then interpolate the thresholds so that there's no discontinuity across sub-image boundaries, I will probably get something halfway decent. This is a good suggestion, and I will have to give it a try, but it still leaves me with the problem of where to draw the line between white and black. More thoughts?
EDIT: Here are some screen dumps from GIMP showing different histograms and the "best" threshold value (chosen by hand) for each histogram. In two of the three a single threshold for the whole image is good enough. In the third, however, the upper left corner really needs a different threshold:
I'm not sure if you still need a solution after all this time, but if you still do. A few years ago I and my team photographed about 250,000 pages with a camera and converted them to (almost black and white ) grey scale images which we then DjVued ( also make pdfs of).
(See The catalogue and complete collection of photographic facsimiles of the 1144 paper transcripts of the French Institute of Pondicherry.)
We also ran into the problem of uneven illumination. We came up with a simple unsophisticated solution which worked very well in practice. This solution should also work to create black and white images rather than grey scale (as I'll describe).
The camera and lighting setup
a) We taped an empty picture frame to the top of a table to keep our pages in the exact same position.
b) We put a camera on a tripod also on top of the table above and pointing down at the taped picture frame and on a bar about a foot wide attached to the external flash holder on top of the camera we attached two "modelling lights". These can be purchased at any good camera shop. They are designed to provide even illumination. The camera was shaded from the lights by putting small cardboard box around each modelling light. We photographed in greyscale which we then further processed. (Our pages were old browned paper with blue ink writing so your case should be simpler).
Processing of the images
We used the free software package irfanview.
This software has a batch mode which can simultaneously do color correction, change the bit depth and crop the images. We would take the photograph of a page and then in interactive mode adjust the brightness, contrast and gamma settings till it was close to black and white. (We used greyscale but by setting the bit depth to 2 you will get black and white when you batch process all the pages.)
After determining the best color correction we then interactively cropped a single image and noted the cropping settings. We then set all these settings in the batch mode window and processed the pages for one book.
Creating DjVu images.
We used the free DjVu Solo 3.1 to create the DjVu images. This has several modes to create the DjVu images. The mode which creates black and white images didn't work well for us for photographs, but the "photo" mode did.
We didn't OCR (since the images were handwritten Sanskrit) but as long as the letters are evenly illuminated I think your OCR software should ignore big black areas like between a two page spread. But you can always get rid of the black between a two page spread or at the edges by cropping the pages twices once for the left hand pages and once for the right hand pages and the irfanview software will allow you to cleverly number your pages so you can then remerge the pages in the correct order. I.e rename your pages something like page-xxxA for lefthand pages and page-xxxB for righthand pages and the pages will then sort correctly on name.
If you still need a solution I hope some of the above is useful to you.
i would recommend calibrating the camera. considering that your lighting setup is fixed (that is the lights do not move between pictures), and your camera is grayscale (not color).
take a picture of a white sheet of paper which covers the whole workable area of your "scanner". store this picture, it tells what is white paper for each pixel. now, when you take take a picture of a document to scan, you can reload your "white reference picture" and even the illumination before performing a threshold.
let's call the white reference REF, the picture DOC, the even illumination picture EVEN, and the maximum value of a pixel MAX (for 8bit imaging, it is 255). for each pixel:
EVEN = DOC * (MAX/REF)
notes:
beware of the parenthesis: most image processing library uses the image pixel type for performing computation on pixel values and a simple multiplication will overload your pixel. eventually, write the loop yourself and use a 32 bit integer for intermediate computations.
the white reference image can be smoothed before being used in the process. any smoothing or blurring filter will do, and don't hesitate to apply it aggressively.
the MAX value in the formula above represents the target pixel value in the resulting image. using the maximum pixel value targets a bright white, but you can adjust this value to target a lighter gray.
Well. Usually the image processing I do is highly time sensitive, so a complex algorithm like the one you're seeking wouldn't work. But . . . have you considered chopping the image up into smaller pieces, and re-scaling each sub-image? That should make the 'dark' pixels stand out fairly well even in an image of variable lighting conditions (I am assuming here that you are talking about a standard mostly-white page with dark text.)
Its a cheat, but a lot easier than the 'right' way you're suggesting.
This might be horrendously slow, but what I'd recommend is to break the scanned surface into quarters/16ths and re-color them so that the average grayscale level is similar across the page. (Might break if you have pages with large margins though)
I assume that you are taking images of (relatively) small black letters on a white background.
One approach could be to "remove" the small black objects, while keeping the illumination variations of the background. This gives an estimate of how the image is illuminated, which can be used for normalizing the original image. It is often enough to subtract the illumination estimate from the original image and then do a threshold based segmentation.
This approach is based on gray scale morphological filters, and could be implemented in matlab like below:
img = imread('filename.png');
illumination = imclose(img, strel('disk', 10));
imgCorrected = img - illumination;
thresholdValue = graythresh(imgCorrected);
bw = imgCorrected > thresholdValue;
For an example with real images take a look at this guide from mathworks. For further reading about the use of morphological image analysis this book by Pierre Soille can be recommended.
Two algorithms come to my mind:
High-pass to alleviate the low-frequency illumination gradient
Local threshold with an appropriate radius
Adaptive thresholding is the keyword. Quote from a 2003 article by R.
Fisher, S. Perkins, A. Walker, and E. Wolfart: “This more sophisticated version
of thresholding can accommodate changing lighting conditions in the image, e.g.
those occurring as a result of a strong illumination gradient or shadows.”
ImageMagick's -lat option can do it, for example:
convert -lat 50x50-2000 input.jpg output.jpg
input.jpg
output.jpg
You could try using an edge detection filter, then a floodfill algorithm, to distinguish the background from the foreground. Interpolate the floodfilled region to determine the local illumination; you may also be able to modify the floodfill algorithm to use the local background value to jump across lines and fill boxes and so forth.
You could also try a Threshold Hysteresis with a rate of change control. Here is the link to the normal Threshold Hysteresis. Set the first threshold to a typical white value. Set the second threshold to less than the lowest white value in the corners.
The difference is that you want to check the difference between pixels for all values in between the first and second threshold. Ideally if the difference is positive, then act normally. But if it is negative, you only want to threshold if the difference is small.
This will be able to compensate for lighting variations, but will ignore the large changes between the background and the text.
Why don't you use simple opening and closing operations?
Try this, just lool at the results:
src - cource image
src - open(src)
close(src) - src
and look at the close - src result
using different window size, you will get backgound of the image.
I think this helps.