I'm making an image processing project which has a 6-step algorithm and I'm stuck in one of these.
First off all, the platform I using is MATLAB, so if you can supply some samples it would be great. But if you don't want to write code samples, please just give me some hints, techniques or etc.
Let me explain my problem. I've segmented a .jpg image and cut out some areas of it. Then I save the result as .png using a mask. The result is like that (The black part is actually transparent, I made it black to see better the problem);
As you may see in the picture, there is some irrelevant areas. I need to get rid of these irrelevant areas. Because I want the foreground as much as smooth. At the first sight, I applied Gaussian blur to the mask and save the image as .png, again. But the result isn't satisfying as you can imagine. I suppose this situation is needed more solid solution than I have tried.
Edit1: I used spectral matting. But it doesn't help. The best result that I can receive is like that;
As you can see there is some problems on the face and lots of problems on the bottom side of the picture. I guess I need kind of edge fixer or edge smoother for the first image above and it should be faster than matting.
Any MATLAB code samples, technique and approach would be great. If you need further explanation, feel free to ask.
You do not want just to "Gauss-blur" the result, you want soft segmentation a.k.a matting. As a first stop for image matting I would recommend Levine Rav-Acha and Lischinski Spectral Matting. You'll find some Matlab code there (I used it in the past - very impressive results).
Related
I was wondering if someone can provide me a guideline to detect if a person in a picture is bald or not, or even better, how much hair s\he has.
So far I tried to detect the face and the eyes position. From that information, I roughly estimate the forehead and bald area by cutting the area above the eyes as high as some portion of the face.
Then I extract HOG features and train the system with bald and not-bald images using SVM.
Now when I'm looking at the test results, I see some pictures classified as bald but some of them actually have blonde hair or long forehead that hair is not visible after the cutting process. I'm using MATLAB for these operations.
So I know the method seems to be a bit naive, but can you suggest a way of finding out the bald area or extracting the hair, if exists. What method would be the most appropriate for that kind of problem?
very general, so answer is general unless further info provided
Use Computer Vision (e.g MATLAB Computer Vision toolkit) to detect face/head
head has analogies (for human faces), using these one can get the area of the head where hair or baldness is (it seems you already have these)
Calculate the (probabilistic color space model) range where the skin of the person lies (most peorple have similar skin collor space range)
Calculate percentage of skin versus other color (meaning hair) in that area
You have it!
To estimate a skin color model check following papers:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.8637&rep=rep1&type=pdf
http://infoscience.epfl.ch/record/135966
http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569293757.pdf
Link
If an area does not fit well with skin model it can be taken as non-skin (meaning hair, assuming no hats etc are present in samples)
Head region is very small, hence, using HOG for classification doesn't make much sense.
You can use prior information - like detect faces; baldness/hair is certain to be found on the area above the face. Also, use some denser feature descriptors.
You are probably ending up with very sparse representation or equivalently less information because of which your classifier is not able to classify correctly.
I have a image with noise. i want to remove all background variation from an image and want a plain image .My image is a retinal image and i want only the blood vessel and the retinal ring to remain how do i do it? 1 image is my original image and 2 image is how i want it to be.
this is my convoluted image with noise
There are multiple approaches for blood vessel extraction in retina images.
You can find a thorough overview of different approaches in Review of Blood Vessel Extraction Techniques and Algorithms. It covers prominent works of many approache.
As Martin mentioned, we have the Hessian-based Multiscale Vessel Enhancement Filtering by Frangi et al. which has been shown to work well for many vessel-like structures both in 2D and 3D. There is a Matlab implementation, FrangiFilter2D, that works on 2D vessel images. The overview fails to mention Frangi but cover other works that use Hessian-based methods. I would still recommend trying Frangi's vesselness approach since it is both powerful and simple.
Aside from the Hesisan-based methods, I would recommend looking into morphology-based methods since Matlab provides a good base for morphological operations. One such method is presented in An Automatic Hybrid Method for Retinal Blood Vessel Extraction. It uses a morphological approach with openings/closings together with the top-hat transform. It then complements the morphological approach with fuzzy clustering and some post processing. I haven't tried to reproduce their method, but the results look solid and the paper is freely available online.
This is not an easy task.
Detecting boundary of blood vessals - try edge( I, 'canny' ) and play with the threshold parameters to see what you can get.
A more advanced option is to use this method for detecting faint curves in noisy images.
Once you have reasonably good edges of the blood vessals you can do the segmentation using watershed / NCuts or boundary-sensitive version of meanshift.
Some pointers:
- the blood vessals seems to have relatively the same thickness, much like text strokes. Would you consider using Stroke Width Transform (SWT) to identify them? A mex implementation of SWT can be found here.
- If you have reasonably good boundaries, you can consider this approach for segmentation.
Good luck.
I think you'll be more served using a filter based on tubes. There is a filter available which is based on the work done by a man called Frangi, and the filter is often dubbed the Frangi filter. This can help you with identifying the vasculature in the retina. The filter is already written for Matlab and a public version is available here. If you would like to read about the underlying research search for: 'Multiscale vessel enhancement', by Frangi (1998). Another group who's done work in the same field are Sato et.al.
Sorry for the lack of a link in the last one, I could only find payed sites for looking at the research paper on this computer.
Hope this helps
Here is what I will do. Basically traditional image arithmetic to extract background and them subtract it from input image. This will give you the desired result without background. Below are the steps:
Use a median filter with large kernel as the first step. This will estimate the background.
Divide the input image with the output of step 1 [You may have to shift the denominator a little (+1) ] to avoid divide by 0.
Do the quantization to 8 or n bit integer based on what bit the original image is.
The output of step 3 above is the background. Subtract it from original image, to get the desired result. This clips all the negative values as well.
At image i need find "table" - simple rectangle.
Problem is with edge recognition, because potencial photos will be "dark".
I tried edge - sobel, canny, log, .... - recognition and after that Hough transformation and line finding. But this algorithms are not enough for this task.
Something what can help me:
- it is rectangle!, only in perspective view (something like fitting perspective rectangle?)
- that object MUST cover atleast for example 90% of photo (i know i need looking near photo edges)
- that rectangle have fast same color (for example wood dining table)
- i need find atleast "only" 4 corners..(but yes, better will be find the edges of that table)
I know how for example sobel, canny or log algorithms works and Hough as well. And naturally those algorithms fail at dark or non-contrast images. But is there some another method for example based at "fitting"?
Images showing photo i can get (you see it would be dark) and what i need find:
and this is really "nice" picture (without noise). I tested it on more noise pictures and the result was..simply horrible..
Result of this picture with actual algorithm log (with another ones it looks same):
I know image and edge recognition is not simple challenge but are there some new better methods or something like that what i can try to use?
In one of posts in here i found LSD algorithm. It seems very nice descripted and it seems it is recognizing really nice straight lines as well. Do you think it would be better to use it insted of the canny or sobel detection?
Another solution will be corner detection, on my sample images it works better but it recognize too much points and there will problem with time..i will need to connect all the points and "find" the table..
Another solution:
I thought about point to point mapping. That i will have some "virtual" table and try to map that table above with that "virtual" table (simple 2d square in painting :] )..But i think point to point mapping will give me big errors or it will not working.
Does someone have any advice what algorithm use to?
I tried recognize edges in FIJI and then put the edge detected image in matlab, but with hough it works bad as well..:/..
What do you think it would be best to use? In short i need find some algorithm working on non contrast, dark images.
I'd try some modified snakes algorithm:
you parameterize your rectangle with 4 points and initialize them somewhere in the image corners. Then you move the points towards image features using some optimization algorithm (e.g. gradient descent, simulated annealing, etc.).
The image features could be a combination of edge features (e.g. sobel directly or sobel of some gaussian filtered image) to be evaluated on the lines between those four points and corner features to be evaluated at those 4 points.
Additionally you can penalize unlikely rectangles (maybe depending on the angles between the points or on the distance to the image boundary).
I am going through feature detection algorithms and a lot of things seems to be unclear. The original paper is quite complicated to understand for beginners in image processing. Shall be glad if these are answered
What are the features which are being detected by SURF and SIFT?
Is it necessary that these have to be computed on gray scale images?
What does the term "descriptor" mean in simple words.
Generally,how many features are selected/extracted?Is there a criteria for that?
What does the size of Hessian matrix determine?
What is the size of the features being detected?It is said that the size of a feature is the size of the blob.So, if size of image is M*N so will there be M*N n umber of features?
These questions may seem too trivial, but please help..
I will try to give an intuitive answer to some of your questions, I don't know answers to all.
(You didn't specify which paper you are reading)
What are the features and how many features are being detected by SURF
and SIFT?
Normally features are any part in an image around which you selected a small block. You move that block by a small distance in all directions. If you find considerable variations between the one you selected and its surroundings, it is considered as a feature. Suppose you moved your camera a little bit to take the image, still you will detect this feature. That is their importance. Normally best example of such a feature is corners in the image. Even edges are not so good features. When you move your block along the edge lines, you don't find any variation, right?
Check this image to understand what I said , only at the corner you get considerable variation while moving the patches, in other two cases you won't get much.
Image link : http://www.mathworks.in/help/images/analyzing-images.html
A very good explanation is given here : http://aishack.in/tutorials/features-what-are-they/
This the basic idea and the algorithms you mentioned make this more robust to several variations and solve many issues. (You can refer their papers for more details)
Is it necessary that these have to be computed on gray scale images?
I think so. Anyway OpenCV works on grayscale images
What does the term "descriptor" mean in simple words?
Suppose you found features in one image, say image of a building. Now you took another image of same building but from a slightly different direction. You found features in the second image also. But how can you match these features. Say feature 1 in image 1 match to which feature in image 2 ? (As a human, you can do easily, right ? This corner of building in first image corresponds to this corner in second image, so and so. Very easy).
Feature is just giving you pixel location. You need more information about that point to match it with others. So you have to describe the feature. And this description is called "descriptors". To describe this features, algorithms are there and you can see it SIFT paper.
Check this link also : http://aishack.in/tutorials/sift-scale-invariant-feature-transform-introduction/
Generally,how many features are selected/extracted?Is there a criteria
for that?
During processing you can see applying different thresholds, removing weak keypoints etc. It is all part of plan. You need to understand algorithm to understand these things. Yes, you can specify these threshold and other parameters (in OpenCV) or you can leave it as default. If you check for SIFT in OpenCV docs, you can see function parameters to specify number of features, number of octave layers, edge threshold etc.
What does the size of Hessian matrix determine?
That I don't know exactly, just it is a threshold for keypoint detector. Check OpenCV docs : http://docs.opencv.org/modules/nonfree/doc/feature_detection.html#double%20hessianThreshold
I'm trying to write a code The helps me in my biology work.
Concept of code is to analyze a video file of contracting cells in a tissue
Example 1
Example 2: youtube.com/watch?v=uG_WOdGw6Rk
And plot out the following:
Count of beats per min.
Strenght of Beat
Regularity of beating
And so i wrote a Matlab code that would loop through a video and compare each frame vs the one that follow it, and see if there was any changes in frames and plot these changes on a curve.
Example of My code Results
Core of Current code i wrote:
for i=2:totalframes
compared=read(vidObj,i);
ref=rgb2gray(compared);%% convert to gray
level=graythresh(ref);%% calculate threshold
compared=im2bw(compared,level);%% convert to binary
differ=sum(sum(imabsdiff(vid,compared))); %% get sum of difference between 2 frames
if (differ ~=0) && (any(amp==differ)==0) %%0 is = no change happened so i dont wana record that !
amp(end+1)=differ; % save difference to array amp wi
time(end+1)=i/framerate; %save to time array with sec's, used another array so i can filter both later.
vid=compared; %% save current frame as refrence to compare the next frame against.
end
end
figure,plot(amp,time);
=====================
So thats my code, but is there a way i can improve it so i can get better results ?
because i get fealing that imabsdiff is not exactly what i should use because my video contain alot of noise and that affect my results alot, and i think all my amp data is actually faked !
Also i actually can only extract beating rate out of this, by counting peaks, but how can i improve my code to be able to get all required data out of it ??
thanks also really appreciate your help, this is a small portion of code, if u need more info please let me know.
thanks
You say you are trying to write a "simple code", but this is not really a simple problem. If you want to measure the motion accuratly, you should use an optical flow algorithm or look at the deformation field from a registration algorithm.
EDIT: As Matt is saying, and as we see from your curve, your method is suitable for extracting the number of beats and the regularity. To accuratly find the strength of the beats however, you need to calculate the movement of the cells (more movement = stronger beat). Unfortuantly, this is not straight forwards, and that is why I gave you links to two algorithms that can calculate the movement for you.
A few fairly simple things to try that might help:
I would look in detail at what your thresholding is doing, and whether that's really what you want to do. I don't know what graythresh does exactly, but it's possible it's lumping different features that you would want to distinguish into the same pixel values. Have you tried plotting the differences between images without thresholding? Or you could threshold into multiple classes, rather than just black and white.
If noise is the main problem, you could try smoothing the images before taking the difference, so that differences in noise would be evened out but differences in large features, caused by motion, would still be there.
You could try edge-detecting your images before taking the difference.
As a previous answerer mentioned, you could also look into motion-tracking and registration algorithms, which would estimate the actual motion between each image, rather than just telling you whether the images are different or not. I think this is a decent summary on Wikipedia: http://en.wikipedia.org/wiki/Video_tracking. But they can be rather complicated.
I think if all you need is to find the time and period of contractions, though, then you wouldn't necessarily need to do a detailed motion tracking or deformable registration between images. All you need to know is when they change significantly. (The "strength" of a contraction is another matter, to define that rigorously you probably would need to know the actual motion going on.)
What are the structures we see in the video? For example what is the big dark object in the lower part of the image? This object would be relativly easy to track, but would data from this object be relevant to get data about cell contraction?
Is this image from a light microscop? At what magnification? What is the scale?
From the video it looks like there are several motions and regions of motion. So should you focus on a smaller or larger area to get your measurments? Per cell contraction or region contraction? From experience I know that changing what you do at the microscope might be much better then complex image processing ;)
I had sucsess with Gunn and Nixons Dual Snake for a similar problem:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.6831
I placed the first aproximation in the first frame by hand and used the segmentation result as starting curv for the next frame and so on. My implementation for this is from 2000 and I only have it on paper, but if you find Gunn and Nixons paper interesting I can probably find my code and scan it.
#Matt suggested smoothing and edge detection to improve your results. This is good advise. You can combine smoothing, thresholding and edge detection in one function call, the Canny edge detector.Then you can dialate the edges to get greater overlap between frames. Little overlap will probably mean a big movement between frames. You can use this the same way as before to find the beat. You can now make a second pass and add all the dialated edge images related to one beat. This should give you an idea about the area traced out by the cells as they move trough a contraction. Maybe this can be used as a useful measure for contraction of a large cluster of cells.
I don't have access to Matlab and the Image Processing Toolbox now, so I can't give you tested code. Here are some hints: http://www.mathworks.se/help/toolbox/images/ref/edge.html , http://www.mathworks.se/help/toolbox/images/ref/imdilate.html and http://www.mathworks.se/help/toolbox/images/ref/imadd.html.