I'm new in StackOverflow and I'd appreciate some help. I'm working in an algorithm for detecting "aguajes", a particular kind of palm. So far, I got good results using a texture feature extractor and other characteristics, but the main problem is that I can't difference between an "aguaje" and other palms (mainly pinnated palms) because the texture is very similar. You cn see two images bellow
1.AGUAJE
AGUAJE
2.Pinnated Palm
Pinnated Palm
I see that the main difference in the second one is the presence of a Central Axis (also called Petiole or Rachis). The question is "How to detect it?". I mean, if I'm able to detect the presence of a Petiole, I'd be able to distinguish between this two types of palm. I think color is not an important issue because even when there's no color (gray image), we can discern where is located that Axis.
Thanks in advance.
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I am researching into the best way to detect test in a photo using open source libraries.
I think the standard way is as follows (note: steps 1 - 4 all use OpenCV):
1) detect outline of document
2) transform document so it's flat and cropped, using said outline
3) Make the background of document white, using a filter
4) Feed resulting image to Tesseract
Is this the optimum process, or is there a better way, or better tools?
Also, what happens for case if the photo doesn't have a document outline (It's possible that step 1 & 2 are redundant)?
Is there anyway to automatically detect document orientation (i.e. portrait / landscape)?
I think your process is fine. I've used a similar process for an Android project.
I think that the only way you can discover if a document is portrait/landscape is to reason with the length of the sides of the bounding box of your outline.
I don't think there's an automatic way to do this, maybe you can find the most external contour approximable with a 4 segment polyline (all doable in opencv). In order to get this you'll have to work with contour hierarchy and contous approximation (see cv2.approxPolyDP).
This is how I would go for automatic outline detection. As I said, the rest of your algorithm seems just fine to me.
PS. I'll leave my Android project GitHub link. I don't know if it can be useful to you, but here I specify the outline by dragging some handles, then transform the image and feed it to Tesseract, using Java and OpenCV. Yeah It's a very bad idea to do that in the main thread of an Android app and yeah, the app is not finished. I just wanted to experiment with OCR, so I didn't care much of performance and usability, since this was not intended to use, but just for studying.
Look up the uniform width transform.
What this does is detect edges which have more or less the same width with respect to their opposite edge. So things like drainpipes (which can be eliminated at a later pass) but also the majority of text. Whilst conceptually it's similar to a distance transform, the published method uses rather ad hoc normal projection methods and Canny edge detection.
I'm newbie with machine learning, and I have only basic knowledge in neural networks.
I have pretty clear task:
1. Video stream shows static picture (white area with yellow squares)
(in different videos squares located in different places)
2. In some moment content of the video changes, and starts to show white area without some of the yellow squares.
3. I need to create mechanism which can determines and somehow indicates that changes.
I'm going to use for that task TensorFlow framework. Could anybody push me in right direction? Or I'll be very happy to see list of steps to overcome the problem.
Thanks in advance.
If you know how the static picture looks beforehand, may be some background-subtraction would work? Basically you just subtract the static picture from every frame and check the content of the result. If the resulting picture is empty (zeros or close to it up to some threshold) there is no change to detect. If the resulting picture contains a region that is non-zero (may be above or below a certain manually tuned threshold), you detected a change in that region.
i am trying to subtract 2 image using the function cvAbsDiff(img1, img2, dest);
it working but sometimes when i bring my hand before my head or body the hand is not clear and background comes into picture... the background image(head) overlays my foreground.(hand)..
it works correctly on plain surfaces i.e when the background is even like a wall.
please check out my image...so that you can better understand my problem...!!!!
http://www.2shared.com/photo/hJghiq4b/bg_overlays_foreground.html
if you have any solution/hint please help me.......
There's nothing wrong with your code . Background subtraction is not a preffered way for motion detection or silhoutte detection because its not very robust.The problem is coming because both the background and the foreground are similar in colour at many regions which on subtractions pushes the foreground to back . You might try using
- optical flow for motion detection
- If your task is just detecting silhoutte or hand try training a HOG classifier over it
In case you do not want to try a new approach . You may try around playing with the threshold value(in your case 30).So when you subtract similar colour image there difference is less than 30 . And later you threshold with 30 so it just blacks out. Also you may try HSV or some other colourspace as well .
Putting in the relevant code would help. Also knowing what you're actually trying to achieve.
Which two images are you subtracting? I've done subtracting subsequent images (so, images taken with a delay of a fraction of a second), and the background subtraction generally results in the edges of moving objects, for example the edges of a hand, and not the entire silhouette of a hand. I'm guessing you're taking the difference of the current frame and a static startup frame. It's possible that parts aren't different enough (skin+skin).
I've got some computer problems tonight, I'll test it out tomorrow (pls put up at least the steps you actually carry thorough though) and let you know.
I'm still not sure what your ultimate goal is, although I'm guessing you want to do some gesture-recognition (since you have a vector called "fingers").
As Manpreet said, your biggest problem is robustness, and that is from the subjects having similar color.
I reproduced your image by having my face in the static comparison image, then moving it. If I started with only background, it was already much more robust and in anycase didn't display any "overlaying".
Quick fix is, make sure to have a clean subject-free static image.
Otherwise, you'll want to have dynamic comparison image, simplest would be comparing frame_n with frame_n-1. This will generally give you just the moving edges though, so if you want the entire silhouette you can either:
1) Use a different segmenting algorithm (what I recommend. Background subtraction is fast and you can use it to determine a much smaller ROI in which to search, and then use a different algorithm for more robust segmentation.)
2) Try to make a compromise between the static and dynamic comparison image, for example as an average of the past 10 frames or something like that. I don't know how well this works, but would be quite simple to implement, worth a try :).
Also, try with CV_THRESH_OTSU instead of 30 for your threshold value, see if you like that better.
Also, I noticed often the output flares (regions which haven't changed switch from black to white). Checking with the live stream, I'm quite certain it because of the webcam autofocusing/adjusting white balance etc.. If you're getting that too, turning off the autofocus etc. should help (which btw isn't done through openCV but depends on the camera. Possibly check this: How to programatically disable the auto-focus of a webcam?)
What would be the best approach for detecting and removing a person's hair in a simple portrait image ? Any useful libraries of algorithms ? I have been looking over openCV which looks like it could be of some use
You're dealing with two different problems here:
detecting if a face in a portrait has hair
"removing" the hair
The first is solvable fairly easily:
Separate the face from the background (as you've mentioned a "simple portrait image", this shouldn't be too hard).
Convert your image to the Y'CbCr color space
Human skin has a fairly narrow range of chrominance values, regardless of race. Check out this paper for the details.
The approach above will help you separate skin areas of the face from non-skin areas
Assume that the non-skin areas consist of hair. Note that facial hair will get picked up as a non-skin area, too.
As far as the second problem goes, you need to clarify exactly what you mean by "removing":
Are you simply cutting out the part of the portrait that has hair? In this case, the solution follows directly from the detection method above.
Are you trying to make it look like the person has no hair at all (e.g. is bald, clean-shaven?) In this case, things will be a lot harder -- there's a reason why professional photo manipulators get paid well.
I think this is hard problem - consider for detecting and removing hair in this case:
I found several papers, maybe they will help you:
Detection and analysis of hair
Dullrazor®: A software approach to hair removal from images
Research on the Expression of Hair in Computer Animation
Cheers!
try this http://betaface.com/demo.html . Color , hair info and more . smile , age, eye etc..
iam working in a project that i take a vedio by a camera and convert this vedio to frames (this part of project is done )
what iam facing now is how to detect moving object in these frames and differentiate them from the background so that i can distinguish between them ?
I recently read an awesome CodeProject article about this. It discusses several approaches to the problem and then walks you step by step through one of the solutions, with complete code. It's written at a very accessible level and should be enough to get you started.
One simple way to do this (if little noise is present, I recommend smoothing kernel thought) is to compute the absolute difference of two consecutive frames. You'll get an image of things that have "moved". The background needs to be pretty static in order to work. If you always get the abs diff from the current frame to the nth frame you'll have a grayscale image with the object that moved. The object has to be different from the background color or it will disappear...