I would like to eliminate the keypoints detected around the frame of an image (an artwork of a museum gallery ). In other words I want to separate out the actual artwork from its frame. Each artwork consist of different types of frames.
![Keypoints detected using sift][1]
I have already written a Python wrapper for David Lowe's SIFT implementation to detect keypoints as well as to compute descriptors.
However my question is what is the best approach to solve this problem? any of the following or something else?
Using Hough transformation (using Python Image Library)
Template matching
Your help is highly appreciated
Thanks again
I'd go with Hough transform and try to detect lines which form a quadrilateral.
You might get into trouble if the painting actually does contain a square or something. I'd look for some assumptions like: acceptable aspect ratio, acceptable size. Also find the outermost quadrilateral, and work your way towards the center of the image picking up inner quadrilaterals, if applicable. This would give you the frame and its thickness, so you can disregard any keypoints here or beyond the frame.
P.S. If you got some random replies from me, it's because I accidentally replied to another post in your thread... ^^
For each artwork, do you have a clean, properly framed reference image?
If so another solution to remove the background clutter is:
to use the ratio test algorithm to compute keypoints correspondences between your frame and the reference image,
to perform a geometric consistency check to filter out false matches.
In addition the geometric check will provide you with the homography matrix that you can use to warp your input frame or alternatively to project the corners of the reference images.
That way you will natively obtain the artwork area within your frame.
Here's an example about how you can do that with opensift's match tool - below is an illustration.
Related
Forgive me but I'm new in OpenCV.
I would like to delete the common background in 3 images, where there is a landscape and a man.
I tried some subtraction codes but I can't solve the problem.
I would like output each image only with the man and without landscape
Are there in OpenCV Algorithms what do this do? (then without any manual operation so no markers or other)
I tried this python code CV - Extract differences between two images
but not works because in my case i don't have an image with only background (without man).
I thinks that good solution should to Compare all the images and save those "points" that are the same at least in an image.
In this way I can extrapolate a background (which we call "Result.jpg") and finally analyze each image and cut those portions that are also present in "Result.jpg".
You say it's a good idea? Do you have other simplest ideas?
Without semantic segmentation, you can't do that.
Because all you can compute is where two images differ, and this does not give you the silhouette of the person, but an overlapping of two silhouettes. You'll never know the exact outline.
I need your advice, guys! So I am trying to create a mask for a football (i.e. soccer) pitch, and if I only use filtering by the most common hue/saturation/value values, I get the following image
As you can see, the desired part is inside the grey boundary I drawn and there is a lot of noise here - trees, lines, external objects, and of course I would like to get rid of it. The desired outcome is something similar to this:
I thought about an algorithm that would transform the first image into another by analyzing each pixel's surrounding and color it white if more than threshold% of pixels into a (x, x) square is white, otherwise black.
Do you know if there is an implementation on openCV or similar libraries for this or I should build it from scratch?
Also, maybe you can propose other way to deal with the noise and external objects? I already tried the morphological transform and blurring techniques, but either I don't do it right or it doesn't work well for my problem.
Thank you in advance for your advice!
I actually found an easy implementation of the algo I proposed - I simply use cv2.blur on the image and then filter with cv2.inRange, so it does exactly what I wanted it to do.
I have a fixed camera and I need to check if its position or orientation has been changed. I am trying to use OpenCV (calculating diiferencies between a reference image and a new one) for this, but I am pretty new to OpenCV (and image processing in general) but I am not really sure what specific algorithm would be the best to use for this, or how to interpret the results to find if the camera has been moved/rotated. Any ideas?
Please help,
One way to do it would be to register the two frames to each other using affine image registration from openCV. From this you can extract the rotation and displacement difference between the two frames. Unfortunately this will only work well for in-plane rotations but I still think it is your best bet.
If you post some sample code and data I would be happy to take a look.
You can use Canny or HoughLinesP to find lines,From this you can get two lines,compare it.Maybe this will be effective in some simple background.if some object in your picture,try sift or other feature extractor,you can take features to find the relationship from two frames.
I am working on an application where I need feature like Cam Scanner where document is to be detected in an image. For that I am using Canny Edge detection followed by Hough Transform.
The results look promising but the text in the document is creating issues as explained via images below:
Original Image
After canny edge detection
After hough transform
My issue lies in the third image, the text in original mage near the bottom has forced hough transform to detect the horizontal line(2nd cluster from bottom).
I know I can take the largest quadrilateral and that would work fine in most cases, but still I want to know any other ways where in this processing I can ignore the effect of text on the edges.
Any help would be appreciated.
I solved the issue of text with the help of median filter of size 15(square) in an image of 500x700.
Median filter doesn't affect the boundaries of the paper, but can help eliminate the text completely.
Using that I was able to get much more effective boundaries.
Another approach you could try is to use thresholding to find the paper boundaries. This would create a binary image. You can then examine the blobs of white pixels and see if any are large enough to be the paper and have the right dimensions. If it fits the criteria, you can find the min/max points of this blob to represent the paper.
There are several ways to do the thresholding, including iterative, otsu, and adaptive.
Also, for best results you may have to dilate the binary image to close the black lines in the table as shown in your example.
Using python, which may be the best algorithm or the best strategy to detect the presence of colored bands as in image?
The image is scanned and cropped, the problem is that the crop not to be precise and I can not make use of a control that makes use of Cartesian coordinates to determine if the lines are present.
The strips may be present or not.
You have a number of options at your disposal:
If the strips are going to be the same size, and their orientation is known, then you can use cross-correlation (with working Python source). Your template image could be a single stripe, or a multiple strip pattern if you know the number of strips and their spacing.
More generally, you could go with morphological image processing and look for rectangles. You'd first have to threshold your image (using Ohtsu's method or some empirically determined threshold) and then perform contour detection. Here's an example that does something similar, but for ellipses -- it's trivial to modify it to look for rectangles. This time the source in in C, but it uses OpenCV like the first example, so it should be trivial to port
There are other approaches such as edge detection and Fourier analysis, but I really think that the first two are going to be more than enough for you.