OpenCV: comparing simple images with small differe

问题:

I have a bunch of "simple" images and I want to compare if they are similar together. I compare them to each other using template matching (cv::matchTemplate) and results are quite good.

Now I want to fine tune my program and I face a problem. For example I have two images which look very much alike. Only differences they have is that another one has thicker line and the digit front of item is different. When both images are small, one pixell difference in line thickness makes big result differences when doing template matching. When line thicknesses are same and only difference is the front digit, I get template matching result something like 0.98 with CV_TM_CCORR_NORMED when match successful. When line thickness is different matching result is something like 0.95.

I cannot decrease my threshold value below 0.98 because some other similar images have same line thickness.

Here are example images:

So what options do I have?

I have tried:

• dilate the original and template
• erode also both
• morphologyEx both
• calculating keypoints and comparing them
• finding corners

But no big success yet. Are those images too simple that detecting "good features" is hard?

Any help is very wellcome.

Thank you!

EDIT:

Here are some other example images. What my program consider as similar are put in same zip-folder. ZIP

回答1:

A possible way might be thinning the two images, so that every line is of one pixel width, since the differing thickness is causing you the main problem with similarity.

The procedure would be to first binarize/threshold the images, then apply a thinning operation on both images, so both are now having the same thickness of 1 px. Then use the usual template matching that you used before with good results.

In case you'd like more details on the thinning/skeletonization of binary images here are a few OpenCV implementations posted on various discussion forums and OpenCV groups:

1. OpenCV code for thinning (Guo and Hall algo, works with CvMat inputs)
2. The JR Parker implementation using OpenCV
3. Possibly more efficient code here (uses OpenCV optimized access methods a lot, however most of the page is in Japanese!)
4. And lastly a brief overview of thinning in case you're interested.

回答2:

You need something more elementary here, there isn't much reason to go for fancy methods. Your figures are already binary ones, and their shapes are very similar overall.

One initial idea: consider the upper points and bottom points in a certain image and form a upper hull and a bottom hull (simply a hull, not a convex hull or anything else). A point is said to be an upper point (respec. bottom point) if, given a column i, it is the first point starting at the top (bottom) of the image that is not a background point in i. Also, your image is mostly one single connected component (in some cases there are vertical bars separated, but that is fine), so you can discard small components easily. This step is important for your situation because I saw there are some figures with some form of noise that is irrelevant to the rest of the image. Considering that a connected component with less than 100 points is small, these are the hulls you get for the respective images included in the question:

The blue line is indicating the upper hull, the green line the bottom hull. If it is not apparent, when we consider the regional maxima and regional minima of these hulls we obtain the same amount in both of them. Furthermore, they are all very close except for some displacement in the y axis. If we consider the mean x position of the extrema and plot the lines of both images together we get the following figure. In this case, the lines in blue and green are for the second image, and the lines in red and cyan for the first. Red dots are in the mean x coordinate of some regional minima, and blue dots the same but for regional maxima (these are our points of interest). (The following image has been resized for better visualization)

As you can see, you get many nearly overlapping points without doing anything. If we do even less, i.e. not even care about this overlapping, and proceed to classify your images in the trivial way: if an image a and another image b have the same amount of regional maxima in the upper hull, the same amount of regional minima in the upper hull, the same amount of regional maxima in the bottom hull, and the same amount of regional minima in the bottom hull, then a and b belong to the same class. Doing this for all your images, all images are correctly grouped except for the following situation:

In this case we have only 3 maxima and 3 minima for the upper hull in the first image, while there are 4 maxima and 4 minima for the second. Following you see the plots for the hulls and points of interest obtained:

As you can notice, in the second upper hull there are two extrema very close. Smoothing this curve eliminates both extrema, making the images match by the trivial method. Also, note that if you draw a rectangle around your images, then this method will tell they are all equal. In that case you will want to compare multiple hulls, discarding the points in the current hull and constructing other ones. Nevertheless, this method is able to group all your images correctly given they are all very simple and mostly noisy-free.

回答3:

From as much as I can get, the difficulty is when the shape is the same, just size is different. A simple hack approach could be: - subtract the images, then erode. If the shapes were the same but one slightly bigger, subtracting will leave only the edges, which will be thin an vanish with erosion as noise.

Somewhat more formal, would be to take the contours and then the approximate polygons and do a invariants comparison (Hu Moments etc.)