How to find the same feature points within an image after print-scan process?

Question

I'm trying to find the same feature points within an image, before and after the print-scan process. To do this, I used cv2.goodFeaturesToTrack method:

import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from skimage import color, transform
import cv2

# Read image as Numpy array
image = np.array(Image.open('images/stairway512.jpg'))

# Blur image
imageBlurred = cv2.blur(image, (10, 10))

# Find 5 feature points in cropped & blurred image
points = cv2.goodFeaturesToTrack(imageBlurred, 5, 0.01, 10)

The points I get are these:

array([[[ 62., 186.]],

       [[298., 398.]],

       [[ 47., 185.]],

       [[298.,  68.]],

       [[195., 135.]]], dtype=float32)

I used blur because I assumed it would minimize the impact of the print-scan process (because I can then blur the scanned image the same way), but I end up with different feature points for the scanned image. However, when I use the same code for the scanned image, I get these points:

array([[[297., 403.]],

       [[297., 359.]],

       [[268., 359.]],

       [[268., 396.]],

       [[308.,  65.]]], dtype=float32)

Any ideas on how to make these points the same?

user1196549 · Accepted Answer · 2021-01-26 14:45:20Z

0

As a rule, feature points are unstable and will vary in position and score when you modify the image.

Take more feature points (say 100, 5 is by far too small) and associate them in pairs by the nearest neighbor rule. This will given more insight in what you can achieve with this detector.

answered Jan 26, 2021 at 14:45

user1196549

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