How to detect texture disruption

Question

Let's say we have a number of color images that are examples of some textured pattern. There is a rare occurrence where this texture is "disrupted" by some foreign object. What would be the best way to detect these rare anomalies?

I thought about training a CNN, but the number of good examples vastly outnumbers the bad examples, so I have my doubts. I started looking into grey level co-occurrence matrices (GLCM) and local binary patterns (LBP), but I think color information could play an important part in determining the occurrence of a disruption. Could I find the distribution from these extracted features (of either GLCM or LBP) and calculate the probability that a new image belongs to this distribution?

Thanks for your help!

Answer 1

It is difficult to figure out your problem without seeing some sample images. In principle there's a wide variety of approaches you could use to detect texture disruption, namely GLCM features, LBPs, Law's masks, vector quantization, etc. Measuring the local entropy is a possible way to go. Consider the image below, in which we can clearly distinguish two types of texture:

The following snippet reads the image, computes the local entropy for each pixel on a circular neighbourhood or a given radius 25 and displays the results:

from skimage import io
from skimage.filters.rank import entropy
from skimage.morphology import disk

img = io.imread('https://i.sstatic.net/Wv74a.png')
R = 25
filtered = entropy(img, disk(R))
io.imshow(filtered)

It clearly emerges from the resulting entropy map that the local entropy values could be utilized to detect texture disruption.