Segmentation of defective areas in grayscale images, which is invariant to shadows

Question

I want to segment defective areas in images using MATLAB/Python-OpenCV.

Original image:

With Defects:

https://i.sstatic.net/mqgGO.jpg

Defect can be seen at 3rd rectangle.

What I tried so far:

• Extract borders of rectangles with LoG filter / threshold graylevel (but not helps much because of shadows)
• Trace their boundaries
• Get centroid
• Find distance between boundary points and centroid with respect to angle ( increment angle by 0.5 degrees for better resolution)
• Find a good template rectangle and save it
• Find the difference between template rectangle and candidate rectangle
• Based on that result I can find the faulty regions but the false-alarm rate increases when I try to increase the sensitivity of algorithm.

I need to get boundaries much more precise and non-noisy. Because of the shadows, the edges of rectangle may vary vastly.

How can I get edges of rectangles more robust to shadows? What can be done instead of what I tried so far?

Thanks for your help!

Answer 1

A Laplace of Gaussian filter is a zero mean operation. If you feed it an 8-bit image with intensities centered on 127, it will return you image data centered on zero. You must use a filter bias of arbitrary value, usually half the container's max value (so in this 8-bit example, the bias would be 127). You can also adjust the filter strength by multiplying the result pixels by a constant, this makes the log filter's effect more apparent.

The log filter will make one white and one black edge for very strong transitions. In the horizontal or vertical direction, finding the actual edge is very easy, as you need only take the average of position of both. This gives you sub-pixel resolution if integrated over a small distance.

If the illumination of these images is very similar, you can use registration and subtraction:

1. Normalize both the image suspected to contain defects and a reference image to some intensity.

2. Register (align) them; you could do this by detecting three points on a rectangle and then moving and rotating one of the images.

3. Subtract the suspect image from the reference image. This gives you an error map. You can apply a small blur and then a tight LoG filter to it to remove noise and make detection more accurate.