I intend to find cracks in the image. However, as you can see, the noise is very heavy. So, does anybody know how to cope with such a extreme situation? Thanks in advance.
This image is color, so the other method is try to convert the other color space and find the easy inspection image. My suggestion try:
boxfilter(src,temp1,kernel......) //try bigger core. or you can use frequency filter
temp2=cv::substract(src,temp1)
or
boxfilter(src,temp12,kernel1......) // the kernel size kernel1<kernel2
boxfilter(src,temp11,kernel2......) //try bigger core. or you can use frequency filter
temp2=cv::substract(temp11,temp12)
then try to
threshold (temp2,temp3......) //or the orher threshold
finally try to use blob to find the longest line or characteristic which is near horizontal.
best regard
Asked: 2014-02-28 22:21:34 -0600
Seen: 349 times
Last updated: Mar 02 '14
I wonder if a hough circle function could be modified to allow for variations of arc during the line.
I have the same question as yours. Suppose it can, then how to reduce the effect caused by such heavy noise?
The crack is not a perfect line but it does have some sections that are less noisy. I would think you would have to start with a Gaussian blur function. Then the hough functions start by measuring local line angles and then tries to stitch them together into a longer line. The noisy bits won't pass this second part of the process.