Quantcast
Channel: OpenCV Q&A Forum - RSS feed
Viewing all articles
Browse latest Browse all 41027

Signal processing to detect "jag-iness"?

$
0
0
![C:\fakepath\gap.png](/upfiles/14431275571242186.png) I have an industrial application where I am using a camera to measure the gap between two pieces of plastic. In the above image I have found the gap and colored it green. I want to be able to analyze the amount of high frequency "jaggies" in this gap, so I can reject photos where the edges between the two pieces is too rough. My first thought was to build a 1-D matrix [x1,x2,x3,....] with the number of gap pixels in each column of my image (above). Then do a DFT and use that to filter out noise. My problem is that I do not understand the output of the Mat::dft() function and how to transform it to get the answer I want. My second (contributing) problem is that I don't really have a good conceptual framework for gauging how "jaggy" an image is, except that I know a jaggy image when I see it. I would appreciate any suggestions for how to solve my problem.

Viewing all articles
Browse latest Browse all 41027

Trending Articles



<script src="https://jsc.adskeeper.com/r/s/rssing.com.1596347.js" async> </script>