shedletsky's profile - activity

overview network karma followed questions activity

2015-12-27 13:32:51 -0600	received badge	● Student (source)
2015-09-29 13:21:52 -0600	commented answer	Signal processing to detect "jag-iness"? Just for my intellectual curiosity, this solution works because the line is horizontal. How would I handle this problem in the more general case? For example, if instead of a line I had a jaggy triangle polyline?
2015-09-29 13:19:36 -0600	commented question	Signal processing to detect "jag-iness"? Jaggies are high frequency noise, like in the bottom of the image. Big ugly chunky pixels.
2015-09-29 13:18:02 -0600	received badge	● Scholar (source)
2015-09-29 13:17:59 -0600	commented answer	Signal processing to detect "jag-iness"? That is a great idea. Thanks for the very thoughtful response.
2015-09-29 13:16:56 -0600	received badge	● Supporter (source)
2015-09-24 21:42:30 -0600	asked a question	Signal processing to detect "jag-iness"? $C:\fakepath\gap.png$ 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.