Ask Your Question

Revision history [back]

click to hide/show revision 1
initial version

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.

click to hide/show revision 2
No.2 Revision

updated 2015-09-24 21:54:40 -0500

berak gravatar image

Signal processing to detect "jag-iness"?

C:\fakepath\gap.png

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.

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.