Correspondence of IMAQ for rotational measure

Hello

I use the IMAQ game ringtone 2 vi to try to measure the rotation of a component in an image. (LabVIEW 2011)

The input image consists of data collected from a laser profile scanner and is converted into a gray level image of 8 bits with the height information replacing the usual light intensity data. (This: http://www.micro-epsilon.co.uk/laser-scanner-profile-sensor/index.html)

A copy of my raw image is attached.

I did a little pre image processing before trying to match to the model, so face a binary image (just 0 and 255) in central France on the image. (assuming that this will make easier?)

I enclose also two pictures with the KING of the superimposed pattern match. The two images are different screenshots to a stationary object, so the pattern match must be in an identical position, but it is not, therefore a change of 1.6 degrees is reported when the object remained stationary.

Anyone has any advice on how to implement the pattern match algorithm to be a little more consistent and reproducible?

The settings that I currently use are:

Learn the model, the learning mode = All

Match, sub pixel accuracy of installation is true, match = indifferent rotation mode, ranges of angle of rotation =-10 + 10

the minimum match score = 500

Thanks in advance.

I suspect the binary conversion is complicating, not easier.  It depends really on the functioning of the algorithm.

What it looks like when the game turns?  Based on the image, it seems that there is some problems of perspective that you will have to deal with.  I'd be interested to see what an original image of the room even turned a known, maybe 30 degree angle.  The form remains the same?

If the binary form is still the same after rotation (perfect), I would consider using the binary research and analysis instead of filtering.  I seem to remember a few tools to measure several properties of a binary image that can be used to determine the rotation of the object.

If the binary is not the same, the manipulation of the original image may solve the problem.  I think that if you resampled the image and increase the resolution to make the circular bottom ovals (assuming they really are circles), you could then measure rotation accurately.  I still consider the binary tools in this case.

Bruce

Tags: NI Hardware

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