Find defects in a 3D image

Hello,
i'm working on a machine that uses a laser scanner to measure gears.
I acquire a XYZ points cloud for each tooth of the gear and i compare them with a "reference" point cloud that is my template, the result of this comparison is an image that represent the Z difference between source and template points cloud. The difference image has a dynamic of 0.3mm in Z using 256 gray levels, 128 grey if the difference is 0, values < 128 are "defects\hole" values > 128 are "defets\peack".
Now i have to develop the final part of the algorithm that discriminates real defects against noise (coming from measure error of gear itself).
I tried using threshold methods but doesn't work in a reliable way.
Attached a couple difference images that have defects
Thanks for any suggestion
Alessandro Ricco
ILVG.it - Italian LabVIEW user Group, ADMIN & LabVIEW Champion
Attachments:
diff.png ‏55 KB
diff11.png ‏56 KB

Hello Alessandro,
by seeding the values I meant that the segmentation procedure (GrabCut for example) needs an input labeled mask.
The options are:
1. Obvious background
2. Obvious foreground
3. Possible background
4. Possible foreground
For more information, you can check the OpenCV's GrabCut arguments explanation here:
http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html#grabcut
You basically need to provide some information of the underlying pixels before running the algorithm. Same goes for example with GrowCut segmentation. I know there is a GrowCut segmentation algorithm written in Matlab, but not sure for OpenCV.
Here is a working .dll implementation of OpenCV's GrabCut algorithm in Labview:
https://decibel.ni.com/content/blogs/kl3m3n/2013/07/30/color-histogram-matching-and-grabcut-segmenta...
It uses a ROI rectangle, where all pixels outside the rectangle are considered as a positive background pixels.
Regarding the difference in the depth direction, you would need to display the 3D pointcloud after the ICP alignment and extract the Zbuffer value from both pointclouds using the same viewpoint. You would basically just need to snap the depth image from the active viewpoint. PCL (point cloud library) has this option I think. This should be much faster than your approach.
See: http://pointclouds.org/documentation/tutorials/range_image_creation.php
This should work after aligning both pointclouds and using a proper sensor position. First, get the depth image of one pointcloud and then the other using the same parameters of the sensor orientation. Then you just calculate the difference.
I have not tried this, but I think it should work. If you are interested, I can try to (when I get the chance) build a .dll which can be called in Labview? That is if you don't beat me to it!
Hope this helps a bit.
Best regards,
K
https://decibel.ni.com/content/blogs/kl3m3n
"Kudos: Users may give one another Kudos on the forums for posts that they found particularly helpful or insightful."

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