Map image model to data

Question

I'm working on a software that checks if some laser-cut parts were cut correctly, using AutoCAD data as reference. I have parsed the dxf-files, converted them to a bmp (and to an xml File that gives me all the information), and now I want to compare this to the real, acquired data.

I have applied enough preprocessing to get a reasonably thresholded, binary picture. This is, however, distorted (unfortunately, telecentric lenses are expensive and the user places the object into a device, causing some translation, some scalation and a tiny amount of rotation, as in 1-2degs).

I have considered Hough transform, but memory is an issue. I have played around with bounding box transformation, but the unknown shape makes this hard. I've read about TILT (no symmetry) and registration algorithms, but I'd like to get another opinion.

I'm looking for some papers, some ideas, some pointers on how to go on.

Thanks.

Answer 1

You may want to look at features that can discriminate the two. Are there simple features that can accurately distinguish a properly cut piece vs. an incorrectly cut piece? If so, you can use the same idea as the Hough transform/template matching, but reducing the template to certain distinguishing features (edges, corners, etc.) to reduce the memory required.

You may want to look at the SIFT/SURF features that aim to match images by a certain set of features while being invariant to the rotation and scale of the objects within the image. There are libraries out there that implement these features (shown on the SURF page).

This however, wont help with the distortion. If you're using the same camera for all images, then you should be able to de-skew them accordingly.

Answer 2

First step is to undistort the image ( see camera calibration - ignore the 3d part).

Then think about the shape matching. Depending on how small the error you are trying to find, this could be very easy or very very difficult, but those links should get you started