Well, about Wednesday night I was looking at the output of one of the sift annotated images from my vision system and I finally snapped. It just looked wrong. It had been bothering me for a while that it didn’t seem to be selecting very interesting features and taking an unreasonable amount of interest in the bedroom wall, so I broke out the example binary provided by Professor Lowe himself (the inventor of the SIFT algorithm) for comparison.

As I suspected running the two SIFT implementations against identical images yielded completely different results. Lowe’s implementation finding many more and far more interesting features than the version I had been using for months.

This discovery was simultaneously bad and good news. Bad in that it meant I had a bunch of work to do, but good in that I had finally discovered the source of all my frustration.

My new plan of attack has been to write a bunch of C# code to invoke Lowe’s binary out of process and parse the resulting key file back into my program. It’s all a bit messy really but this whole project is still just a proof of concept so I’m not too stressed about performance at the moment. There is no doubt in my mind at all that C# is magnitudes slower than C for this sort of work, but development time is significantly less and for a prototype that’s what matters.

I still have a bunch of work to do before I fully understand stereo vision and the associated camera calibration requirements but I am getting there.

It’s been ages since I did any work on my vision project and I find myself struggling to find the motivation. I have been stuck on a tricky problem for a while now and I think I need a breakthrough to get some interest back. The problem is that the calculations I am using to calculate the real world 3d coordinates of features are giving completely nonsensical answers. I am having trouble establishing whether the formulas I am using are wrong, or if the feature matching isn’t working properly and I am trying to work out pose information for a bad stereo feature match…

If only I could find a site on the web somewhere that had a definitive discussion of calculating real world 3d coordinates from stereo images that would at least help discount the first option.

I’ve tons of fun over the last few days trying to debug my vision system. To be fair I haven’t really spent that much time on it yet, but it is a fairly complicated system and trying to guess which features in the image the computer is interested in and why is a bit of a challenge. To make things easier I have been trying to annotate the images as the feature detection process runs and save them to disk so I can analyse them later. It’s required the wrapping of more functionality of the opencv library so yet more fun with marshalling and P/Invoke!

The other thing that maybe complicating matters is that I’m not sure how well the SIFT detection works in low light conditions and since I do all my hacking in the evening…

Maybe a bit of dedicated hacking this weekend will help.

In other news I am off to see Billy Corgan tomorrow night so am getting really amped for that!