Temporal Stability of Current Segmentation

Posted: February 16, 2012 at 4:03 pm

So I had a chance to look at the data I dumped on Monday that shows the features of patches in relation to frame numbers. The unfortunate realization is that the centre position of the patches is not a good indicator that they should be merged. The reason why is that the segmentation is very unstable over time. My tolerance for merging patches is currently 3 pixels, but after looking at the data, some patches are as much as 70 pixels off from frame to frame, because the edges are so unstable. I hope this is caused by the mean-shift segmentation, which causes a huge computational load.

My next steps are to see if another method may be more stable. I asked on the OpenCV mailinglist and someone did mention that task independent segmentation is inherently problematic, as in the general case of segmentation is an open problem. It has been argued that humans can only do it so well because of top-down control processes influencing perception. Since the segmentation does not have to be perfect I’ll look at other, less perceptual, segmentation methods that may be more stable. One promising method is using an edge detector to bound a floodfill operation. This should be more stable over time, but the regions may be strangely shaped. Better than nothing.