I have been interested in image segmentation for over a decade, and from time to time, I extend our initial technique, called Statistical Region Merging (SRM, PAMI 2004).
Usually, time is a constraint, and many approaches have been designed (the usual suspects are mean shift and normalized cuts)
I have been concerned with the following problem: Can we design a segmentation algorithm that always (smoothly) improve with time?
I will present a first attempt (with a first theoretical analysis) at the 2nd IAPR Asian Conference on Pattern Recognition (ACPR2013).
It is fast and delivers soft contour segmentation (SRM is hard contour segmentation).