Motion Segmentation by EM Clustering of Good Features

King Yuen Wong
Minas E. Spetsakis

Department of Computer Science
Centre for Vision Research
York University
4700 Keele Street
Toronto, ONTARIO
CANADA, M3J 1P3

Email addresses
kywong@cs.yorku.ca
minas@cs.yorku.ca

Abstract

We present a new algorithm that does motion segmentation by tracking
small textured patches and then clustering them using EM. A small
patch has the advantage that its motion is well modeled by uniform
flow and runs a lower risk of boundary inclusion. Inherently, a small
patch has less data so it is more susceptible to noise and it is not
well suited to fit locally higher order flow models. To overcome these
difficulties, we introduce a motion coherence detector to select only the best
features and an efficient statistical technique to compute
segment-wise affine flow from the EM clustering parameters. We
incorporate a residual noise model without any statistical
independence assumption and an efficient Chi square test for the noise
model to obtain dense segmentation. Computational efficiency is
striven for within a rigorous mathematical framework. Experiments with
real image sequences show good segments under a variety of conditions.

Paper URL Link

http://www.cs.yorku.ca/~kywong/pubs/victorIVR04.pdf