### BMVC 2004, Kingston, 7th-9th Sept, 2004

EM Clustering of Incomplete Data Applied to Motion Segmentation

K.Y. Wong, L. Ye and M.E. Spetsakis(York University, Canada)

Many clustering problems in Computer Vision group data points that are the result of

statistical estimation and these data points can have a great amount of uncertainty.

Motion segmentation by clustering of optical flow is such an example because very

often optical flow cannot be estimated without significant uncertainty. We present a EM

based clustering algorithm for incomplete data and we apply it to the problem of motion

segmentation. The input to the algorithm are the velocity likelihoods and the number of

clusters. The algorithm is mathematically very elegant because it does not impose any

constraints on the velocity likelihood thus multi-modal likelihood is modeled without

difficulty. Coupled with a sophisticated correlated image noise model, the algorithm can

handle substantial deviations from the intensity constancy assumption. Experiments

with real image sequences show excellent results.

(pdf article)