  For many tracking and surveillance applications, background subtraction
provides an effective means of segmenting objects moving in front of a static
background. Researchers have traditionally used combinations of morphological
operations to remove the noise inherent in the background-subtracted result.
Such techniques can effectively isolate foreground objects, but tend to lose
fidelity around the borders of the segmentation, especially for noisy input.
This paper explores the use of a minimum graph cut algorithm to segment the
foreground, resulting in qualitatively and quantitiatively cleaner
segmentations. Experiments on both artificial and real data show that the
graph-based method reduces the error around segmented foreground objects. A
MATLAB code implementation is available at
http://www.cs.smith.edu/~nhowe/research/code/#fgseg
