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 graphbased method reduces the error around segmented foreground objects.
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Howe, Nicholas and Deschamps, Alexandra, "Better Foreground Segmentation Through Graph Cuts" (2004). Computer Science: Faculty Publications, Smith College, Northampton, MA.