Better Foreground Segmentation Through Graph Cuts
<|reference_start|>Better Foreground Segmentation Through Graph Cuts: 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<|reference_end|>
@article{howe2004better,
    title={Better Foreground Segmentation Through Graph Cuts},
    author={Nicholas R. Howe & Alexandra Deschamps},
    journal={arXiv preprint arXiv:cs/0401017},
    year={2004},
    doi={10.1016/j.eswa.2010.09.137},
    archivePrefix={arXiv},
    eprint={cs/0401017},
    primaryClass={cs.CV}
}