@conference {cVaras14,
	title = {Region-based Particle Filter for Video Object Segmentation},
	booktitle = {CVPR - Computer Vision and Pattern Recognition},
	year = {2014},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Ohio},
	abstract = {<p>We present a video object segmentation approach that\&nbsp;extends the particle filter to a region-based image representation. Image partition is considered part of the particle filter measurement, which enriches the available information and leads to a re-formulation of the particle filter.\&nbsp;The prediction step uses a co-clustering between the previous image object partition and a partition of the current\&nbsp;one, which allows us to tackle the evolution of non-rigid\&nbsp;structures. Particles are defined as unions of regions in the\&nbsp;current image partition and their propagation is computed\&nbsp;through a single co-clustering. The proposed technique is\&nbsp;assessed on the SegTrack dataset, leading to satisfactory\&nbsp;perceptual results and obtaining very competitive pixel error rates compared with the state-of-the-art methods.</p>},
	keywords = {co-clustering, particle filter, segmentation, tracking},
	author = {David Varas and Marqu{\'e}s, F.}
}
