TY - GEN
T1 - Combining region and edge cues for image segmentation in a probabilistic Gaussian mixture framework
AU - Rotem, Omer
AU - Greenspan, Hayit
AU - Goldberger, Jacob
PY - 2007
Y1 - 2007
N2 - In this paper we propose a new segmentation algorithm which combines patch-based information with edge cues under a probabilistic framework. We use a mixture of multiple Gaussians for building the statistical model with color and spatial features, and we incorporate edge information based on texture, color and brightness differences into the EM algorithm. We evaluate our results qualitatively and quantitatively on a large data-set of natural images and compare our results to other state-of-the-art methods.
AB - In this paper we propose a new segmentation algorithm which combines patch-based information with edge cues under a probabilistic framework. We use a mixture of multiple Gaussians for building the statistical model with color and spatial features, and we incorporate edge information based on texture, color and brightness differences into the EM algorithm. We evaluate our results qualitatively and quantitatively on a large data-set of natural images and compare our results to other state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=34948882758&partnerID=8YFLogxK
U2 - 10.1109/cvpr.2007.383232
DO - 10.1109/cvpr.2007.383232
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AN - SCOPUS:34948882758
SN - 1424411807
SN - 9781424411801
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
BT - 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
T2 - 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Y2 - 17 June 2007 through 22 June 2007
ER -