DocumentCode
3128558
Title
Mean shift analysis and applications
Author
Comaniciu, Dorin ; Meer, Peter
Author_Institution
Dept. of Electr. & Comput. Eng., Rutgers Univ., Piscataway, NJ, USA
Volume
2
fYear
1999
fDate
1999
Firstpage
1197
Abstract
A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatial-range (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven. The proposed filtering method associates with each pixel in the image the closest local mode in the density distribution of the joint domain. Segmentation into a piecewise constant structure requires only one more step, fusion of the regions associated with nearby modes. The proposed technique has two parameters controlling the resolution in the spatial and range domains. Since convergence is guaranteed, the technique does not require the intervention of the user to stop the filtering at the desired image quality. Several examples, for gray and color images, show the versatility of the method and compare favorably with results described in the literature for the same images
Keywords
computer vision; image segmentation; color images; density gradient; discontinuity preserving filtering; gray level; image segmentation; lattices; mean shift analysis; nonparametric estimator; spatial-range domain; Application software; Color; Computer vision; Convergence; Image quality; Image segmentation; Kernel; Lattices; Pixel; Spatial resolution;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on
Conference_Location
Kerkyra
Print_ISBN
0-7695-0164-8
Type
conf
DOI
10.1109/ICCV.1999.790416
Filename
790416
Link To Document