Mean Shift Analysis and Applications

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 ltering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven. The proposed ltering 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 associatedwith nearby modes. The proposedtechnique 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 ltering 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.

• Type: Conference
• Year: 1999
• Where: ICCV
• Authors: Dorin Comaniciu, Peter Meer
• Keywords: Closest Local Mode, Color Images, Computer Vision, Discontinuity Preserving Ltering, ICCV 1999, Image Segmentation, Mean Shift