An interactive algorithm for soft segmentation and matting of natural images and videos is presented in this paper. The technique follows and extends Protiere et al. (2007), where the user first roughly scribbles/labels different regions of interest, and from them the whole data is automatically segmented. The segmentation and alpha matte are obtained from the fast, linear complexity, computation of weighted distances to the user-provided scribbles. These weighted distances assign probabilities to each labeled class for every pixel. The weights are derived from models of the image regions obtained from the user provided scribbles via kernel density estimation. The matting results follow from combining this density and the computed weighted distances. We present the underlying framework and examples showing the capability of the algorithm to segment and compute alpha mattes, in interactive real time, for difficult natural data.
['Xue Bai', 'Guillermo Sapiro']
Distancecut: Interactive Segmentation and Matting of Images and Videos