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Least square image matting

Kun W., Nanning Z., Huilai Q.

Machine Graphics and Vision

2007

Vol. 16, No. 1/2

139-152

This paper addresses the well-known problem of natural image matting. Most of the previous matting algorithms require the user to define the tri-map, which is an inconvenient work and sometimes a burden, especially in a complex situation. This paper uses ceratain user defined foreground and background strokes to estimate the image matte. First we use a Gauss Markov random field to model the matting problem. Then we use the least square optimization approach to solve it. Experimental results show that our approach could properly handle confused boundaries. It also could deal with semi-transparent conditions such as fire etc.

Color image segmentation based on edge-preservation smoothing and soft C-means clustering

Bing C., Nanning Z., Ying W., Yongping Z., Zhihua Z.

Machine Graphics and Vision

2002

Vol. 11, No. 2/3

183-194

A new approach to color image segmentation is demonstrated here. The color iamge, which is usually in the RGB space, is translated into the CIE(Lab) color space. The three components are smoothed using a variation-based approach. By minimizing an energy functional with a non-convex regular function, we can get a smoothed image. During the iteraction, the edges of the image are preserved. A soft C-means clustering algorithm, which is an improvement on the hard C-means algorithm, is employed to segment them after smoothing. This algorithm overcomes the problem of dependence of the initializations, Finally, an experiment is given to show the effectiveness and robustness of the method.