Human body auto-extraction using graph cuts

Grabcut is an Interactive segmentation algorithm, which is employed to extract the foreground of an image, but it works wrong frequently when the pixels around the foreground are similar to the pixels of the background. We advance a new cost function to correct the mistake after we analyze the reason why it works wrong. Experimental results show that the new cost function outperforms the original one. Moreover, based on the new cost function we propose, we advance an algorithm of human body auto-extraction, which can be used to extract human body from background without interaction of user.

[1]  Chiunhsiun Lin,et al.  A statistic approach to the detection of human faces in color nature scene , 2002, Pattern Recognit..

[2]  King Ngi Ngan,et al.  Locating facial region of a head-and-shoulders color image , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[3]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[4]  D. Greig,et al.  Exact Maximum A Posteriori Estimation for Binary Images , 1989 .

[5]  Abbes Amira,et al.  Accelerating colour space conversion on reconfigurable hardware , 2005, Image Vis. Comput..

[6]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[7]  Anil K. Jain,et al.  Face Detection in Color Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Rainer Lienhart,et al.  Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection , 2003, DAGM-Symposium.

[9]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.