Abstract:

Finger vein image has a low contrast and polarization characteristics and traditional segmentation algorithms can not solve these problems. C-V model contains the global information of the image, it can detect weak edge information. This article improves based on the classical model of the C-V model, increases the distance penalty term and edge detection function for fast and accurate finger vein image segmentation, it also solves the problem of uneven distribution of gray which the classical CV model could not solve. The improved algorithm preserves the characteristics of global optimization C-V model while effectively avoiding the re-initialization process, and it is more sensitive to edge. The simulation results show that the improved C-V model is better able to deal with the problem of fuzzy boundaries and uneven gray, it also can improve the efficiency of segmentation.

Key words: C-V model, distance penalty term, edge detection function, finger vein image, image segmentation