Component-Normalized Generalized Gradient Vector Flow for Snakes

The abstract should summarize the contents of the paper and should contain at least 70 and at most 150 words. It should be written using the abstract environment. Snakes, or active contours, have been widely used for image segmentation. An external force for snakes called gradient vector flow (GVF) largely addresses traditional snake problems of initialization sensitivity and poor convergence to concavities, and generalized GVF (GGVF) aims to improve GVF snake convergence to long and thin indentations. In this paper, we find and show that in the case of long and thin even-width indentations, GGVF generally fails to work. We identify the crux of the convergence problem, and accordingly propose a new external force termed as component-normalized GGVF (CN-GGVF) to eliminate the problem. CN-GGVF is obtained by normalizing each component of initial GGVF vectors with respect to its own magnitude. Comparisons against GGVF snakes show that the proposed CN-GGVF snakes can capture long and thin indentations regardless of odd or even widths with remarkably faster convergence speeds, and achieve lower computational complexity in vector normalization.