Improved Euclidean transform method using Voronoi diagram
Jang Seok Hwan, Park Yong Sup, Kim Whoi Yul · 2004
In this paper, we present an improved method to calculate Euclidean distance transform based on Guan's method. Compared to the conventional method, Euclidean distance can be computed faster using Guan's method when the number of feature pixels is small; however, overall computational cost increases proportional to the number of feature pixels in an image. To overcome this problem, we divide feature pixels into two groups: boundary feature pixels (BFPs) and non-boundary feature pixels (NFPs). Here BFPs are defined as those in the 4-neighborhood of foreground pixels. Then, only BFPs are used to calculate the Voronoi diagram resulting in a Euclidean distance map. Experimental results indicate that the proposed method takes 40 Percent less computing time on average than Guan's method. To prove the performance of the proposed method, the computing time of Euclidean distance map by proposed method is compared with the computing time of Guan's method in 16 images that are binary and the size of 512512.