DocumentCode
3297665
Title
A pseudo-distance map for the segmentation-free skeletonization of gray-scale images
Author
Jang, Jeong-Hun ; Hong, Ki-Sang
Author_Institution
Div. of Electr. & Comput. Eng., POSTECH, South Korea
Volume
2
fYear
2001
fDate
2001
Firstpage
18
Abstract
In this paper we introduce a new tool, called a pseudo-distance map (PDM), for extracting skeletons from grayscale images without region segmentation or edge detection. Given an edge-strength function (ESF) of a gray-scale image, the PDM is computed from the ESF using the partial differential equations we propose. The PDM can be thought of as a relaxed version of a Euclidean distance map. Therefore, its ridges correspond to the skeleton of the original gray-scale image and it provides information on the approximate width of skeletonized structures. Since the PDM is directly computed from the ESF without thresholding it, the skeletonization result is generally robust and less noisy. We tested our method using a variety of synthetic and real images. The experimental results show that our method works well on such images
Keywords
computational geometry; edge detection; image thinning; partial differential equations; Euclidean distance map; edge-strength function; gray-scale images; grayscale images; partial differential equations; pseudo-distance map; segmentation-free skeletonization; skeletons extraction; Data mining; Equations; Euclidean distance; Gray-scale; Image edge detection; Image segmentation; Noise shaping; Robustness; Shape; Skeleton;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on
Conference_Location
Vancouver, BC
Print_ISBN
0-7695-1143-0
Type
conf
DOI
10.1109/ICCV.2001.937586
Filename
937586
Link To Document