NDLI: Research on image vectorization based on mathematical morphology

Content Provider	IEEE Xplore Digital Library
Author	Zhang Xiao-xian Zheng Guo-xun Fu Hao-hai Cao Li-ying
Copyright Year	2011
Description	Author affiliation: College of Information and Technology Science, Jilin Agricultural University, Changchun Jilin China (Cao Li-ying) \|\| College of Software, Changchun Institute of Technology, Changchun Jilin China (Zhang Xiao-xian; Zheng Guo-xun; Fu Hao-hai)
Abstract	Image vectorization plays an important role in the digital image processing. Because the traditional linear vectorization methods have some shortcomings including processing data slowerly, being sensitive to noises and being easy to be distorted, this paper proposes an image vectorization method based on mathematical morphology. This method consists of the image edge detection method based on the four structural elements template, the morphological sequential homotypic skeleton abstraction method based on the eight structural elements template and the vectorization method based on the dynamic change of pace about Freeman's chain code. Examples have proved that this method can abstract high precision skeletons rapidly and attain high accuracy vector data measured by the arc segment. This method has many advantages, such as the faster processing rate, the higher accuracy and the less storage space. So it has certain feasibility and practicability in digital image vectorization.
Starting Page	1179
Ending Page	1182
File Size	242043
Page Count	4
File Format	PDF
ISBN	9781612847191
e-ISBN	9781612847221
DOI	10.1109/MEC.2011.6025677
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Image edge detection Noise Morphology Transforms Image vectorization Skeleton Mathematical model Mathematical morphology Edge detection Skeleton abstraction
Content Type	Text
Resource Type	Article

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