Complex curve tracing based on a minimum spanning tree model and regularized fuzzy clustering

The fuzzy curve-tracing (FCT) algorithm can be used to extract a smooth curve from unordered noisy data. However, the model produces good results only if the curve shape is either opened or closed. In this paper, we propose several techniques to generalize the FCT algorithm for tracing complicated c...

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Published in:2004 International Conference on Image Processing, 2004. ICIP '04 Vol. 3; pp. 2091 - 2094 Vol. 3
Main Authors: Lam, B.S.Y., Hong Yan
Format: Conference Proceeding
Language:English
Published: Piscataway NJ IEEE 2004
Subjects:
Applied sciences
Artificial intelligence
Clustering algorithms
Computer science; control theory; systems
Data engineering
Data mining
Exact sciences and technology
Handwriting recognition
Information technology
Mathematical model
Noise shaping
Partitioning algorithms
Pattern recognition. Digital image processing. Computational geometry
Shape
Smoothing methods
Cluster analysis
Smoothing methods
Image processing
Modeling
Fuzzy logic
Fuzzy algorithm
Spanning tree
Classification
Chinese
Tracing
Eikonal approximation
Manuscript character
Signal to noise ratio
ISBN:0780385543, 9780780385542
ISSN:1522-4880
Online Access:Get full text
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Abstract The fuzzy curve-tracing (FCT) algorithm can be used to extract a smooth curve from unordered noisy data. However, the model produces good results only if the curve shape is either opened or closed. In this paper, we propose several techniques to generalize the FCT algorithm for tracing complicated curves. We develop a modified clustering algorithm that can produce cluster centers less dependent on the pre-specified number of clusters, which makes the reordering of cluster centers easier. We make use of the Eikonal equation and the Prim's algorithm to form the initial curve, which may contain sharp corners and intersections. We also introduce a more powerful curve smoothing method. Our generalized FCT algorithm is able to trace a wide range of complicated curves, such as handwritten Chinese characters.
AbstractList The fuzzy curve-tracing (FCT) algorithm can be used to extract a smooth curve from unordered noisy data. However, the model produces good results only if the curve shape is either opened or closed. In this paper, we propose several techniques to generalize the FCT algorithm for tracing complicated curves. We develop a modified clustering algorithm that can produce cluster centers less dependent on the pre-specified number of clusters, which makes the reordering of cluster centers easier. We make use of the Eikonal equation and the Prim's algorithm to form the initial curve, which may contain sharp corners and intersections. We also introduce a more powerful curve smoothing method. Our generalized FCT algorithm is able to trace a wide range of complicated curves, such as handwritten Chinese characters.
Author Lam, B.S.Y.
Hong Yan
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  surname: Hong Yan
  fullname: Hong Yan
  organization: Dept. of Comput. Eng. & Inf. Technol., Hong Kong City Univ., Kowloon, China
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Keywords Cluster analysis
Smoothing methods
Image processing
Modeling
Fuzzy logic
Fuzzy algorithm
Spanning tree
Classification
Chinese
Tracing
Eikonal approximation
Manuscript character
Signal to noise ratio
Language English
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Snippet The fuzzy curve-tracing (FCT) algorithm can be used to extract a smooth curve from unordered noisy data. However, the model produces good results only if the...
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SubjectTerms Applied sciences
Artificial intelligence
Clustering algorithms
Computer science; control theory; systems
Data engineering
Data mining
Exact sciences and technology
Handwriting recognition
Information technology
Mathematical model
Noise shaping
Partitioning algorithms
Pattern recognition. Digital image processing. Computational geometry
Shape
Smoothing methods
Title Complex curve tracing based on a minimum spanning tree model and regularized fuzzy clustering
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Volume 3
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