Journal article
Self-organizing maps for the skeletonization of sparse shapes
IEEE transactions on neural networks, Vol.11(1), pp.241-248
01/01/2000
DOI: 10.1109/72.822527
PMID: 18249756
Abstract
This paper presents a method for computing the skeleton of planar shapes and objects which exhibit sparseness (lack of connectivity), within their image regions. Such sparseness in images may occur due to poor lighting conditions, incorrect thresholding or image subsampling. Furthermore, in document image analysis, sparse shapes are characteristic of texts faded due to aging and/or poor ink quality. Due to the lack of pixel level connectivity, conventional skeletonization techniques perform poorly on such (sparse) shapes. Given the pixel distribution for a shape, the proposed method involves an iterative evolution of a piecewise-linear approximation of the shape skeleton by using a minimum spanning tree-based self-organizing map (SOM). By constraining the SOM to lie on the edges of the Delaunay triangulation of the shape distribution, the adjacency relationships between regions in the shape are detected and used in the evolution of the skeleton. The SOM, on convergence, gives the final skeletal shape. The skeletonization is invariant to Euclidean transformations. The potential of the method is demonstrated on a variety of sparse shapes from different application domains.
Details
- Title: Subtitle
- Self-organizing maps for the skeletonization of sparse shapes
- Creators
- Rahul SinghVladimir CherkasskyNikolaos Papanikolopoulos
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on neural networks, Vol.11(1), pp.241-248
- DOI
- 10.1109/72.822527
- PMID
- 18249756
- ISSN
- 1045-9227
- eISSN
- 1941-0093
- Language
- English
- Date published
- 01/01/2000
- Academic Unit
- Computer Science
- Record Identifier
- 9984446068202771
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