Piecewise Linear Skeletonization Using Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Skeletonizing Reconfigurable Self-Organizing Model: Validation Through Text Recognition
Neural Processing Letters
Object shape extraction based on the piecewise linear skeletal representation
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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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 sub-sampling. Furthermore, in document image analysis, sparse shapes are characteristic of texts faded due to aging and/or poor ink quality. 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