Computational geometry: an introduction
Computational geometry: an introduction
A thinning algorithm based on contours
Computer Vision, Graphics, and Image Processing
Computer processing of line images: a survey
Pattern Recognition
Thinning Methodologies-A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-Pass Parallel Thinning: Analysis, Properties, and Quantitative Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Generation of Skeletons from Discrete Euclidean Distance Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skeletons from dot patterns: a neural network approach
Pattern Recognition Letters
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
A fast parallel algorithm for thinning digital patterns
Communications of the ACM
A novel triangulation procedure for thinning hand-written text
Pattern Recognition Letters
A Euclidean Distance Transform Using Grayscale Morphology Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Rotation Invariant Rule-Based Thinning Algorithm for Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skeletonization of Ribbon-Like Shapes Based on a New Wavelet Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image thinning using pulse coupled neural network
Pattern Recognition Letters
A Fast Parallel Thinning Algorithm for the Binary Image Skeletonization
International Journal of High Performance Computing Applications
Analysis of stroke structures of handwritten Chinese characters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Skeletonization of ribbon-like shapes based on regularity andsingularity analyses
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Perceptually stable regions for arbitrary polygons
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Implementation of parallel thinning algorithms using recurrent neural networks
IEEE Transactions on Neural Networks
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The accuracy of a non-pixel-based skeletonization method is largely dependent on the contour information chosen as input. When using a Constrained Delaunay Triangulation to construct an object's skeleton, a number of contour pixels must be chosen as a basis for triangulation. This paper presents a new method of selecting these contour pixels. A new method for measuring skeletonization error is proposed, which quantifies the deviation of a skeleton segment from the true medial axis of a stroke in an image. The goal of the proposed algorithm is to reduce this error to an acceptable level, whilst retaining the superior efficiencies of previous non-pixel-based techniques. Experimental results show that the proposed method is adept at following the medial axis of an image, and is capable of producing a skeleton that is confirmed by a human's perception of the image. It is also computationally efficient and robust against noise.