Learning and Design of Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Piecewise Linear Skeletonization Using Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
A k-segments algorithm for finding principal curves
Pattern Recognition Letters
Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Detection of Curved Text Path Based on the Fuzzy Curve-Tracing (FCT) Algorithm
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Unsupervised hierarchical image segmentation with level set and additive operator splitting
Pattern Recognition Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Convergence condition and efficient implementation of the fuzzy curve-tracing (FCT) algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Image Processing
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Extracting centrelines from an unordered data set is an important problem in image processing and pattern recognition. The key strategy of existing methods is to find a curve placed at the middle of the data points. However, if the data set contains noise, these algorithms will converge to a local minimum. In this paper, we propose a new technique, which combines the level set and affine transform methods. In this approach, the user-defined initial curve is driven towards the object data. Experimental results show that the proposed method can enhance the performance of a class of curve extraction algorithms effectively.