Patterns and operators: the foundations of data representation
Patterns and operators: the foundations of data representation
A vectorizer and feature extractor for document recognition
Computer Vision, Graphics, and Image Processing
Computer processing of line images: a survey
Pattern Recognition
Continuous Skeletons from Digitized Images
Journal of the ACM (JACM)
Computer representation of planar regions by their skeletons
Communications of the ACM
Digital Picture Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Simulating the Grassfire Transform Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Representation Using a Generalized Potential Field Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skeletonization of Three-Dimensional Object Using Generalized Potential Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant property of contour: VPIUD with arbitrary neighbourhood
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
A Fast Parallel Thinning Algorithm for the Binary Image Skeletonization
International Journal of High Performance Computing Applications
Hardware Implementation of Skeletonization Algorithm for Parallel Asynchronous Image Processing
Journal of Signal Processing Systems
DGCI'09 Proceedings of the 15th IAPR international conference on Discrete geometry for computer imagery
On the computation of the {3, 4, 5} curve skeleton of 3D objects
Pattern Recognition Letters
Selecting anchor points for 2D skeletonization
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
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A sequential method of skeletonization for digital binary images is proposed. The method realizes in a discrete plane prairie-fire propagation model. The techniques of contour following is adopted to simulate the simultaneous fire spreading off of the grass perimeter. The algorithm contains two steps: (1) find and mark all of the contours of the input image during a conventional scan. Then, after the borders a 'lighted', (2) repeatedly examine the borders and strip deletable edge points until no more can be removed. In this way only the successive contours, but not the whole image, will be processed. The second step is subdivided as follows: first, the contours representing the current fire fronts are traced, all the edge points are memorized, and the next fire fronts are marked. Second, the algorithm verifies the memorized edge points and those marked only once are removed. Although no neighborhood's test is needed during this stage of peeling, the connectedness of objects to thinning is conserved. Insignificant spurs can, optionally, be removed during thinning.