Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Shape Description Via the Multiresolution Symmetric Axis Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
A pyramid algorithm for fast curve extraction
Computer Vision, Graphics, and Image Processing
Hierarchical Image Analysis Using Irregular Tessellations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiresolution algorithm for rotation-invariant matching of planar shapes
Pattern Recognition Letters
Detection and recognition of objects in time-varying using attention, representation, and memory pyramids
FORMS: a flexible object recognition and modeling system
International Journal of Computer Vision
Note on the multiscale representation of 2D and 3D shapes
Graphical Models and Image Processing
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Jump-Diffusion Process for Computing Medial Axes in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
Hierarchical Decomposition and Axial Shape Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Top-Down and Bottom-Up Analysis for a Multiscale Skeleton Hierarchy
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
Multiresolution Skeletonization in Binary Pyramids
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
A Lattice Approach to Image Segmentation
Journal of Mathematical Imaging and Vision
Higher order symmetry for non-linear classification of human walk detection
Pattern Recognition Letters
Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rectification of the chordal axis transform skeleton and criteria for shape decomposition
Image and Vision Computing
Journal of Visual Languages and Computing
Palmprint verification using hierarchical decomposition
Pattern Recognition
A similarity-based approach for shape classification using Aslan skeletons
Pattern Recognition Letters
Skeletonization of noisy images via the method of legendre moments
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Skeleton pruning by contour partitioning
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
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This paper presents a new procedure to hierarchically decompose a multiscale discrete skeleton. The skeleton is a linear pattern representation that is generally recognized as a good shape descriptor. For discrete images, the discrete skeleton is often preferable. Multiresolution representations are convenient for many image analysis tasks. Our resulting skeleton decomposition shows two different types of hierarchy. The first type of hierarchy is one of different scales, as the original pattern is converted into an AND-pyramid and the skeleton is computed for each resolution level. The second type of hierarchy is established at each level of the pyramid by identifying and ranking skeleton subsets according to their permanence, where permanence is a property intrinsically related to local pattern thickness. To achieve the decomposition, both bottom-up and top-down analysis in the sense of moving from higher to lower resolution and vice versa are used. The bottom-up analysis is used to ensure that a part of the skeleton that is connected at a higher resolution level is also connected (if at all present) in the next, lower resolution level. The top-down analysis is used to build the permanence hierarchy ranking the skeleton components. Our procedure is based on the use of (3 脳 3) local operations in digital images, so it is fast and easy to implement. This skeleton decomposition procedure is most effective on patterns having different thickness in different regions. A number of examples of decompositions of multiscale skeletons (with and without loops) will be shown. The skeletons are, in most cases, nicely decomposed into meaningful parts. The procedure is general and not limited to any specific application.