Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Detecting the dominant points by the curvature-based polygonal approximation
CVGIP: Graphical Models and Image Processing
Zernike moment-based image analysis and its application
Pattern Recognition Letters
Digital Image Processing
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Curves vs. skeletons in object recognition
Signal Processing - Special section on content-based image and video retrieval
Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Path Similarity Skeleton Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A complex network-based approach for boundary shape analysis
Pattern Recognition
A skeleton and neural network-based approach for identifying cosmetic surface flaws
IEEE Transactions on Neural Networks
Texture analysis and classification: A complex network-based approach
Information Sciences: an International Journal
Approximate partitioning of 2D objects into orthogonally convex components
Computer Vision and Image Understanding
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This paper presents a novel methodology to shape characterization, where a shape skeleton is modeled as a dynamic graph, and degree measurements are computed to compose a set of shape descriptors. The proposed approach is evaluated in a classification experiment which considers a generic set of shapes. A comparison with traditional shape analysis methods, such as Fourier descriptors, Curvature, Zernike moments and Multi-scale Fractal Dimension, is also performed. Results show that the method is efficient for shape characterization tasks, in spite of the reduced amount of information present in the shape skeleton.