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
Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape classification using complex network and Multi-scale Fractal Dimension
Pattern Recognition Letters
A skeleton and neural network-based approach for identifying cosmetic surface flaws
IEEE Transactions on Neural Networks
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This paper presents a novel approach to shape characterization, where a shape skeleton is modelled as a dynamic graph, and its complexity is evaluated in a dynamic evolution context. Descriptors achieved by using this approach show to be efficient in the characterization of different shape patterns with different variations in their structure (such as, occlusion, articulation and missing parts). Experiments using a generic set of shapes are presented as also a comparison with traditional shape analysis methods, such as Fourier descriptors, Curvature, Zernike moments and Bouligand-Minkowski. Although the reduced amount of information present in the shape skeleton, results show that the method is efficient for shape characterization tasks, overcoming the traditional approaches.