Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of visual information retrieval
Principles of visual information retrieval
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Image Processing
A Stochastic Neural Model for Fast Identification of Spatiotemporal Sequences
Neural Processing Letters
Proceedings of the Second European Conference on Computer Vision
ECCV '92 Proceedings of the Second European Conference on Computer Vision
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Real-time shape description system based on MPEG-7 descriptors
Journal of Systems Architecture: the EUROMICRO Journal
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
UKSIM '08 Proceedings of the Tenth International Conference on Computer Modeling and Simulation
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
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In this article, we propose a new approach for fast recognition of objects from two-dimensional binary images using descriptors of curvature, the moment and an artificial neural network. This model associates a coefficient of certainty for each classification. Two image descriptors where used, the Hu moments and Curvature Scale Space, to provide a reduced representation invariant to image transformations, and a neural network applying a Gibbs distribution of probability is used to calculate the coefficient of certainty to link an image to one class. A benchmark data set is used to demonstrate the usefulness of the proposed methodology. The robustness of the proposed approach is also evaluated under rotation, scale transformations. The evaluation of the performance is based on the accuracy in the framework of a Monte Carlo experiment.