Fundamentals of digital image processing
Fundamentals of digital image processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multiscale Fourier Descriptor for Shape Classification
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Nonparametric shape priors for active contour-based image segmentation
Signal Processing
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
An optimization on pictogram identification for the road-sign recognition task using SVMs
Computer Vision and Image Understanding
Goal evaluation of segmentation algorithms for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
Shape classification algorithm using support vector machines for traffic sign recognition
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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In this paper, we report a study of different preprocessing and classification techniques that can be applied to shape classification using the signature of the blob, or its FFT, as the main feature. Eight well-known classification methods were tested and compared. The results obtained show that, for shapes with a small to medium amount of distortion, all the methods obtained an almost 100% success probability. However, as distortion increased, those not based on the FFT performed better than the other algorithms, at the expense of a small increase in computational time. The samples employed for training and testing purposes were not hand-selected, but were generated by an application developed as part of this study. This application simulates the main distortions that can be produced by a real camera, including shifts, scalings, rotations, affine transformations and noise. We demonstrate that the use of these synthetic images for the training process, instead of manually selected ones, had proven to perform well with real images. A study of the false positive problem is also included, showing that, with the use of SVMs and careful selection of the training set, a large number of false positives can be discarded in the detection step.