Evaluation of shape classification techniques based on the signature of the blob

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.

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