Shape extraction and classification of pizza base using computer vision

The quality of pizza base should be determined before further processing. However such classification is highly sensitive to human error. Image processing techniques can be used to extract shape features from digital images of pizza bases. The support vector machine (SVM) is the state-of-the-art classification technique and is capable of learning in high-dimensional feature space with less training data. In this paper, an algorithm for the shape extraction of pizza base and a new classification algorithm based on Fourier transform and SVM for shape grading of pizza bases are presented. The experiments showed that 86.7% classification accuracy was achieved with a linear SVM classifier, 95.0% with a polynomial SVM classifier, and 98.3% with a Gaussian radial basis function SVM classifier. The computer vision system developed has a great potential to assist in the automation of pizza base classification.

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