Pattern Recognition Project: Wood Species Recognition

This work intends to contribute to the study of wood texture classification by implementing and evaluating the performance of several feature extraction methods applied in combination with a variety of classification techniques. An exotic wood texture images dataset has been used to test the generated code. Implemented feature extraction methods comprise gray level co-occurrence matrix, mathematical morphology, ranklets, curvelets, wavelets and local binary patterns; tested classification techniques include k-nearest neighbour, linear discriminant classifiers, quadratic discriminant classifiers, neural networks, and support vector machines. Results have been evaluated based on computation time and classification accuracy, the highest success rate having been achieved by a novel scheme integrating X and X features introduced in this report.

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