An Alternative Approach to Computing Shape Orientation with an Application to Compound Shapes
International Journal of Computer Vision
Recycled paper visual indexing for quality control
Expert Systems with Applications: An International Journal
Morphological segmentation and classification of marble textures at macroscopical scale
Computers & Geosciences
Cascaded and hierarchical neural networks for classifying surface images of marble slabs
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Expert Systems with Applications: An International Journal
Robotics and Computer-Integrated Manufacturing
Automatic classification of granite tiles through colour and texture features
Expert Systems with Applications: An International Journal
Color texture analysis using CFA chromatic co-occurrence matrices
Computer Vision and Image Understanding
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In this paper, we present an automatic system and algorithms for the classification of marble slabs into different groups in real time in production line, according to slabs quality. The application of the system is aimed at the marble industry, in order to automate and improve the manual classification process of marble slabs carried out at present. The system consists of a mechatronic prototype, which houses all the required physical components for the acquisition of marble slabs images in suitable light conditions, and computational algorithms, which are used to analyze the color texture of the marble surfaces and classify them into their corresponding group. In order to evaluate the color representation influence on the image analysis, four color spaces have been tested: RGB, XYZ, YIQ, and K-L. After the texture analysis performed with the sum and difference histograms algorithm, a feature extraction process has been implemented with principal component analysis. Finally, a multilayer perceptron neural network trained with the backpropagation algorithm with adaptive learning rate is used to classify the marble slabs in three categories, according to their quality. The results (successful classification rate of 98.9%) show very high performance compared with the traditional (manual) system.