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The process of sorting marble plates according to their surface texture is animportant task in the automated marble plate production. Nowadays someinspection systems in marble industry that automate the classification tasksare too expensive and are compatible only with specific technological equipmentin the plant. In this paper a new approach to the design of an Automated MarblePlate Classification System (AMPCS),based on different neural network inputtraining sets is proposed, aiming at high classification accuracy using simpleprocessing and application of only standard devices. It is based on training aclassification MLP neural network with three different input training sets:extracted texture histograms, Discrete Cosine and Wavelet Transform over thehistograms. The algorithm is implemented in a PLC for real-time operation. Theperformance of the system is assessed with each one of the input training sets.The experimental test results regarding classification accuracy and quickoperation are represented and discussed.

Automated Marble Plate Classification System Based On Different Neural Network Input Training Sets and PLC Implementation