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A Study for Prediction of Minerals in Rock Images using Back Propagation Neural Networks

This paper presents a novel approach for the segmentation of ground based images of rocks using back propagation neural network architecture. The designed system actually identifies the possible minerals by analyzing the surface color of the rocks. The rocks in Balochistan are very hard and defined. Such rocks are typically full of minerals. The rocks in the province of Balochistan are peculiar in their shape and surface colour. Usually, these colours are developed due to the reaction of the particles of the minerals with air. The upper layer of dust upon these rocks can be really useful in identifying the possible minerals concealing inside the rocks. The designed mechanism uses conventional artificial neural networks to identify various coloured parts of the rocks which are further classified into different minerals using histograms. The BPNN helps to learn to solve the task through a dynamic adaptation of its classification context. The designed system is trained by providing it the basic information related to the physical features of various mineral and types of rocks. The designed system highlights the various parts of the images by using various colours for various minerals