Neural recognition of minerals
The design of a neural network is presented for the recognition of six kinds of minerals (chalcopyrite, chalcosine, covelline, bornite, pyrite, and energite) and to determine the percentage of these minerals from a digitized image of a rock sample. The input to the neural network corresponds to the histogram of the region of interest selected by the user from the image that it is desired to recognize, which is processed by the neural network, identifying one of the six minerals learned. The network’s training process took place with 160 regions of interest selected from digitized photographs of mineral samples. The recognition of the different types of minerals in the samples was tested with 240 photographs that were not used in the network’s training. The results showed that 97% of the images used to train the network were recognized correctly in the percentage mode. Of the new images, the network was capable of recognizing correctly 91% of the samples.
