Automatic mineral classification in the macroscopic scale
Computers & Geosciences
Mountain Clustering on Non-Uniform Grids Using P-Trees
Fuzzy Optimization and Decision Making
A kernel-based subtractive clustering method
Pattern Recognition Letters
Image languages in intelligent radiological palm diagnostics
Pattern Recognition
IKNN: Informative K-Nearest Neighbor Pattern Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Automatic recognition of handwritten medical forms for search engines
International Journal on Document Analysis and Recognition
Mineral identification using color spaces and artificial neural networks
Computers & Geosciences
A pattern recognition based approach to consistency analysis of geophysical datasets
Computers & Geosciences
An automated mineral classifier using Raman spectra
Computers & Geosciences
Fast K-means algorithm based on a level histogram for image retrieval
Expert Systems with Applications: An International Journal
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The classification of rocks is an inherent part of modern geology. The manual identification of rock samples is a time-consuming process, and-due to the subjective nature of human judgement-burdened with risk. In the course of the study discussed in the present paper, the authors investigated the possibility of automating this process. During the study, nine different rock samples were used. Their digital images were obtained from thin sections, with a polarizing microscope. These photographs were subsequently classified in an automatic manner, by means of four pattern recognition methods: the nearest neighbor algorithm, the K-nearest neighbor, the nearest mode algorithm, and the method of optimal spherical neighborhoods. The effectiveness of these methods was tested in four different color spaces: RGB, CIELab, YIQ, and HSV. The results of the study show that the automatic recognition of the discussed rock types is possible. The study also revealed that, if the CIELab color space and the nearest neighbor classification method are used, the rock samples in question are classified correctly, with the recognition levels of 99.8%.