Developing a computer vision method based on AHP and feature ranking for ores type detection

Detection of size, shape and color of minerals are important for obtaining information about minerals. The output of mines is ores which vary in colors and shapes. The multiplicity of ores, large scale features and the importance of speeding up the mineral type detection process for intelligent systems, leads us to rely more on expert's advice and rank the selected available features for type detection, according to their importance. In this paper, to separate different ores and gangue minerals, image processing and computer vision techniques with combination of multi criteria decision making (MCDM) approach are applied. Our method proposes a novel way which combines the image processing techniques and artificial neural networks, with analytic hierarchy process (AHP) approaches to detect different types of ores. By help of experts in feature ranking, the image processing techniques proved to be more effective and prompt. The final results show that the proposed method is more successful in type detection of minerals than the other image processing techniques for ores type detection. Our method is also applicable for real-time systems to estimate minerals at on-line ore sorting and classification stages.