Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4969661 | Pattern Recognition | 2017 | 26 Pages |
Abstract
Optical properties of crystals are one of the most powerful features for mineral identification. In this study, a novel automated color-based mineral identification (MI) procedure is developed, which can efficiently incorporate color variations under plane-polarized (PPL) and cross-polarized (XPL) illumination modes. Any unknown mineral is registered by two 19-point sets (under PPL and XPL) in the CIELab color space and the proposed scheme tracks mineral color variations in the CIELab color space. If the corresponding PPL and XPL modified Hausdorff distances (with those of the known mineral) are within acceptable ranges, it can be recognized as a specific mineral. The method is easy to implement compared to methods that examine different color-based features. In addition, not much color information is lost as is the case with feature-based MI schemes. The proposed method, therefore, provides a significant improvement in discriminatory power and compared to conventional methods, the procedure enables reliable and consistent identification over a larger range of minerals.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Saeed Aligholi, Gholam Reza Lashkaripour, Reza Khajavi, Morteza Razmara,