Global and local characterization of rock classification by Gabor and DCT filters with a color texture descriptor

In the automatic classification of colored natural textures, the idea of proposing methods that reflect human perception arouses the enthusiasm of researchers in the field of image processing and computer vision. Therefore, the color space and the methods of analysis of color and texture, must be discriminating to correspond to the human vision. Rock images are a typical example of natural images and their analysis is of major importance in the rock industry. In this paper, we combine the statistical (Local Binary Pattern (LBP) with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion) and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptors named respectively Gabor Adjacent Local Binary Pattern Color Space Fusion (G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion (D-ALBPCSF) for the extraction of visual textural and colorimetric features from direct view images of rocks. The textural images from the two G-ALBPCSF and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2 and the intersection of histograms that we have adapted to color histograms. The results obtained allowed us to highlight the discrimination of the rock classes. The proposed extraction method provides better classification results for various direct view rock texture images. Then it is validated by a confusion matrix giving a low error rate of 0.8% of classification

Identification of cement manufacturing raw materials using machine vision

In the mining and manufacturing industry, there is a need for a non-extractive system to identify raw materials on conveying systems. Such a system would allow identification of raw materials on conveying systems preventing cross-contamination when the materials arrive at the final storage location. This project used machine vision techniques to identify cement manufacturing raw materials (clinker, gypsum and, limestone). Firstly, a representative sample (25 x 10kg samples of each material) was collected using a stratified random sampling procedure. This stratified random sampling procedure ensured the sample accurately represented the raw material in the stockpile. A dual purpose test bed and controlled lighting camera enclosure (for static model development and future dynamic system implementation) were constructed to minimise the effect of varying ambient light. This test bed and camera enclosure allowed the CMOS global shutter industrial camera to take twenty, 24bit colour images (8bit for each colour) of each sample. These images were catalogued and stored in a database for further model training and verification purposes. These images were pre-processed by a median filter which allowed any over saturated pixels (due to raw material surface moisture reflection) to have their intensity level reduced by replacing its value by the median value of its local neighbours. From the filtered image the individual red, green and blue (RGB) components were passed to a Histogram function which binned (255 bins for 8-bit colour) the various pixel intensities. The statistical features (weighted mean, skewness and kurtosis) of each colour's histogram were then stored in an array which then passed to the image feature database. A varying amount of feature arrays were used to train and verify the success of a probabilistic neural network (PNN) model. Initial optimisation of the PNN model was conducted using a local search algorithm which changed the smoothing parameter which achieved 94.83% accuracy. This model was then improved by implementing a Supervised Learning Probabilistic Neural Network (SLPNN). This model added data weight which changed the height of the Gaussian distribution function and input variable vector weight which changes the width of Gaussian distribution function. The implementation of the Supervised Learning Probabilistic Neural Network improved the models accuracy to 99.57%. Further model field testing will be required to verify the system in an operational environment where the camera enclosure will be subjected to dust, noise, varying temperatures and moisture. The Supervised Learning Probabilistic Neural Network outperforms the standard Probabilistic Neural Network which has been proven by this work. This work supports the claim that Machine Vision can be successfully be used to identify cement manufacturing raw materials with a high success rate. It also contributes to the literature by classifying clinker, gypsum and limestone in one body of work

Application of Near Infrared Sensors to Minerals Preconcentration

The aim of this project was to investigate the potential and suitability of the application of near Infrared spectroscopy/sensors in automatic preconcentration of complex ores. Two ore types (copper and platinum) were considered for investigation. The near infrared region of electromagnetic spectrum has been used for mineral mapping in the minerals industries. However, its application as a sensing technology in the sorting of base and precious metals is still minimal. In practice, a near infrared sensor can measure characteristic features of carbonate, hydroxyl and water groups contained in minerals and rocks. Successful sensor-based sorting requires a good understanding of the minerals and their distribution in an ore. For the copper ores, mineralogical analysis was carried out using QEMSCAN® and qualitative XRD analysis. XRF analysis was used to determine the copper concentration in the various particles. In addition to the XRF elemental analysis, copper values were calculated from copper bearing minerals in the ore. XRD analysis was performed on the platinum ore. Methods of ore sorting based on near infrared readings and near infrared active functional groups (-OH, H2O, and CO32-) were investigated and strategies developed for both ore types. In addition to external environmental influence, most minerals contain water in their chemical structure. Therefore, considering the H2O absorption feature(s) for ore sorting was not considered optimal. Strategies were developed which target the discrimination of either or both carbonate and hydroxyl bearing particles as waste. Individual particles spectra were analysed and absorption features assigned to the various chemical species and minerals responsible for the absorptions. Due to individual particle mineralogical variation, particles were classified either as products, waste or middlings. For copper ore, targeting only the calcite (carbonate) dominated particles for discrimination as waste provided a better option for preconcentration. Application for the platinum ores targeted the discrimination of chlorite, antigorite, and/or calcite dominated samples as waste. Compared with sample mineralogy, samples could be classified as product or waste using near infrared.Nigerian Tertiary Education Trust Fund (TETFUND) and Anglo America