An Iterative Thresholding Algorithm for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Practical computer vision using C
Practical computer vision using C
Neural networks for pattern recognition
Neural networks for pattern recognition
Neural network design
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Image Processing
Computer and Robot Vision
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
Histogram-based segmentation in a perceptually uniform color space
IEEE Transactions on Image Processing
Engineering Applications of Artificial Intelligence
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In this study, an image analysis-based ore quality monitoring system was developed. The study was conducted at a limestone mine located in India. The samples were collected based on a stratified random sampling method, and images of these samples were taken in a simulated environment in a laboratory. The image preprocessing and segmentation were performed using different segmentation methods to extract morphological, colour and textural features. A total of 189 features was extracted during this study. Principal components analysis was conducted to reduce the feature vector for modeling purposes. Five principal components, which were extracted from the feature vectors, captured 95% of the total feature variance. A neural network model was used as a mapping function for ore grade prediction. The five principal components were used as input, and four grade attributes of limestone (CaO, Al"2O"3, Fe"2O"3 and SiO"2) were used as output. The developed model was then used for day to day quality monitoring at 3 different face locations of the mine. Results revealed that this technique can be successfully used for ore grade monitoring at the mine level in a controlled environment.