Effect on the Performance of a Support Vector Machine Based Machine Vision System with Dry and Wet Ore Sample Images in Classification and Grade Prediction

@article{Patel2019EffectOT,
  title={Effect on the Performance of a Support Vector Machine Based Machine Vision System with Dry and Wet Ore Sample Images in Classification and Grade Prediction},
  author={Ashok Kumar Patel and Snehamoy Chatterjee and Amit Kumar Gorai},
  journal={Pattern Recognition and Image Analysis},
  year={2019},
  volume={29},
  pages={309-324}
}
The aim of the present study is to analysing the effect of water absorption on iron ore samples in the performances of SVM-based machine vision system. Two types of SVM-based machine vision system (classification and regression) were designed and developed, and performances were compared with dry and wet ore sample images. The images of the ore samples were captured in both the conditions (wet and dry) to examine the proposed model performance. A total of 280 image features were extracted and… CONTINUE READING

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