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Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification

Sebastian Budzan1, Dariusz Buchczik2, Marek Pawełczyk3

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This study introduces a machine vision technique for identifying and categorizing copper ore grains. The method effectively classifies grains by size and shape using advanced image processing, improving mineral processing efficiency.

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edge detectionfeature extractiongrain detectionseeded region growing segmentation

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Area of Science:

  • Materials Science
  • Geology
  • Computer Vision

Background:

  • Accurate characterization of mineral grains is crucial for efficient ore processing.
  • Traditional methods for grain analysis can be time-consuming and subjective.

Purpose of the Study:

  • To develop an automated machine vision system for copper ore grain detection and classification.
  • To enhance the accuracy and efficiency of mineral grain analysis.

Main Methods:

  • A novel approach combining seeded region growing segmentation and edge detection, limited to grain boundaries.
  • Utilizing 2D Fast Fourier Transform (2DFFT) and Gray-Level Co-occurrence Matrix (GLCM) for sample pre-processing and noise reduction.
  • Employing region growing enhanced by derivatives and modified Niblack's thresholding for grain detection, followed by shape feature extraction for classification.

Main Results:

  • The proposed method successfully detected and classified copper ore grains based on their shape features.
  • The system demonstrated efficiency in analyzing real copper ore samples of known granularity.
  • Information on multiple granularity fractions was generated simultaneously.

Conclusions:

  • The developed machine vision method offers an effective and automated solution for copper ore grain analysis.
  • This technique can significantly improve the precision and speed of mineral characterization in ore processing.
  • The feature extraction and classification approach provides valuable data for optimizing mineral recovery.