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Splitting ore from X-ray image based on improved robust concave-point algorithm.

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This study introduces an intelligent ore segmentation method using X-ray images, improving accuracy by effectively handling pseudo-concave points on contours for better ore classification.

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Area of Science:

  • Mineral Processing
  • Image Analysis
  • Computational Geometry

Background:

  • Accurate ore segmentation from X-ray images is crucial for efficient ore classification and separation.
  • Conventional segmentation methods struggle with real-time processing, robustness, and accuracy in industrial ore production.

Purpose of the Study:

  • To propose an advanced ore segmentation method for pseudo-dual-energy X-ray images.
  • To overcome limitations of traditional methods in terms of speed, reliability, and precision.

Main Methods:

  • A novel method combining contour extraction, concave point detection, and a unique concave point matching module.
  • Adaptive thresholding, filtering, morphological operations, and vector-based concave point detection are employed.
  • A key contribution is the concave point matching module that uses auxiliary lines to eliminate interference from boundary points.

Main Results:

  • The proposed method successfully removes interference from pseudo-concave points on ore contours.
  • Achieved accurate segmentation results in industrial experiments using antimony ore X-ray images.
  • Demonstrated superior performance compared to conventional segmentation techniques.

Conclusions:

  • The developed intelligent segmentation method effectively processes X-ray images of ore.
  • It meets the demanding requirements of real-time, robust, and accurate ore segmentation in industrial settings.
  • This approach enhances the accuracy of ore classification by improving segmentation quality.