Automatic recognition method for sintered ore essential mineral phase

An automatic identification and mineral phase technology, applied in character and pattern recognition, measuring devices, material analysis through optical means, etc., can solve the problems of low accuracy of statistical methods, easy misidentification, heavy workload, etc.

Inactive Publication Date: 2008-02-27
BAOSHAN IRON & STEEL CO LTD +1
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Problems solved by technology

Therefore, the manual method has many disadvantages, such as heavy workload, low efficiency, low accuracy of the statistical method of point-to-surface, and easy misrecognition.
[0004] The existing computer-based identification and analysis technology for iron ore sinter microscopic image tissue composition requires manual input of the gray threshold range of different components, because the grayscale of each component in the microscopic image varies with the experimental conditions. Changes, even experienced experimenters can hardly give an accurate classification gray threshold, and the method of manual intervention has great disadvantages in terms of efficiency and accuracy

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  • Automatic recognition method for sintered ore essential mineral phase
  • Automatic recognition method for sintered ore essential mineral phase
  • Automatic recognition method for sintered ore essential mineral phase

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Embodiment

[0077] A method for automatic identification of main mineral phases of sintered ore, the steps of which are:

[0078] 1. Sampling and analyzing the iron ore numbered 3DL0603, collecting a total of 400 pictures with a size of 800×800 pixels. First, the statistics of red, green, and blue channels and the grayscale histogram after grayscale conversion are carried out for each image; Green, blue and gray total histogram distribution; see Figure 3, Figure 3 is the original red total histogram distribution.

[0079] 2. Use the EM algorithm to perform histogram approximation to obtain four optimal thresholds, t1=62, t2=99, t3=145, t4=171, see FIG. 4 . Take M=5, the iteration error is 10 -3 , using the EM algorithm can achieve stability after an average of 200 iterations.

[0080] 3. After obtaining the optimal threshold, return to the first image to start the analysis and statistics process, and divide the image into component regions according to the threshold. See Fig. 5(a) for...

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Abstract

This invention relates to sintered mineral phase analysis techniques. It discloses a automatic identification method of major mineral phase in sintered minerals, the method is applicable for automatically find out the optimal threshold value under the condition that distribution of shade of gray of all types of sintered phase are mixed and the regular of peak value distribution is not obvious, that is based on the expectation maximization (EM) methods; using mathematical expectation maximization histogram approximation algorithm, through the fit of Gaussian distribution of different components to obtain the most optimized image segmentation gray threshold; analysis the digital microscopy image of the sintered minerals to acquire optimal threshold value parameters of segmentation of by hematite, magnetite, iron calcium, and silicate binder these five categories, so as to achieve the computer intelligent identification method of hematite, magnetite, iron calcium, and silicate binder, thereby achieving accurate statistical calculation of each phase content by computers, laid a solid and accurate basis for phase analysis of sintered mineral.

Description

(1) Technical field [0001] The invention relates to a mineral phase analysis technology of sintered ore, in particular to an automatic identification method for the main mineral phase of sintered ore. (2) Background technology [0002] Sinter is a man-made rich ore that is mixed with various materials and partially melted at high temperature. It is a heterogeneous phase and its mineral phase structure is very complex. The mineral phase structure of sinter usually has four major phases: hematite, magnetite, calcium ferrite, and silicate. In the microscopic images of sintered ore, the components of various phases are mixed and nested, the boundaries between the components are not clear, and the color information is not rich. Mixed together, the gray peak and distribution of some components are not obvious, which makes it difficult to find out the accurate gray threshold for image segmentation. These factors have caused great difficulties in the automatic identification and a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/84G01N21/25G06K9/46
Inventor 李咸伟邹丹平刘其真
Owner BAOSHAN IRON & STEEL CO LTD
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