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Quick coal and rock recognition method based on DCT low-frequency component characteristics

A low-frequency component, coal and rock recognition technology, applied in the field of image recognition, can solve the problem of time-consuming calculation

Inactive Publication Date: 2015-01-28
CHINA UNIV OF MINING & TECH (BEIJING)
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Gray-level co-occurrence matrix uses image texture characteristics and gray-level co-occurrence matrix to identify objects, and extracts features directly from the image domain, and the calculation is very time-consuming

Method used

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  • Quick coal and rock recognition method based on DCT low-frequency component characteristics
  • Quick coal and rock recognition method based on DCT low-frequency component characteristics
  • Quick coal and rock recognition method based on DCT low-frequency component characteristics

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Experimental program
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Embodiment Construction

[0023] figure 1 It is the basic flow of the fast coal and rock identification method based on DCT low-frequency component features, see figure 1 Describe in detail.

[0024] A. Preprocess the collected coal and rock sample images and divide them into training set and test set

[0025] A sub-image with a pixel size of N is intercepted in the center of the image from a number of coal and rock sample images collected from the site of the coal-rock identification task, such as a coal mining face, with different illuminance and different viewpoints, such as 80×80 pixel size, if Color image, convert it to grayscale image. Further divide each sub-image into disjoint sub-blocks, such as 8×8 pixel size, select half of the sub-blocks as the training set for coal and rock respectively, and the other half as the test set;

[0026] B. Extract feature information vectors of images in training set and test set

[0027] Perform DCT transformation on the training set and test set, so that ...

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Abstract

The invention discloses a quick coal and rock recognition method based on DCT low-frequency component characteristics. According to the method, coal and rock image characteristic information is described through low-frequency components after DCT of images, and a coal and rock classifier is established through a learning vector quantitative method; in the recognition process, image characteristic vectors of the images to be recognized are extracted through a method similar to that of training images, the characteristic vectors of the images are input in the classifier, and recognition is conducted through the ratio of the characteristic vectors of the images to be recognized in the classifier. According to the method, the images in different degrees of illuminance and different viewpoints of coal and rock are used as training samples, the influence of illuminance and imaging viewpoint change is small, the recognition rate is high, and stability is good.

Description

technical field [0001] The invention relates to a fast coal and rock recognition method based on DCT low-frequency component features, and belongs to the technical field of image recognition. Background technique [0002] my country is a large coal-producing country. According to relevant data, the national coal output reached 3.7 billion tons in 2013. In the process of coal production, the identification and classification of coal and rocks will be used in production links such as drum coal mining, tunneling, top coal caving mining, and raw coal selection of gangue. The existing coal-rock identification mainly focuses on coal-rock interface identification, which is one of the keys to unmanned mining technology. During the production process, through the automatic recognition of the coal-rock interface, the adjustment of the cutting height of the shearer drum can improve the productivity and transportation efficiency of coal, reduce the wear and tear of equipment and labor ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 孙继平刘剑桥
Owner CHINA UNIV OF MINING & TECH (BEIJING)
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