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Self-adaptive identification method for particles in asphalt mixture CT image

A technology of asphalt mixture and CT images, which is applied in the field of adaptive recognition of particles in asphalt mixture CT images, can solve the problem of low extraction accuracy and achieve the effect of improving the accuracy of mesostructure extraction

Pending Publication Date: 2020-08-18
HARBIN INST OF TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of low accuracy of extracting the mesoscopic structure of the mixture caused by the black core phenomenon in the center of the specimen in the CT image of the existing asphalt mixture, and propose a method for extracting the particles in the CT image of the asphalt mixture. adaptive recognition method

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  • Self-adaptive identification method for particles in asphalt mixture CT image
  • Self-adaptive identification method for particles in asphalt mixture CT image
  • Self-adaptive identification method for particles in asphalt mixture CT image

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specific Embodiment approach 1

[0049] Specific implementation mode one: combine figure 1 Describe this embodiment, the specific process of an adaptive recognition method for particles in an asphalt mixture CT image in this embodiment is:

[0050] Step 1. Determine the radius and center position of the specimen in the asphalt mixture CT image;

[0051] Step 2, performing void grayscale zeroing processing on the CT image of the determined specimen radius and circle center, to obtain a CT image after void grayscale zeroing processing;

[0052] Step 3, performing statistical gray distribution along the radial direction on the CT image processed by zeroing the gray scale of the gap;

[0053] Step 4, performing uniform processing on the brightness of the image after the step 3 statistically distributes the gray scale along the radial direction;

[0054] Step 5, performing filtering and noise reduction on the image after the homogenization processing in step 4;

[0055] Step 6, performing global threshold segm...

specific Embodiment approach 2

[0057] Specific embodiment two: the difference between this embodiment and specific embodiment one is: in the step one, determine the specimen radius and the center of circle position in the asphalt mixture CT image; the specific process is:

[0058] Because in the actual CT scanning process, the specimen is not always in the center position, which leads to the difference in the center position of the specimen in the image, and determining the center and radius of the specimen is of great significance for subsequent image processing operations, so It is necessary to identify the center and radius of the specimen in the image.

[0059]In the CT image of asphalt mixture: the gray value of voids < the gray value of asphalt mortar < the gray value of aggregate particles;

[0060] Step 11. Abstract the matrix of the entire asphalt mixture CT image into a Cartesian coordinate system. The row number and column number of the pixel points are the y coordinates and x coordinates of the ...

specific Embodiment approach 3

[0066] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: in the step two, the CT image of the determined specimen radius and the center of the circle is processed to zero the gray scale of the gap; the specific process is:

[0067] Since the extraction target of the present invention is aggregate, both the mortar and the void need to be removed as the background, and the mortar cannot be removed due to the existence of the black core and the overlap of the gray value of the aggregate, so first use multi-threshold segmentation and image subtraction Eliminate the gaps in the CT image specimen whose radius and center of the circle have been determined (the gap is found by threshold segmentation, because the gray value of the gap is relatively small, you can use the multi-threshold segmentation method to calculate multiple thresholds, representing The gray value of the gap is between a certain two thresholds, and the gap will b...

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Abstract

The invention discloses a self-adaptive identification method for particles in an asphalt mixture CT image, aiming to solve the problem of low extraction accuracy of a microstructure of a mixture caused by a black core phenomenon in the center of a test piece in an existing asphalt mixture CT image. The self-adaptive identification method comprises the following steps: 1, determining the radius and circle center position of a test piece in an asphalt mixture CT image; 2, performing gap gray level zeroing processing on the CT image; 3, performing statistical gray distribution on the CT image after the gap gray return-to-zero processing along the radial direction; 4, carrying out the homogenization of the brightness of the image after the statistical gray scale is distributed in the radial direction; 5, performing filtering and noise reduction on the image subjected to the homogenization processing; 6, performing global threshold segmentation on the image subjected to filtering and noisereduction; and 7, performing image morphological processing on the image after global threshold segmentation. The self-adaptive identification method is used for digital image processing and road engineering.

Description

technical field [0001] The invention relates to an adaptive recognition method for particles in CT images of asphalt mixtures. Digital image processing, road engineering. Background technique [0002] Asphalt mixture is a multi-phase composite material, each component material has great difference, randomness and variability, resulting in non-unique mesostructure, and the traditional macroscopic empirical evaluation method cannot effectively reflect its structure Composition, it is necessary to reveal the performance mechanism of asphalt mixture from a mesoscopic point of view. In order to accurately extract the mesoscopic structure of the mixture, it is necessary to develop an image processing method for CT images of asphalt mixture with higher precision. [0003] There are roughly five peaks in the grayscale histogram of the CT image of asphalt mixture, and each peak should represent in descending order of grayscale: background, voids and artifacts, asphalt mortar, aggreg...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/73G06T7/62G06T5/00G06T7/136
CPCG06T7/0002G06T7/73G06T7/62G06T7/136G06T2207/10081G06T2207/20036G06T5/70
Inventor 谭忆秋邢超张凯粱尊东
Owner HARBIN INST OF TECH
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