CN110738674A - A texture feature measurement method based on particle density - Google Patents

Abstract

A texture feature measurement method based on particle density, in the field of bubble flotation, the present invention proposes a texture feature measurement method based on particle density, the method extracts bubble images based on a digital image acquisition system set on site, and proposes The concept of particle area is introduced, and the particle area on the surface of the bubble is accurately extracted, and the extracted particle area is classified according to the position of the center point, and then the density between the particle areas is measured, and the particle density of texture features is defined. , to reflect the texture features of the whole image. It effectively makes up for the defect that the traditional texture feature extraction method does not consider the particles on the surface of the bubble, so that it can more accurately judge the working conditions and effectively guide the dosing.

Description

A texture feature measurement method based on particle density

technical field

The invention belongs to the technical field of froth flotation, and in particular relates to a texture feature measurement method in the process of zinc flotation.

Background technique

Froth flotation is a beneficiation method widely used at home and abroad. This method can effectively separate the target minerals according to the difference between the hydrophilicity and hydrophobicity of the mineral surface. In the process of froth flotation, the target minerals and their symbiotic gangue are ground into particles of suitable size and then sent to the flotation tank. The surface properties of different mineral particles are adjusted by adding chemicals. A large number of bubbles with different size, shape, texture and other characteristic information are formed in the flotation, so that the useful mineral particles adhere to the surface of the bubbles, and the bubbles carry the mineral particles to the surface of the flotation cell to form a bubble layer, and the gangue minerals remain in the pulp, thereby To achieve mineral sorting. Due to the long process flow of flotation, the unclear internal mechanism, many influencing factors, many variables involved and serious nonlinearity, and the process index cannot be detected online, the flotation process has always relied on the artificial naked eye to observe the bubble state on the surface of the flotation cell. To complete the field operation, this production method is highly subjective, and it is difficult to achieve objective evaluation and cognition of the flotation bubble state, resulting in frequent fluctuations in flotation production indicators, serious loss of mineral raw materials, large consumption of chemicals, and low resource recovery rate. The situation occurs, especially in today's increasingly scarce high-grade mineral resources, the flotation ore source is complex in composition and the mineral grade is low, and the manual operation of flotation production is more difficult to effectively carry out. Applying machine vision to the flotation process, using digital image processing technology to analyze the flotation bubble image, can achieve an objective description of the bubble state, and then further search and analyze the relationship between bubble characteristic parameters and process indicators, so as to The production automation of the flotation process was advanced. Flotation bubbles show a special texture state with different flotation states. The texture of the bubble image is a comprehensive reflection of the surface roughness, contrast and viscosity of the bubbles. Production operation variables are closely related to flotation production indicators such as concentrate grade and tailings content. The current extraction method of bubble texture information mainly extracts local features, but the extraction accuracy is not enough and the bubble surface particles are not considered in the texture extraction process. It is difficult to accurately reflect the working conditions. In fact, some small ore or magazine particles are often attached to the surface of the bubble, causing the surface of the bubble to be rough. The number and distribution density of these small particles are related to the grade of zinc concentrate. Aiming at the problem that the particles on the surface of bubbles have not been considered in previous studies, a new texture feature measurement method based on particle density is proposed. The particle area on the surface of the bubble is accurately extracted, and the density between the particle areas is quantitatively measured, and a new texture feature particle density is defined to reflect the texture feature of the entire image, effectively avoiding the traditional texture feature extraction. Limitations of the method, so as to more accurately judge the working conditions and effectively guide the dosing.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a texture feature measurement method based on particle density. In flotation production, the surface texture of flotation bubbles is an important visual information reflecting ore grade, which is closely related to flotation conditions and directly reflects bubbles. The degree of mineralization of the bubble layer. Aiming at the problem that the existing texture feature extraction of flotation bubble images does not consider the particles on the surface of the bubble, a texture feature measurement method particle density is defined. The method firstly segmented the bubble image, extracted the bubbles of interest, then extracted the particle area on the surface of the bubble, and analyzed how the particle density reflects the working conditions, indicating that it can more accurately classify minerals. To regulate and guide flotation production.

The technical solution steps adopted are as follows:

Step 1: Use the flotation on-site image acquisition system to collect the bubble video of zinc flotation and convert the bubble video into continuous images, and perform data preprocessing on the collected zinc flotation image data, as follows:

1) Eliminate erroneous data that exceeds the normal change threshold;

2) Eliminate incomplete data;

Step 2: Convert the bubble image from an RGB color image to a grayscale image, and obtain the grayscale matrix A of the image:

e _gf represents the gray value corresponding to each pixel in the gray image, where g∈N, f∈N, N∈(400,800).

Step 3: In the bubble image, the regular shape of the bubble is smooth surface, the highlight point is located at the top of the convex surface of a single bubble, the highlight point area has the minimum gray value, and the highlight point is the center, and the gray value is downward. It gradually increases and reaches the maximum value when it reaches the boundary of the bubble; in fact, the bubble is often attached to some small ore or magazine particles, causing the surface of the bubble to be rough, and the number and distribution density of these small particles are closely related to the dosing. The amount is related to the grade of zinc concentrate;

First, segment the bubbles, use the watershed method to segment the bubbles to obtain h individual bubbles, and store the grayscale matrix of each bubble to obtain the grayscale matrix set of a single bubble B={b ₁ ,b ₂ ,b ₃ ,...,b _λ ,...,b _h }, b _λ is the λ-th bubble grayscale numerical matrix, h is the total number of In the bubble image collection, the bubbles with a bubble size greater than 1200 pixels are screened out, which is the region of interest. For the filtered bubbles, the grayscale value of a single bubble is used to replace the grayscale value of the highlighted part of the bubble. , obtain the gray-scale matrix set C={c ₁ ,c ₂ ,c ₃ ,...,c _ε ,...,c _K } of the bubbles to be detected, where K is the number of bubbles that meet the bubble size requirements.

Step 4: Detection of particle area:

1. In the non-particle area, the surface of the bubble is smooth, and the variation range of the gray value is within the gradient range;

2. The change of gray value in the particle area is beyond the gradient range;

Use the following steps to extract particle regions:

S1: The search method for defining the particle area of the divided and filtered bubbles: for the bubble c _ε , take eight directions of 0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°, The grayscale values of all pixels in any direction in the bubble form an array, the maximum width of a single bubble is a finite value, and the number of columns of the leftmost pixel of the bubble in the grayscale matrix is H _m , Note that the number of columns of the rightmost pixel point of the bubble in the gray matrix is from H _n , when searching, starting from the column H _m in the gray matrix of the bubble, from left to right, and starting from the upper part of the boundary of the bubble and starting from the top Start the search downward, and the initial search direction is 270°;

S2: Mark the trip point:

(1) Set the threshold value of grayscale gradient to [0,8], first start the search from the leftmost pixel point of the upper half boundary of the bubble, start the search from the direction of 270°, take the step size as 1, and start the search in the direction of 270° The modulo of the difference between the upper and next gray values and the current gray value is defined as the gray gradient value, and the gray gradient value is compared with the threshold value. If the gray gradient value exceeds the threshold value, the current gray value position is marked as jump. Change point d ₁ , the position is expressed in Cartesian coordinates as: (x ₁ , y ₁ ), the column H ₀ where the first jump point is located is recorded as the left boundary of the particle area, where H _m <...<H ₀ < H ₁ <H ₂ <...<H _k <H _k+1 <H _k+2 <...<H _n , H ₁ is the first column after H ₀ , and so on. Column H ₀ is searched to the right, and no jump point is found until column H _k+2 , then column H _k+1 is the right boundary of this particle area;

(2) From column H ₁ to column H _k , each column is searched in the direction of 270° from top to bottom, and two jump points are obtained. When searching column H ₁ , record the ray directions of 90 degrees and 270 degrees. The jump points are d ₂ and d ₃ , and the midpoint of the positions of the two jump points is taken as the position center divergence point e ₁ ; the position divergence center point e ₁ is taken as the starting point, and the search starts on both sides according to the 0° and 180° ray directions , mark the position of the nearest jump point, d ₄ , d ₅ ; take the position divergence center point as the starting point, start searching on both sides according to the 45° and 225° ray directions, and mark the position d ₆ , d ₇ of the nearest jump point ; Take the position divergence center point as the starting point, start searching on both sides according to the 135° and 315° ray directions, and mark the positions d ₈ and d ₉ of the nearest jump point;

S3: Extraction of particle area:

For bubble c _ε , 8 jump points are obtained from the position divergence center point e ₁ of H ₁ column, H ₂ , H ₃ ,...,H _k column position divergence center point e ₂ ,e ₃ ,... ., e _k get 8 jump points respectively, then the particle area gets 8k jump points in total, if 8k < 24, this area is noise, and it will not be considered, when 8k ≥ 24, these 8k jump points will be The particle regions are obtained by connecting them in sequence, denoted as D _r (r∈a).

Step 5: Determine the position of the center point of the particle area:

与顶点(x₁,y₁)相同，则该颗粒区域的面积如下式所示：For the particle region D _r , denote the number of transition points in the particle region as _tr , that is, the region boundary has _tr vertices, and the coordinates are (x _i , y _i ), i=1, 2,..., _tr , vertex

The same as the vertex (x ₁ , y ₁ ), the area of the particle region is given by the following formula:

如下式所示：The coordinates of the center point of the particle area

As shown in the following formula:

以不同颗粒区域中心点之间的直线距离定义一级、二级邻域区域以及其他邻域区域,对颗粒区域P_u,u∈a，定义如下：Repeat step 4 to search out all particle regions in the gray value matrix of a single bubble in the bubble set to be detected. For bubble c _ε , count the number of particle regions L _ε , and then determine the coordinates of the center point of each particle region

The primary and secondary neighborhood regions and other neighborhood regions are defined by the straight-line distance between the center points of different particle regions. For the particle region P _u , u ∈ a, the definitions are as follows:

where v∈a and v≠u;

Denote the number of primary neighborhood regions of the particle region P _u as _{qu , the number of secondary neighborhood regions as s u ,} and the number of other neighborhood regions as _wu , then there is qu _u +s _u +w _u =L _ε -1; the weights occupied by the number of first-level neighborhood regions, the number of second-level neighborhood regions, and the number of other neighborhood regions are set to 0.6, 0.3, and 0.1, respectively, then for the bubble c _ε , the texture feature quantity is defined The particle density Z _ε is expressed as follows:

Define the grain density G of the entire image, as shown in the following formula:

Step 6: Determine the working condition by particle density:

When in the state ①, the surface texture of the bubble is fine, the agent is excessive, and the mineral particles carried in the bubble exceed the bearing capacity of the bubble, causing the bubble to break a lot. In this case, the waste of the agent is not only serious, but also the grade of the concentrate is low. ;

When in state ②, the flotation agent is appropriate, the flotation performance is good, and the flotation production efficiency is the highest;

When it is in state ③, the viscosity of the pulp is low, the dosage of the agent is insufficient, the ore content of the bubbles is less, the water content is high, and the grade of the concentrate is low.

In step 2, the bubble image is converted from an RGB color image to a grayscale image, and the grayscale matrix A of the image is obtained, where N∈(400,800).

The invention defines a new texture feature particle density to reflect the texture feature of the entire image, effectively avoids the limitations of the traditional texture feature extraction method, and effectively overcomes the uneven illumination of the flotation scene to extract texture features. Therefore, it can more accurately judge the working conditions and effectively guide the dosing.

Description of drawings

Fig. 1 is a flow chart of a texture feature measurement method based on particle density.

FIG. 2 is a schematic diagram of the particle area extracted by S3 in step four.

Detailed ways

Figure 1 is a flow chart of the present invention.

Step 1: Use the flotation on-site image acquisition system to collect the bubble video of zinc flotation and convert the bubble video into continuous images, and perform data preprocessing on the collected zinc flotation image data, as follows:

1) Eliminate erroneous data that exceeds the normal change threshold;

2) Eliminate incomplete data;

Step 2: Convert the bubble image from an RGB color image to a grayscale image, and obtain the grayscale matrix A of the image:

e _gf represents the gray value corresponding to each pixel in the gray image, where g∈N, f∈N, N∈(400,800).

Step 3: In the bubble image, the regular shape of the bubble is smooth surface, the highlight point is located at the top of the convex surface of a single bubble, the highlight point area has the minimum gray value, and the highlight point is the center, and the gray value is downward. It gradually increases and reaches the maximum value when it reaches the boundary of the bubble; in fact, the bubble is often attached to some small ore or magazine particles, causing the surface of the bubble to be rough, and the number and distribution density of these small particles are closely related to the dosing. The amount is related to the grade of zinc concentrate;

First, segment the bubbles, use the watershed method to segment the bubbles to obtain h individual bubbles, and store the grayscale matrix of each bubble to obtain the grayscale matrix set of a single bubble B={b ₁ ,b ₂ ,b ₃ ,...,b _λ ,...,b _h }, b _λ is the λ-th bubble grayscale numerical matrix, h is the total number of In the bubble image collection, the bubbles with a bubble size greater than 1200 pixels are screened out, which is the region of interest. For the filtered bubbles, the grayscale value of a single bubble is used to replace the grayscale value of the highlighted part of the bubble. , obtain the gray-scale matrix set C={c ₁ ,c ₂ ,c ₃ ,...,c _ε ,...,c _K } of the bubbles to be detected, where K is the number of bubbles that meet the bubble size requirements.

Step 4: Detection of particle area:

1. In the non-particle area, the surface of the bubble is smooth, and the variation range of the gray value is within the gradient range;

2. The change of gray value in the particle area is beyond the gradient range;

Use the following steps to extract particle regions:

S1: The search method for defining the particle area of the divided and filtered bubbles: for the bubble c _ε , take eight directions of 0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°, The grayscale values of all pixels in any direction in the bubble form an array, the maximum width of a single bubble is a finite value, and the number of columns of the leftmost pixel of the bubble in the grayscale matrix is H _m , Note that the number of columns of the rightmost pixel point of the bubble in the gray matrix is from H _n , when searching, starting from the column H _m in the gray matrix of the bubble, from left to right, and starting from the upper part of the boundary of the bubble and starting from the top Start the search downward, and the initial search direction is 270°.

S2: Mark the trip point:

(1) Set the threshold value of the grayscale gradient to [0,8]. First, start the search from the leftmost pixel point of the upper half boundary of the bubble as the starting point, start the search from the direction of 270°, take the step size as 1, and start the search in the direction of 270°. The modulo of the difference between the upper, next gray value and the current gray value is defined as the gray gradient value, and the gray gradient value is compared with the threshold value. If the gray gradient value exceeds the threshold value, the current gray value position is marked as jump. Change point d ₁ , the position is expressed in Cartesian coordinates as: (x ₁ , y ₁ ), the column H ₀ where the first jump point is located is recorded as the left boundary of the particle region, where H _m <...<H ₀ < H ₁ <H ₂ <...<H _k <H _k+1 <H _k+2 <...<H _n , H ₁ is the first column after H ₀ , and so on. Column H ₀ is searched to the right, and no jump point is found until column H _k+2 , then column H _k+1 is the right boundary of this particle area;

(2) From column H ₁ to column H _k , each column is searched in the direction of 270° from top to bottom, and two jump points are obtained. When searching column H ₁ , record the ray directions of 90 degrees and 270 degrees. The jump points are d ₂ and d ₃ , and the midpoint of the positions of the two jump points is taken as the position center divergence point e ₁ ; the position divergence center point e ₁ is taken as the starting point, and the search starts on both sides according to the 0° and 180° ray directions , mark the position of the nearest jump point, d ₄ , d ₅ ; take the position divergence center point as the starting point, start searching on both sides according to the 45° and 225° ray directions, and mark the position d ₆ , d ₇ of the nearest jump point ; Take the position divergence center point as the starting point, start searching on both sides according to the 135° and 315° ray directions, and mark the positions d ₈ and d ₉ of the nearest jump point;

S3: Extraction of particle area:

For bubble c _ε , 8 jump points are obtained from the position divergence center point e ₁ of H ₁ column, H ₂ , H ₃ ,...,H _k column position divergence center point e ₂ ,e ₃ ,... ., e _k get 8 jump points respectively, then the particle area gets 8k jump points in total, if 8k < 24, this area is noise, and it will not be considered, when 8k ≥ 24, these 8k jump points will be The particle regions are obtained by connecting them in sequence, denoted as D _r (r∈a). Figure 2 is a schematic diagram of the extracted particle regions.

Step 5: Determine the position of the center point of the particle area:

与顶点(x₁,y₁)相同，则该颗粒区域的面积如下式所示：For the particle region D _r , denote the number of transition points in the particle region as _tr , that is, the region boundary has _tr vertices, and the coordinates are (x _i , y _i ), i=1, 2,..., _tr , vertex

The same as the vertex (x ₁ , y ₁ ), the area of the particle region is given by the following formula:

如下式所示：The coordinates of the center point of the particle area

As shown in the following formula:

以不同颗粒区域中心点之间的直线距离定义一级、二级邻域区域以及其他邻域区域,对颗粒区域P_u,u∈a，定义如下：Repeat step 4 to search out all particle regions in the gray value matrix of a single bubble in the bubble set to be detected. For bubble c _ε , count the number of particle regions L _ε , and then determine the coordinates of the center point of each particle region

The primary and secondary neighborhood regions and other neighborhood regions are defined by the straight-line distance between the center points of different particle regions. For the particle region P _u , u ∈ a, the definitions are as follows:

where v∈a and v≠u;

Denote the number of primary neighborhood regions of the particle region P _u as _{qu , the number of secondary neighborhood regions as s u ,} and the number of other neighborhood regions as _wu , then there is qu _u +s _u +w _u =L _ε -1; the weights occupied by the number of first-level neighborhood regions, the number of second-level neighborhood regions, and the number of other neighborhood regions are set to 0.6, 0.3, and 0.1, respectively, then for the bubble c _ε , the texture feature quantity is defined The particle density Z _ε is expressed as follows:

Define the grain density G of the entire image, as shown in the following formula:

Step 6: Determine the working condition by particle density:

When in the state ①, the surface texture of the bubble is fine, the agent is excessive, and the mineral particles carried in the bubble exceed the bearing capacity of the bubble, causing the bubble to break a lot. In this case, the waste of the agent is not only serious, but also the grade of the concentrate is low. ;

When in state ②, the flotation agent is appropriate, the flotation performance is good, and the flotation production efficiency is the highest;

When it is in state ③, the viscosity of the pulp is low, the dosage of the agent is insufficient, the ore content of the bubbles is less, the water content is high, and the grade of the concentrate is low.

In step 2, the bubble image is converted from an RGB color image to a grayscale image, and the grayscale matrix A of the image is obtained, where N∈(400,800).

The invention defines a new texture feature particle density to reflect the texture feature of the entire image, effectively avoids the limitations of the traditional texture feature extraction method, and effectively overcomes the uneven illumination of the flotation scene to extract texture features. Therefore, it can more accurately judge the working conditions and effectively guide the dosing.

Claims (6)

1, A texture feature measurement method based on particle density, comprising the following steps:

step , collecting bubble videos of zinc flotation at historical moments by using a flotation field image collecting system, converting the bubble videos into multi-frame continuous images, and performing data preprocessing on collected zinc flotation image data;

step two: converting the bubble image from RGB color image to gray image to obtain gray matrix A of image

e_gfExpressing the gray value corresponding to each pixel points in the gray image, wherein g belongs to N, f belongs to N;

step three: dividing the bubbles, dividing the bubbles by a watershed method to obtain h individual bubbles, storing the gray matrix of each bubble to obtain a gray matrix set B ═ B of each bubble₁,b₂,b₃,...,b_λ,...,b_h}，b_λIs the lambda-th bubble gray value matrix, and in the divided single bubble image set, the bubble with the size larger than 1200 pixel values is screened out, and is marked as C ═ C₁,c₂,c₃,...,c_ε,...,c_KK is the number of single bubbles meeting the size requirement of the bubbles;

step four: detection of particle regions

The surface of the bubble in the non-particle area is smooth, the change range of the gray value is in the gradual change range, and the change of the gray value in the particle area exceeds the gradual change range, and the particle area is extracted by adopting the following steps:

s1: defining a searching mode of a particle area for the bubbles after segmentation and screening;

s2: marking a jumping point in the searching process of the particle area;

s3: after marking the jumping points, extracting particle areas;

step five: repeating the step four to search out all particle areas in the gray value matrix of the single bubble in the bubble set to be detected, and regarding the bubbles c after cutting and screening_εCounting the number L of particle regions_εThen, the coordinates of the center point of each particle region are determined

Defining class II neighborhood region and other neighborhood regions by the straight line distance between the center points of different particle regions, for the particle region P_uU ∈ a, defined as follows:

wherein v belongs to a and v is not equal to u;

marking particle region P_uThe number of -level neighborhood regions is q_uThe number of the second-level neighborhood region is s_uAnd the number of other neighborhood regions is w_uThen there is q_u+s_u+w_u＝L_ε-1, the weight of the number of level two neighborhood regions, the number of level two neighborhood regions and the number of other neighborhood regions is set to 0.6, 0.3, 0.1 respectively, then for bubble c_εDefining the grain density Z of the texture feature quantity_εThe expression is as follows:

the particle concentration G of the whole image is defined as shown in the following formula:

step six: the working condition is judged according to the particle concentration:

when the bubble is in the state ①, the surface texture of the bubble is fine, the medicament is excessive, the mineral particles carried in the bubble exceed the carrying capacity of the bubble, so that the bubble is greatly crushed, the medicament waste is serious, and the concentrate grade is low;

when the flotation agent is in the state of ②, the flotation agent is proper, the flotation performance is good, and the flotation production efficiency is high;

when the slurry is in the state of ③, the slurry viscosity is low, the agent addition is insufficient, the bubble ore content is low, the water content is high, and the concentrate grade is low.

2. The method for particle-density-based texture feature measurement according to claim 1, wherein the second step comprises converting the bubble image from RGB color image to gray-scale image, resulting in a gray-scale matrix A of the image, where N ∈ (400, 800).

3. The method of claim 1, wherein the step S1 includes defining a search pattern of particle areas for the segmented and filtered bubbles:

for bubble c_εTaking eight directions of 0 degree, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees and 315 degrees, at the bubble c_εThe gray values of all the pixel points in any directions form arrays, the maximum width of a single bubble is a finite value, and the column number of the pixel point on the leftmost side of the bubble in the gray matrix is H_mThe column number of the rightmost pixel point of the bubble in the gray matrix is H_nH from bubble gray matrix at search_mThe columns start searching from left to right and from top to bottom starting from the bubble top half boundary, with the initial search direction being 270.

4. The texture feature measurement method based on particle density, wherein in step four S2, the step of marking the jumping points for the search mode of dividing the particle area defined by the screened bubbles comprises the following steps:

1) setting the threshold value of gray gradation to [0,8 ]]Firstly, starting to search from the 270-degree direction by taking the leftmost pixel point of the boundary of the upper half part of the bubble as a starting point, taking the step length as 1, defining the modulus of the difference value between the lower gray values and the current gray value as a gray gradient value in the 270-degree direction, comparing the gray gradient value with a threshold value, and marking the position of the current gray value as a jump point d if the gray gradient value exceeds the threshold value₁Cartesian for positionThe molar coordinates are expressed as: (x)₁,y₁) th trip point located in column H₀Is denoted as the left boundary of the particle region, where H_m＜...＜H₀＜H₁＜H₂＜...＜H_k＜H_k+1＜H_k+2＜...＜H_n，H₁Is H₀Column , and so on, in this search pattern sequentially from H₀Column search to the right through H_k+2When the column is not searched for a trip point, H_k+1Column is the right boundary of this granular area;

2) from H₁Is listed to H_kBetween columns, every columns are searched from top to bottom in 270 deg. direction to obtain two jump points, and in the H th search₁When the column is arranged, the jumping points of the ray directions of 90 degrees and 270 degrees are recorded as d₂And d₃Taking the midpoint of the positions of the two jump points as a position center divergence point e₁(ii) a Diverging from the central point e by position₁Starting the search from both sides in the direction of the 0 DEG and 180 DEG rays as starting points, marking the position of the nearest trip point, d₄，d₅(ii) a Starting to search from the position divergence central point to two sides in the ray directions of 45 degrees and 225 degrees, and marking the position d of the nearest jump point₆，d₇(ii) a Starting to search from the position divergence center point to two sides in the ray directions of 135 degrees and 315 degrees, and marking the position d of the nearest jump point₈，d₉。

5. The texture feature measurement method based on particle concentration of claim 4, wherein in the step four S3, the extraction process of the particle region is as follows:

for bubble c_εFrom H₁The position of the column diverges from the center point e₁8 trip points, H, are obtained₂,H₃,...,H_kThe position of the column diverges from the center point e₂,e₃,...,e_kRespectively obtaining 8 jumping points, obtaining 8k jumping points in the particle region, if 8k is less than 24, the region is a noise point, not considering, when 8k is more than or equal to 24, connecting the 8k jumping points in sequence to obtain the particle region, and recording the particle region as D_r,r∈a。

6. The texture feature measurement method based on particle concentration of claim 5, wherein in the fifth step, the determination of the position of the center point of the particle region is performed by:

for the particle region D_rThe number of the transition points of the grain region is recorded as t_rI.e. the zone boundary has t_rA vertex with coordinates of (x)_i,y_i),i＝1,2,...,t_rVertex, point

And vertex (x)₁,y₁) Similarly, the area of the particle region is shown by the following formula:

center point coordinates of particle region

As shown in the following formula:

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