CN110728676A - Texture feature measurement method based on sliding window algorithm - Google Patents
Texture feature measurement method based on sliding window algorithm Download PDFInfo
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Abstract
The invention discloses a texture feature measuring method based on a sliding window algorithm, and belongs to the field of bubble flotation. The defect that the traditional texture feature extraction method does not consider the particles on the surface of the bubbles is effectively overcome, so that the working condition can be judged more accurately and the dosing can be effectively guided.
Description
Technical Field
The invention belongs to the technical field of froth flotation, and particularly relates to a texture feature measurement method in a zinc flotation process.
Background
The froth flotation is a mineral separation method widely used at home and abroad, and the method can effectively separate target minerals according to the difference between the hydrophilicity and the hydrophobicity of the surfaces of the minerals. In the froth flotation process, target minerals and gangue symbiotic with the target minerals are ground into particles with proper sizes and then sent into a flotation tank, different mineral particle surface properties are adjusted by adding medicaments, and meanwhile, the particles are continuously stirred and blown in the flotation process, so that a large number of bubbles with characteristic information such as different sizes, forms and textures are formed in ore pulp, useful mineral particles are adhered to the surfaces of the bubbles, the bubbles carry the mineral particles to rise to the surfaces of the flotation tank to form bubble layers, and gangue minerals are left in the ore pulp, and therefore mineral separation is achieved. Because the flotation process has long flow, an undefined internal mechanism, a plurality of influence factors, a plurality of related variables, severe nonlinearity, incapability of on-line detection of process indexes and the like, the flotation process mainly depends on manual visual observation of the bubble state on the surface of the flotation tank to complete on-site operation, the production mode has strong subjectivity, objective evaluation and cognition of the flotation bubble state are difficult to realize, the situations of frequent fluctuation of flotation production indexes, severe loss of mineral raw materials, large medicament consumption, low resource recovery rate and the like are caused, particularly in the present day that high-grade mineral resources are increasingly deficient, the flotation mineral source components are complex, the mineral taste is low, and the manual operation of flotation production is difficult to effectively carry out. Machine vision is applied to the flotation process, the flotation bubble image is analyzed by using a digital image processing technology, objective description of the bubble state can be realized, and then the relation between the bubble characteristic parameters and the process indexes is further searched and analyzed, so that the production automation of the flotation process is promoted. Flotation bubbles present a special texture state along with the difference of the flotation state, the texture of a bubble image is the comprehensive reflection of the roughness, the contrast and the viscosity of the bubble surface, the method is closely related to flotation production operation variables such as dosage, ventilation quantity and the like, the concentrate grade, the tailing content and other flotation production indexes, the current bubble texture information extraction method mainly extracts local features, the problems of insufficient extraction precision, no consideration of bubble surface particles in the texture extraction process and the like exist, the working condition is difficult to accurately reflect, in fact, some ores or magazine small particles are often attached to the bubble surface to cause the roughness of the bubble surface, the quantity and the distribution density of the small particles are closely related to the zinc concentrate grade, and the problem of the bubble surface particles is not considered in the previous research, the texture feature measurement method based on a sliding window algorithm is provided, according to the method, the bubble image is extracted based on the digital image acquisition system arranged on the site, then the particle areas on the surface of the bubbles are accurately extracted, the density among the particle areas is quantitatively measured, the new texture feature particle density is defined to reflect the texture features of the whole image, the limitation of the traditional texture feature extraction method is effectively avoided, and therefore the working condition is more accurately judged and the medicine adding is effectively guided.
Disclosure of Invention
The invention aims to provide a texture characteristic measuring method based on a sliding window algorithm. Aiming at the problem that the existing texture feature extraction of the flotation bubble image does not consider the particles on the surface of the bubble, the invention defines the particle density of the texture feature measurement method by using a sliding window algorithm. The method firstly segments the bubble image, then extracts the particle area on the bubble surface, and analyzes and explains how the particle concentration reflects the working condition, which shows that the method can more accurately regulate and control the mineral grade and guide the flotation production.
The technical scheme adopted by the invention comprises the following steps:
the method comprises the following steps: collecting bubble videos of zinc flotation by using a flotation field image acquisition system, converting the bubble videos into continuous images, and performing data preprocessing on the acquired zinc flotation image data as follows:
1) rejecting erroneous data that exceeds a normal variation threshold;
2) removing incomplete data;
step two: converting the bubble image from an RGB color image into a gray image to obtain a gray matrix A of the image:
egfexpressing the gray value corresponding to each pixel point in the gray image, wherein g belongs to N, f belongs to N, N belongs to (400, 800);
step three: in the bubble image, the conventional bubble shape is smooth in surface, the highlight point is positioned at the top end of the convex curved surface of a single bubble, the highlight point area presents the minimum gray value, the gray value gradually increases downwards by taking the highlight point as the center, and the maximum gray value is reached when the highlight point reaches the bubble boundary; in fact, the bubbles are often attached with some ore or magazine small particles, which cause the surface of the bubbles to be rough and uneven, and the quantity and distribution density of the small particles are related to the dosage and the zinc concentrate grade;
firstly, dividing bubbles, dividing the bubbles by a watershed method to obtain h individual bubbles, storing a gray matrix of each bubble to obtain a gray matrix set B ═ B of each bubble1,b2,b3,...,bλ,...,bh}，bλIs the lambda-th bubble gray value matrix, h is the total number of the bubbles after segmentation, in the single bubble image set after segmentation, screens the bubble that the bubble size is greater than 1200 pixel values, is the region of interest promptly, for the bubble after the screening, replaces the grey scale value of this bubble highlight point part with the grey scale mean value of single bubble, obtains to detect bubble gray matrix set C ═ { C ═ of detecting bubble1,c2,c3,...,cε,...,cKK is the number of bubbles meeting the bubble size requirement.
Step four: detection of particle area:
for the individual bubbles obtained after screening, the following operations are respectively performed on each bubble:
s1: taking subblocks with the size of 31 multiplied by 31 pixel points as a window, sliding from top to bottom and from left to right along the bubble boundary, and setting the step length to be 5;
s2: a sliding window algorithm is adopted to divide a single bubble image into a 31 x 31 rectangular sub-block region, the divided rectangular sub-block region is judged, and a particle region is screened out, wherein the judging method specifically comprises the following steps:
(1) displaying the gray value change of the sub-block area by using broken lines, wherein the horizontal axis represents the number of columns, the vertical axis represents the gray value, each broken line represents the gray value change of a corresponding row in the gray matrix, and then, the total number of the broken lines is 31, and all the broken lines are displayed on one graph at the same time;
(2) marking a gray line with a gray value of 205 on the gray images of all the rectangular sub-block areas, and then performing the following judgment:
q1 judging whether there is a broken line with T (T ≧ 3) row number difference less than 2 crossing the 205 gray line from bottom to top, if not, it means the image contains no particle area, if so, it marks the leftmost crossing point as E1Further executing judgment of Q2;
q2 calculating the slope k of the T polylines when they cross the gray scale line 2051,k2,...,kη,...,ktIf the condition k is satisfiedηIf the image size is more than or equal to 3.6, executing Q3 judgment, and otherwise, excluding the image;
q3 judging whether the T fold line passes through the 205 gray scale line from top to bottom on the gray scale image of the rectangular sub-block area, if not, excluding the image, if yes, marking the right-most passing point as E when passing back2Further executing judgment of Q4;
q4 calculation of E ═ E2-E1If E is less than or equal to 6.8, the rectangular sub-block region contains complete particles, and the rectangular sub-block region meeting the above condition is defined as a particle region.
Step five: defining the particle concentration:
all particle zonesThe domains are subblocks with pixel sizes of 31 × 31, the geometric center of the subblock, i.e., the pixel with coordinates (16,16) in the gray matrix of the subblock, is defined as the center point of the particle region, and the position of the center point in the gray matrix of the bubble corresponding to the particle region is expressed by cartesian coordinates as:namely the position of the central point of the particle region.
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 bubble cεCounting the number L of particle regionsεThen, the coordinates of the center point of each particle region are determined
Defining primary neighborhood region, secondary neighborhood region and other neighborhood regions by the straight line distance between the central points of different particle regions, and aligning the particle region PuU ∈ a, defined as follows:
wherein v belongs to a and v is not equal to u;
marking particle region PuThe number of the first-level neighborhood region is quThe number of the second-level neighborhood region is suAnd the number of other neighborhood regions is wuThen there is qu+su+wu＝Lε-1; the weights of the number of the first-level neighborhood regions, the number of the second-level neighborhood regions and the number of the other neighborhood regions are respectively set to be 0.6, 0.3 and 0.1, so that the bubble c is treatedεDefining the grain density Z of the texture feature quantityεThe 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.
In the second step, the bubble image is converted into a gray level image from an RGB color image, and a gray level matrix A of the image is obtained, wherein N belongs to (400, 800).
The invention defines new texture feature particle density to reflect the texture feature of the whole image, effectively avoids the limitation of the traditional texture feature extraction method, and effectively overcomes the influence of the uneven illumination phenomenon of the flotation field on the texture feature extraction, thereby more accurately judging the working condition and effectively guiding the dosing.
Drawings
Fig. 1 is a flow chart of a texture feature measurement method based on a sliding window algorithm.
Fig. 2 is a schematic diagram of the region of the particles extracted at S3 in step four.
Detailed Description
FIG. 1 is a flow chart of the present invention.
The method comprises the following steps: collecting bubble videos of zinc flotation by using a flotation field image acquisition system, converting the bubble videos into continuous images, and performing data preprocessing on the acquired zinc flotation image data as follows:
1) rejecting erroneous data that exceeds a normal variation threshold;
2) removing incomplete data;
step two: converting the bubble image from an RGB color image into a gray image to obtain a gray matrix A of the image:
egfexpressing the gray value corresponding to each pixel point in the gray image, wherein g belongs to N, f belongs to N, N belongs to (400, 800);
step three: in the bubble image, the conventional bubble shape is smooth in surface, the highlight point is positioned at the top end of the convex curved surface of a single bubble, the highlight point area presents the minimum gray value, the gray value gradually increases downwards by taking the highlight point as the center, and the maximum gray value is reached when the highlight point reaches the bubble boundary; in fact, the bubbles are often attached with some ore or magazine small particles, which cause the surface of the bubbles to be rough and uneven, and the quantity and distribution density of the small particles are related to the dosage and the zinc concentrate grade;
firstly, dividing bubbles, dividing the bubbles by a watershed method to obtain h individual bubbles, storing a gray matrix of each bubble to obtain a gray matrix set B ═ B of each bubble1,b2,b3,...,bλ,...,bh}，bλIs the lambda-th bubble gray value matrix, h is the total number of the bubbles after segmentation, in the single bubble image set after segmentation, screens the bubble that the bubble size is greater than 1200 pixel values, is the region of interest promptly, for the bubble after the screening, replaces the grey scale value of this bubble highlight point part with the grey scale mean value of single bubble, obtains to detect bubble gray matrix set C ═ { C ═ of detecting bubble1,c2,c3,...,cε,...,cKK is the number of bubbles meeting the bubble size requirement.
Step four: detection of particle area:
for the individual bubbles obtained after screening, the following operations are respectively performed on each bubble:
s1: taking subblocks with the size of 31 multiplied by 31 pixel points as a window, sliding from top to bottom and from left to right along the bubble boundary, and setting the step length to be 5;
s2: a sliding window algorithm is adopted to divide a single bubble image into a 31 x 31 rectangular sub-block region, the divided rectangular sub-block region is judged, and a particle region is screened out, wherein the judging method specifically comprises the following steps:
(3) displaying the gray value change of the sub-block area by using broken lines, wherein the horizontal axis represents the number of columns, the vertical axis represents the gray value, each broken line represents the gray value change of a corresponding row in the gray matrix, and then, the total number of the broken lines is 31, and all the broken lines are displayed on one graph at the same time;
(4) marking a gray line with a gray value of 205 on the gray images of all the rectangular sub-block areas, and then performing the following judgment:
q1 judging whether there is a broken line with T (T ≧ 3) row number difference less than 2 crossing the 205 gray line from bottom to top, if not, it means the image contains no particle area, if so, it marks the leftmost crossing point as E1Further executing judgment of Q2;
q2 calculating the slope k of the T polylines when they cross the gray scale line 2051,k2,...,kη,...,ktIf the condition k is satisfiedηIf the image size is more than or equal to 3.6, executing Q3 judgment, and otherwise, excluding the image;
q3 judging whether the T fold line passes through the 205 gray scale line from top to bottom on the gray scale image of the rectangular sub-block area, if not, excluding the image, if yes, marking the right-most passing point as E when passing back2Further executing judgment of Q4;
q4 calculation of E ═ E2-E1If E is less than or equal to 6.8, the rectangular sub-block region contains complete granules, the rectangular sub-block region satisfying the above condition is defined as the granule region, and FIG. 2 is a schematic diagram of the extracted granule region.
Step five: defining the particle concentration:
all the particle areas are subblocks with the pixel point size of 31 multiplied by 31, the geometric center of the subblock, namely the pixel point with the coordinate of (16,16) in the gray matrix of the subblock is defined as the central point of the particle area, and the central point is in the gray matrix of the bubble corresponding to the particle areaExpressed in cartesian coordinates as:
namely the position of the central point of the particle region.
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 bubble cεCounting the number L of particle regionsεThen, the coordinates of the center point of each particle region are determined
Defining primary neighborhood region, secondary neighborhood region and other neighborhood regions by the straight line distance between the central points of different particle regions, and aligning the particle region PuU ∈ a, defined as follows:
wherein v belongs to a and v is not equal to u;
marking particle region PuThe number of the first-level neighborhood region is quThe number of the second-level neighborhood region is suAnd the number of other neighborhood regions is wuThen there is qu+su+wu＝Lε-1; the weights of the number of the first-level neighborhood regions, the number of the second-level neighborhood regions and the number of the other neighborhood regions are respectively set to be 0.6, 0.3 and 0.1, so that the bubble c is treatedεDefining the grain density Z of the texture feature quantityεThe 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.
In the second step, the bubble image is converted into a gray level image from an RGB color image, and a gray level matrix A of the image is obtained, wherein N belongs to (400, 800).
The invention defines new texture feature particle density to reflect the texture feature of the whole image, effectively avoids the limitation of the traditional texture feature extraction method, and effectively overcomes the influence of the uneven illumination phenomenon of the flotation field on the texture feature extraction, thereby more accurately judging the working condition and effectively guiding the dosing.
Claims (4)
1. A texture feature measurement method based on a sliding window algorithm is characterized by comprising the following steps:
the method comprises the following steps: 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
egfExpressing the gray value corresponding to each pixel point in the gray image, wherein g belongs to N, and f belongs to N;
step (ii) ofThirdly, the method comprises the following steps: 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,...,bλ,...,bh}，bλIs 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,...,cε,...,cKK is the number of single bubbles meeting the size requirement of the bubbles;
step four: detection of particle regions
S1: for the bubbles after segmentation and screening, the subblocks with r multiplied by r pixel point size are taken as windows, the subblocks slide from top to bottom and from left to right along the foam boundary, and the step length is set to be 5;
s2: dividing a single foam image into r multiplied by r rectangular sub-block regions by adopting a sliding window method, and judging the divided rectangular sub-block regions to screen out particle regions;
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 bubble cεCounting the number L of particle regionsεThen, the coordinates of the center point of each particle region are determined
Defining primary neighborhood region, secondary neighborhood region and other neighborhood regions by the straight line distance between the central points of different particle regions, and aligning the particle region PuU ∈ a, defined as follows:
wherein v belongs to a and v is not equal to u;
marking particle region PuThe number of the first-level neighborhood region is quThe number of the second-level neighborhood region is suAnd the number of other neighborhood regions is wuThen there is qu+su+wu＝Lε-1; the weights of the number of the first-level neighborhood regions, the number of the second-level neighborhood regions and the number of the other neighborhood regions are respectively set to be 0.6, 0.3 and 0.1, so that the bubble c is treatedεDefining the grain density Z of the texture feature quantityεThe 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 of claim 1, wherein the second step comprises converting the foam image from an RGB color image to a gray scale image, resulting in a gray scale matrix A for the image, where N e (400, 800).
3. The texture feature measurement method based on the sliding window algorithm as claimed in claim 1, wherein the step four S2 includes:
taking r as 31, dividing a single foam image into 31 × 31 rectangular sub-block regions by adopting a sliding window method, and judging the divided rectangular sub-block regions to screen out particle regions, wherein the judging method specifically comprises the following steps:
(1) displaying the gray value change of the sub-block area by using broken lines, wherein the horizontal axis represents the number of columns, the vertical axis represents the gray value, each broken line represents the gray value change of a corresponding row in the gray matrix, and then, the total number of the broken lines is 31, and all the broken lines are displayed on one graph at the same time;
(2) marking a gray line with a gray value of 205 on the gray images of all the rectangular sub-block areas, and then performing the following judgment:
q1 judging whether there is a broken line with T (T ≧ 3) row number difference less than 2 crossing the 205 gray line from bottom to top, if not, it means the image contains no particle area, if so, it marks the leftmost crossing point as E1Further executing judgment of Q2;
q2 calculating the slope k of the T polylines when they cross the gray scale line 2051,k2,...,kη,...,ktIf the condition k is satisfiedηIf the image size is more than or equal to 3.6, executing Q3 judgment, and otherwise, excluding the image;
q3 judging whether the T fold line passes through the 205 gray scale line from top to bottom on the gray scale image of the rectangular sub-block area, if not, excluding the image, if yes, marking the right-most passing point as E when passing back2Further executing judgment of Q4;
q4 calculation of E ═ E2-E1If E is less than or equal to 6.8, the rectangular sub-block region contains complete particles, and the rectangular sub-block region meeting the above condition is defined as a particle region.
4. The texture feature measurement method based on the sliding window algorithm as claimed in claim 1, wherein the step five comprises: determination of the position of the center point of the particle region:
all the particle areas are subblocks with the pixel point size of 31 multiplied by 31, the geometric center of the subblock, namely the pixel point with the coordinate of (16,16) in the gray matrix of the subblock, is defined as the central point of the particle area, and the central point is opposite to the particle areaThe position in the grayscale matrix of the bubbles is expressed in cartesian coordinates as:
namely the position of the central point of the particle region.
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