CN104165696A - Material surface color feature on-line automatic detection method - Google Patents
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Abstract
The invention relates to a material surface color feature on-line automatic detection method, and belongs to the technical field of automatic on-line detection of material surface quality. First a material standard sample is selected according to real-time environment and the material type, by adopting a plurality of high-precision cameras capable of covering the surface of the whole material standard sample to be detected to shoot a plurality of original images of the surface of the material standard sample to be detected, and after image processing, a standard color classification threshold value is obtained; for a material to be detected, by adopting the plurality of high-precision cameras capable of covering the surface of the whole material to be detected to shoot a plurality of original images of the surface of the material to be detected, and after image processing, using the obtained standard color classification threshold value to calculate the surface color features of material to be detected; and the surface color features of the material to be detected are compared with evaluation criteria, and surface color quality grading of the material to be detected is obtained through on-line automatic detection. The detection method has relatively high application value, and the method is simple and easy to implement.
Description
Technical field
The present invention relates to a kind of material surface color characteristic online automatic detection method, belong to the automatic on-line detection technique field of material surface quality.
Background technology
Material is the material base that the mankind depend on for existence and development.20 century 70 people are described as information, material and the energy on three large pillars of civilization in the present age.The new technology revolution of the eighties taking hi-tech group as representative, is listed as new material, infotech and biotechnology again the important symbol of new technology revolution.This is mainly because material and the development of the national economy, national defense construction and people's lives are closely related.Along with the raising of living standards of the people, people also bring up to a unprecedented height for the requirement of material quality.
Due to the complicacy of most of manufacture of materials technological processs, inevitably cause the unevenness of material surface color.Though the performance index of material are many, surface quality index is an of paramount importance class, mainly comprises surface color, surface imperfection and size etc.At present, the online surface quality of material detects and mostly still rests on artificial visually examine's sorting phase, and material is sent to by line conveyor, and workman contrasts it and the material of standard, and then the judged result of basis oneself is carried out classification.Not only labour intensity is large for this detection mode, precision and efficiency of detecting is low, and testing result is subject to the subjective factor impacts such as supervisory personnel's technical quality, experience, human eye resolution characteristic and visual fatigue, lack accuracy and standardization, easily generation is undetected determines inaccurate phenomenon with quality grade, and the detection means of this backwardness cannot meet the requirement of modern material industrial development.Therefore, realize material surface quality automatic on-line detect be necessary.
For manufacture of materials producer, the material of same model generally has several " looks number ", the batching of homochromy material, texture are almost not just the same, just visually difference to some extent of color, and many reasons such as this fluctuation mainly due to technological process and furnace temperature cause.Because this species diversity is very little, this just requires detection system can accurately distinguish this species diversity, then carries out material surface quality grading according to these differences, namely requires hierarchical algorithms should have suitable accuracy.Simultaneously because detection system finally will be applied to the online detection of industry spot, thereby require algorithm to there is real-time.So accuracy and real-time are with regard to the corresponding main target that becomes research.
Study in sum high precision, high-level efficiency and stable material surface color characteristic online automatic detection method and system, to saving labour, alleviate labor strength and improve the consistance of detection efficiency and testing result, there is stronger realistic meaning.
Summary of the invention
The problem and the deficiency that exist for above-mentioned prior art, the invention provides a kind of material surface color characteristic online automatic detection method.This detection method has higher using value, and method is simple, and the present invention is achieved through the following technical solutions.
A kind of material surface color characteristic online automatic detection method, its concrete steps are as follows:
(1) first according to real time environment and material category chosen material master sample, by adopting some the high precision video cameras that can cover whole material standard sample surface to be detected, take the surperficial original image of some parts of material standard samples to be detected, after image is processed, obtain Standard Colors classification thresholds;
(2) material to be detected is passed through to adopt some the high precision video cameras that can cover whole material surface to be detected, take the surperficial original image of some parts of materials to be detected, after image is processed, then the Standard Colors classification thresholds that adopts step (1) to obtain calculates the surface color feature of this material to be detected;
(3) the surface color feature and the evaluation criterion that step (2) are obtained to this material to be detected contrast, and obtain the surface color quality grading of this material online automatic detection to be detected.
The step that obtains Standard Colors classification thresholds in described step (1) is:
1.1 adopt matlab function imread to read in matlab the surperficial original image of the some parts of material standard samples to be detected of having taken successively;
Then 1.2 adopt matlab function rgb2gray that the surperficial original image in each step 1.1 is transformed into gray-scale map by cromogram;
1.3 adopt matlab function sort to arrange by ascending order its pixel each gray-scale map obtaining in step 1.2, arrange pixel according to gray-scale value order from small to large; And continue to adopt matlab function plot to simulate the curve map of the ascending order pixel of each image according to collating sequence point;
1.4 carry out segmentation according to the number of curve break in the curve map of the ascending order pixel obtaining in main color category and step 1.3 in master sample by the curve map of ascending order pixel, and the hop count of segmentation is corresponding with the main color of material successively;
1.5 curves that step 1.4 is obtained to corresponding hop count adopt matlab function plot to simulate corresponding curve map, and obtain the midpoint tangent line of every section of curve, the tangent line joining of adjacent curve is the threshold value that these two sections of colors are divided, each original image can obtain one group of threshold value, and in group, threshold value is arranged by order from small to large;
1.6 by all surfaces original image through step 1.1 to 1.5 processing and obtain group in threshold value mean value.
The step that described step (2) is calculated the surface color feature of this material to be detected is:
2.1 adopt matlab function imread to read in matlab the surperficial original image of taking some parts of materials to be detected successively;
Then 2.2 adopt matlab function rgb2gray that the surperficial original image in each step 2.1 is transformed into gray-scale map by cromogram;
2.3 adopt matlab function sort to arrange by ascending order its pixel each gray-scale map obtaining in step 2.2, arrange pixel according to gray-scale value order from small to large; And continue to adopt matlab function plot to simulate the curve map of the ascending order pixel of each image according to collating sequence point;
In the group that the curve map of the 2.4 ascending order pixels that step 2.3 is obtained obtains according to step 1.6, threshold value mean value is divided into the curve of corresponding hop count, obtain this color corresponding pixel and, each pictures can obtain pixel corresponding to one group of main color and;
2.5 by all surfaces original image through step 2.1 to 2.4 processing and obtain surface color respective pixel and the mean value of this material to be detected.
What the surface color quality of this material online automatic detection to be detected of described step (3) was graded is: the surface color respective pixel of this material to be detected of first step 2.5 being tried to achieve and mean value obtain respectively the pixel rate of specific gravity of main color divided by total pixel of this detected materials; Then compare according to pixel rate of specific gravity and this evaluation of material standard to be detected, can obtain the affiliated grade of this kind of material surface color characteristic.
As shown in Figure 1, above-mentioned material surface color feature on-line automatic detection device comprises the computing machine 1, manufacture of materials opertaing device 2, material production equipment 3, material transfer equipment 4 and the high precision camera 5 that connect successively.
The invention has the beneficial effects as follows: (1) solved current material enterprise in the time producing in enormous quantities because manual detection speed is slow, labour intensity is large, environment is severe, subjective factor affects the weak points such as larger to result; (2) material surface color characteristic real-time detection method of the present invention and system are simple, can be accurately, fast, reliably, online test material surface color feature in real time, and promptly provide material surface quality assessment; (3) the present invention is simple has an application prospect more widely, can obtain higher economic return; (4) detection method of the present invention and system can be carried out surface quality detection to the material of most kinds on market, can the maximum constraints of every kind of color be set to each quality grade according to the standard of producer oneself, and can as required, by professional, these restrictions be adjusted online.
Brief description of the drawings
Fig. 1 is the structural representation in the automatic testing process of the present invention;
Fig. 2 is the A material original image example of the embodiment of the present invention 1;
Fig. 3 is that the A material standard sampled pixel point gray-scale value ascending order of the embodiment of the present invention 1 is arranged exemplary plot;
Fig. 4 is the A material standard sampled pixel point first paragraph matching exemplary plot of the embodiment of the present invention 1, and wherein degree of fitting is 0.999;
Fig. 5 is the A material standard sampled pixel point second segment matching exemplary plot of the embodiment of the present invention 1, and wherein degree of fitting is 0.992;
Fig. 6 is the 3rd section of matching exemplary plot of A material standard sampled pixel point of the embodiment of the present invention 1, and wherein degree of fitting is 0.988;
Fig. 7 is the 4th section of matching exemplary plot of A material standard sampled pixel point of the embodiment of the present invention 1, and wherein degree of fitting is 0.991;
Fig. 8 is the gray-scale value threshold value exemplary plot that the A material surface color of the embodiment of the present invention 1 is divided;
Fig. 9 is the B material original image example of the embodiment of the present invention 2;
Figure 10 is that the B material standard sampled pixel point gray-scale value ascending order of the embodiment of the present invention 2 is arranged exemplary plot;
Figure 11 is the B material standard sampled pixel point first paragraph matching exemplary plot of the embodiment of the present invention 2, and wherein degree of fitting is 0.999;
Figure 12 is the B material standard sampled pixel point second segment matching exemplary plot of the embodiment of the present invention 2, and wherein degree of fitting is 0.999;
Figure 13 is the 3rd section of matching exemplary plot of B material standard sampled pixel point of the embodiment of the present invention 2, and wherein degree of fitting is 0.999;
Figure 14 is the 4th section of matching exemplary plot of B material standard sampled pixel point of the embodiment of the present invention 2, and wherein degree of fitting is 0.997;
Figure 15 is the 5th section of matching exemplary plot of B material standard sampled pixel point of the embodiment of the present invention 2, and wherein degree of fitting is 0.998;
Figure 16 is the gray-scale value threshold value exemplary plot that the B material surface color of the embodiment of the present invention 2 is divided.
In figure: 1-computing machine, 2-manufacture of materials opertaing device, 3-material production equipment, 4-material transfer equipment, 5-high precision camera.
Embodiment
Below in conjunction with the drawings and specific embodiments, the invention will be further described.
Embodiment 1
As shown in Figure 2, this A material is four kinds of colors: black, Dark grey, light gray and white, and wherein black and white is impurity, its content is more few better.The evaluation criterion of this A material is: (1) is (free from foreign meter) under normal circumstances, and Dark grey proportion is 30%, and light grey proportion is 70%, and acceptable error is (1%, 1%), meets this standard qualified, otherwise defective; (2) if impure, impurity (black and white) content is lower than 1%, and black and white is more qualified lower than 0.5% than weight average, otherwise defective, and while only having above two whiles qualified, this material surface quality is qualified, otherwise defective.
This material surface color characteristic online automatic detection method, its concrete steps are as follows:
(1) first according to real time environment and material category chosen material master sample, by adopting 6 high precision video cameras that can cover whole A material standard sample surface to be detected, take the surperficial original image of 50 parts of A material standard samples to be detected, after image is processed, obtain Standard Colors classification thresholds; The step that obtains Standard Colors classification thresholds is:
1.1 adopt matlab function imread to read in matlab the surperficial original image of 50 parts of material standard samples to be detected having taken successively;
Then 1.2 adopt matlab function rgb2gray that the surperficial original image in each step 1.1 is transformed into gray-scale map by cromogram;
1.3 adopt matlab function sort to arrange by ascending order its pixel each gray-scale map obtaining in step 1.2, arrange pixel according to gray-scale value order from small to large; And continue to adopt matlab function plot to simulate the curve map of the ascending order pixel of each image according to collating sequence point, as shown in Figure 3;
1.4 carry out segmentation according to the number of curve break in the curve map of the ascending order pixel obtaining in main color category and step 1.3 in master sample by the curve map of ascending order pixel, the hop count of segmentation is corresponding with the main color of material successively, this A material is four kinds of colors: black, Dark grey, light gray and white, curve in Fig. 3 is also to there being 3 turning points, therefore curve is divided into four sections, main segment of curve corresponding to color, black, Dark grey, light gray and pixel value corresponding to white raise successively;
1.5 curves that step 1.4 is obtained to corresponding hop count adopt matlab function plot to simulate corresponding curve map, as shown in Fig. 4 to 7, and obtain the midpoint tangent line of every section of curve, the tangent line joining of adjacent curve is the threshold value that these two sections of colors are divided, each original image can obtain one group of threshold value, and in group, threshold value is arranged by order from small to large;
1.6 by all surfaces original image through step 1.1 to 1.5 processing and obtain group in threshold value mean value, as shown in Figure 8, the threshold value mean value obtaining is [gray-scale value 120; Gray-scale value 225; Gray-scale value 240], the separation of the gray-scale value between black, Dark grey color is 120; The separation of the gray-scale value between Dark grey, light grey color is 225, and the separation of light grey and white gray-scale value is 240;
(2) material to be detected is passed through to adopt 6 high precision video cameras that can cover whole material surface to be detected, take the surperficial original image of 50 parts of materials to be detected, after image is processed, then the Standard Colors classification thresholds that adopts step (1) to obtain calculates the surface color feature of this material to be detected; The step of calculating the surface color feature of this material to be detected is:
2.1 adopt matlab function imread to read in matlab the surperficial original image of taking 50 parts of materials to be detected successively;
Then 2.2 adopt matlab function rgb2gray that the surperficial original image in each step 2.1 is transformed into gray-scale map by cromogram;
2.3 adopt matlab function sort to arrange by ascending order its pixel each gray-scale map obtaining in step 2.2, arrange pixel according to gray-scale value order from small to large; And continue to adopt matlab function plot to simulate the curve map of the ascending order pixel of each image according to collating sequence point;
In the group that the curve map of the 2.4 ascending order pixels that step step 2.3 is obtained obtains according to step 1.6, threshold value mean value is divided into the curve of corresponding hop count, obtain this color corresponding pixel and, each pictures can obtain pixel corresponding to one group of main color and;
2.5 by all surfaces original image through step 2.1 to 2.4 processing and obtain surface color respective pixel and the mean value of this material to be detected, pixel and mean value that the main color of this material is obtained are as shown in table 1.
(3) the surface color feature and the evaluation criterion that step (2) are obtained to this material to be detected contrast, and obtain the surface color quality grading of this material online automatic detection to be detected; What the surface color quality of this material online automatic detection to be detected was graded is: the surface color respective pixel of this material to be detected of first step 2.5 being tried to achieve and mean value obtain respectively the pixel rate of specific gravity of main color divided by total pixel of this detected materials; The pixel rate of specific gravity of main color is as shown in table 1, and then as shown in Table 1, known black and white impurity proportion is respectively 0.61% and 0.77%, all higher than 0.5%, therefore impurity does not meet quality standard, defective; Even if light grey proportion is 70.83%, in (1%, 1%) error range of 70%, but Dark grey proportion is 27.79%, not in (1%, 1%) error range of 30%, thus do not meet quality standard, defective.To sum up, this piece A material surface is off quality.
Table 1
Embodiment 2
As shown in Figure 9, this B material is five kinds of colors: black, Dark grey, grey, light gray and white, the evaluation criterion of this B material is: black, Dark grey, grey, light gray and white proportion are respectively: 6%, 24%, 6%, 61% and 3%, acceptable error is (0.5%, 0.5%), meet this standard qualified, otherwise defective.
This B material surface color characteristic online automatic detection method, its concrete steps are as follows:
(1) first according to real time environment and material category chosen material master sample, by adopting 6 high precision video cameras that can cover whole material standard sample surface to be detected, take the surperficial original image of some parts of material standard samples to be detected, after image is processed, obtain Standard Colors classification thresholds; The step of Standard Colors classification thresholds is:
1.1 adopt matlab function imread to read in matlab the surperficial original image of 50 parts of material standard samples to be detected having taken successively;
Then 1.2 adopt matlab function rgb2gray that the surperficial original image in each step 1.1 is transformed into gray-scale map by cromogram;
1.3 adopt matlab function sort to arrange by ascending order its pixel each gray-scale map obtaining in step 1.2, arrange pixel according to gray-scale value order from small to large; And continuing to adopt matlab function plot to simulate the curve map of the ascending order pixel of each image according to collating sequence point, this curve map is as shown in figure 10;
1.4 carry out segmentation according to the number of curve break in the curve map of the ascending order pixel obtaining in main color category and step 1.3 in master sample by the curve map of ascending order pixel, the hop count of segmentation is corresponding with the main color of material successively, this B material is five kinds of colors: black, Dark grey, grey, light gray and white, the curve of this Figure 10 be divided into the first paragraph corresponding with above-mentioned color, second segment, the 3rd section, the 4th section, the 5th section;
1.5 curves that step 1.4 is obtained to corresponding hop count adopt matlab function plot to simulate corresponding curve map, as shown in Figure 11 to 15, and obtain the midpoint tangent line of every section of curve, the tangent line joining of adjacent curve is the threshold value that these two sections of colors are divided, each original image can obtain one group of threshold value, and in group, threshold value is arranged by order from small to large;
1.6 by all surfaces original image through step 1.1 to 1.5 processing and obtain group in threshold value mean value, as shown in figure 16, the threshold value mean value obtaining is [gray-scale value 100; Gray-scale value 130; Gray-scale value 190; Gray-scale value 215], the separation of the gray-scale value between black, Dark grey color is 100; The separation of the gray-scale value between Dark grey, grey color is 130; The separation of the gray-scale value between grey, light grey color is 190, and the separation of light grey and white gray-scale value is 215;
(2) material to be detected is passed through to adopt 6 high precision video cameras that can cover whole material surface to be detected, take the surperficial original image of 50 parts of materials to be detected, after image is processed, then the Standard Colors classification thresholds that adopts step (1) to obtain calculates the surface color feature of this material to be detected; The step of calculating the surface color feature of this material to be detected is:
2.1 adopt matlab function imread to read in matlab the surperficial original image of taking some parts of materials to be detected successively;
Then 2.2 adopt matlab function rgb2gray that the surperficial original image in each step 2.1 is transformed into gray-scale map by cromogram;
2.3 adopt matlab function sort to arrange by ascending order its pixel each gray-scale map obtaining in step 2.2, arrange pixel according to gray-scale value order from small to large; And continue to adopt matlab function plot to simulate the curve map of the ascending order pixel of each image according to collating sequence point;
In the group that the curve map of the 2.4 ascending order pixels that step 2.3 is obtained obtains according to step 1.6, threshold value mean value is divided into the curve of corresponding hop count, obtain this color corresponding pixel and, each pictures can obtain pixel corresponding to one group of main color and;
2.5 by all surfaces original image through step 2.1 to 2.4 processing and obtain surface color respective pixel and the mean value of this material to be detected, surface color respective pixel and mean value are as shown in table 2.
(3) the surface color feature and the evaluation criterion that step (2) are obtained to this material to be detected contrast, and obtain the surface color quality grading of this material online automatic detection to be detected.
What the surface color quality of this material online automatic detection to be detected was graded is: the surface color respective pixel of this material to be detected of first step 2.5 being tried to achieve and mean value obtain respectively the pixel rate of specific gravity of main color divided by total pixel of this detected materials, and the pixel rate of specific gravity of main color is as shown in table 2; Then be respectively according to black, Dark grey, grey, light gray and white proportion: 6.15%, 23.97%, 5.56%, 61.42% and 2.90%, each color than weight average within the scope of acceptable error, therefore this piece B material surface is up-to-standard.
Table 2
Embodiment 3
This material surface color characteristic online automatic detection method, its concrete steps are as follows:
(1) first according to real time environment and material category chosen material master sample, by adopting 10 high precision video cameras that can cover whole material standard sample surface to be detected, take the surperficial original image of 100 parts of material standard samples to be detected, after image is processed, obtain Standard Colors classification thresholds; The step of Standard Colors classification thresholds is:
1.1 adopt matlab function imread to read in matlab the surperficial original image of 100 parts of material standard samples to be detected having taken successively;
Then 1.2 adopt matlab function rgb2gray that the surperficial original image in each step 1.1 is transformed into gray-scale map by cromogram;
1.3 adopt matlab function sort to arrange by ascending order its pixel each gray-scale map obtaining in step 1.2, arrange pixel according to gray-scale value order from small to large; And continue to adopt matlab function plot to simulate the curve map of the ascending order pixel of each image according to collating sequence point;
1.4 carry out segmentation according to the number of curve break in the curve map of the ascending order pixel obtaining in main color category and step 1.3 in master sample by the curve map of ascending order pixel, and the hop count of segmentation is corresponding with the main color of material successively;
1.5 curves that step 1.4 is obtained to corresponding hop count adopt matlab function plot to simulate corresponding curve map, and obtain the midpoint tangent line of every section of curve, the tangent line joining of adjacent curve is the threshold value that these two sections of colors are divided, each original image can obtain one group of threshold value, and in group, threshold value is arranged by order from small to large;
1.6 by all surfaces original image through step 1.1 to 1.5 processing and obtain group in threshold value mean value.
(2) material to be detected is passed through to adopt 10 high precision video cameras that can cover whole material surface to be detected, take the surperficial original image of 100 parts of materials to be detected, after image is processed, then the Standard Colors classification thresholds that adopts step (1) to obtain calculates the surface color feature of this material to be detected; The step of calculating the surface color feature of this material to be detected is:
2.1 adopt matlab function imread to read in matlab the surperficial original image of taking 100 parts of materials to be detected successively;
Then 2.2 adopt matlab function rgb2gray that the surperficial original image in each step 2.1 is transformed into gray-scale map by cromogram;
2.3 adopt matlab function sort to arrange by ascending order its pixel each gray-scale map obtaining in step 2.2, arrange pixel according to gray-scale value order from small to large; And continue to adopt matlab function plot to simulate the curve map of the ascending order pixel of each image according to collating sequence point;
In the group that the curve map of the 2.4 ascending order pixels that step 2.3 is obtained obtains according to step 1.6, threshold value mean value is divided into the curve of corresponding hop count, obtain this color corresponding pixel and, each pictures can obtain pixel corresponding to one group of main color and;
2.5 by all surfaces original image through step 2.1 to 2.4 processing and obtain surface color respective pixel and the mean value of this material to be detected.
(3) the surface color feature and the evaluation criterion that step (2) are obtained to this material to be detected contrast, and obtain the surface color quality grading of this material online automatic detection to be detected.What the surface color quality of this material online automatic detection to be detected was graded is: the surface color respective pixel of this material to be detected of first step 2.5 being tried to achieve and mean value obtain respectively the pixel rate of specific gravity of main color divided by total pixel of this detected materials; Then compare according to pixel rate of specific gravity and this evaluation of material standard to be detected, can obtain the affiliated grade of this kind of material surface color characteristic.
Claims (4)
1. a material surface color characteristic online automatic detection method, is characterized in that concrete steps are as follows:
(1) first according to real time environment and material category chosen material master sample, by adopting some the high precision video cameras that can cover whole material standard sample surface to be detected, take the surperficial original image of some parts of material standard samples to be detected, after image is processed, obtain Standard Colors classification thresholds;
(2) material to be detected is passed through to adopt some the high precision video cameras that can cover whole material surface to be detected, take the surperficial original image of some parts of materials to be detected, after image is processed, then the Standard Colors classification thresholds that adopts step (1) to obtain calculates the surface color feature of this material to be detected;
(3) the surface color feature and the evaluation criterion that step (2) are obtained to this material to be detected contrast, and obtain the surface color quality grading of this material online automatic detection to be detected.
2. material surface color characteristic online automatic detection method according to claim 1, is characterized in that: the step that obtains Standard Colors classification thresholds in described step (1) is:
1.1 adopt matlab function imread to read in matlab the surperficial original image of the some parts of material standard samples to be detected of having taken successively;
Then 1.2 adopt matlab function rgb2gray that the surperficial original image in each step 1.1 is transformed into gray-scale map by cromogram;
1.3 adopt matlab function sort to arrange by ascending order its pixel each gray-scale map obtaining in step 1.2, arrange pixel according to gray-scale value order from small to large; And continue to adopt matlab function plot to simulate the curve map of the ascending order pixel of each image according to collating sequence point;
1.4 carry out segmentation according to the number of curve break in the curve map of the ascending order pixel obtaining in main color category and step 1.3 in master sample by the curve map of ascending order pixel, and the hop count of segmentation is corresponding with the main color of material successively;
1.5 curves that step 1.4 is obtained to corresponding hop count adopt matlab function plot to simulate corresponding curve map, and obtain the midpoint tangent line of every section of curve, the tangent line joining of adjacent curve is the threshold value that these two sections of colors are divided, each original image can obtain one group of threshold value, and in group, threshold value is arranged by order from small to large;
1.6 by all surfaces original image through step 1.1 to 1.5 processing and obtain group in threshold value mean value.
3. arbitrary material surface color characteristic online automatic detection method according to claim 2, is characterized in that: the step that described step (2) is calculated the surface color feature of this material to be detected is:
2.1 adopt matlab function imread to read in matlab the surperficial original image of taking some parts of materials to be detected successively;
Then 2.2 adopt matlab function rgb2gray that the surperficial original image in each step 2.1 is transformed into gray-scale map by cromogram;
2.3 adopt matlab function sort to arrange by ascending order its pixel each gray-scale map obtaining in step 2.2, arrange pixel according to gray-scale value order from small to large; And continue to adopt matlab function plot to simulate the curve map of the ascending order pixel of each image according to collating sequence point;
In the group that the curve map of the 2.4 ascending order pixels that step 2.3 is obtained obtains according to step 1.6, threshold value mean value is divided into the curve of corresponding hop count, obtain this color corresponding pixel and, each pictures can obtain pixel corresponding to one group of main color and;
2.5 by all surfaces original image through step 2.1 to 2.4 processing and obtain surface color respective pixel and the mean value of this material to be detected.
4. arbitrary material surface color characteristic online automatic detection method according to claim 3, is characterized in that: the surface color quality grading of this material online automatic detection to be detected of described step (3) be: the surface color respective pixel of this material to be detected of first step 2.5 being tried to achieve and mean value obtain respectively the pixel rate of specific gravity of main color divided by total pixel of this detected materials; Then compare according to pixel rate of specific gravity and this evaluation of material standard to be detected, can obtain the affiliated grade of this kind of material surface color characteristic.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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