CN106157271A - A kind of method that carbon-bearing particulate matter nanostructured based on image processing techniques is analyzed - Google Patents
A kind of method that carbon-bearing particulate matter nanostructured based on image processing techniques is analyzed Download PDFInfo
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- 239000013618 particulate matter Substances 0.000 title claims abstract description 78
- 238000000034 method Methods 0.000 title claims abstract description 64
- 238000012545 processing Methods 0.000 title claims abstract description 29
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical group [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 27
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 27
- 239000011248 coating agent Substances 0.000 claims abstract description 17
- 238000000576 coating method Methods 0.000 claims abstract description 17
- 239000002086 nanomaterial Substances 0.000 claims abstract description 10
- 230000000694 effects Effects 0.000 claims abstract description 7
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 16
- 238000010606 normalization Methods 0.000 claims description 11
- 238000006243 chemical reaction Methods 0.000 claims description 10
- 229960000935 dehydrated alcohol Drugs 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 6
- 230000002708 enhancing effect Effects 0.000 claims description 6
- 230000000877 morphologic effect Effects 0.000 claims description 6
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims description 4
- 229910052802 copper Inorganic materials 0.000 claims description 4
- 239000010949 copper Substances 0.000 claims description 4
- 229960004756 ethanol Drugs 0.000 claims description 4
- 230000010355 oscillation Effects 0.000 claims description 4
- 239000000443 aerosol Substances 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000012217 deletion Methods 0.000 claims description 3
- 230000037430 deletion Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 claims description 3
- 230000010339 dilation Effects 0.000 claims description 3
- 230000003628 erosive effect Effects 0.000 claims description 3
- XNKARWLGLZGMGX-UHFFFAOYSA-N ethyl 4-(4-chloro-2-methylphenoxy)butanoate Chemical group CCOC(=O)CCCOC1=CC=C(Cl)C=C1C XNKARWLGLZGMGX-UHFFFAOYSA-N 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- 239000003245 coal Substances 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000004445 quantitative analysis Methods 0.000 abstract description 2
- 241000209140 Triticum Species 0.000 abstract 1
- 235000021307 Triticum Nutrition 0.000 abstract 1
- 239000003575 carbonaceous material Substances 0.000 abstract 1
- 238000011156 evaluation Methods 0.000 abstract 1
- 239000006229 carbon black Substances 0.000 description 10
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 9
- 238000009826 distribution Methods 0.000 description 2
- 238000002173 high-resolution transmission electron microscopy Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
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- 238000012986 modification Methods 0.000 description 2
- 230000003647 oxidation Effects 0.000 description 2
- 238000007254 oxidation reaction Methods 0.000 description 2
- 239000008188 pellet Substances 0.000 description 2
- 239000002912 waste gas Substances 0.000 description 2
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- 239000011521 glass Substances 0.000 description 1
- 239000008187 granular material Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
- G06T2207/20032—Median filtering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20036—Morphological image processing
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Abstract
The invention discloses a kind of method carbon-bearing particulate matter nanostructured being analyzed based on image processing techniques.Particulate matter can be engine emission particulate matter, burning coal generates particulate matter, wheat stalk burning generates the various carbonaceous materials such as particulate matter, this method is to the length of each crystallite of carbon-coating in particulate matter and curvature, and the spacing distance of adjacent two crystallites carries out quantitative analysis.This method comprises the following steps: particulate matter microscopic appearance Image Acquisition；Image is carried out digital processing；Calculate the nano structure parameter of particulate matter.The present invention is on the basis of carrying out digital processing to particulate matter microscopic appearance image, automatically remove crystallite cross point further, and crystallite is carried out automatic linear search matching, computational efficiency and the accuracy in computation of particulate matter nano structure parameter can be effectively improved, thus provide a kind of evaluation methodology that atmospheric pollution particulate matter is controlled technology application effect.
Description
Technical field
The present invention relates to a kind of method that carbon-bearing particulate matter nanostructured is analyzed, belong to technical field of image processing.
Background technology
Haze has become as a kind of diastrous weather of big and medium-sized cities, is included the extensive concern of China by countries in the world, and wherein pellet is the main component of haze.Mainly there is automobile particularly emission of diesel engine particulate matter in the source of these particulate matters, burns waste gas produced by coal, commercial production discharge waste gas etc., and most of particulate matters are all the carbon-bearing particulate matters with carbon as core.At present to the generation of particulate matter and eliminate mechanism particularly particulate matter structure and physicochemical property and generation thereof, development law has become as the focus of attention of field of Environment Protection.
Numerous studies both domestic and external find, the nanostructured of particulate matter has with its oxidation characteristic and directly contacts.The particulate matter with undefined structure is easier to oxidized than the particulate matter with lattice structure, for carbon-bearing particulate matter, shorter crystallite length, bigger crystallite curvature and interlamellar spacing represent that in particulate matter, undefined structure becomes apparent from, it is easier to be eliminated, have lower oxidation activation energy.Therefore, use rational method that the nanostructured of particulate matter carries out quantitative analysis, understand the generation of particulate matter and eliminate mechanism, it will help the control of particulate matter.Current existing method lacks the automatic elimination algorithm to crystallite cross point, and in crystallite is to distance computation, lacks automatic Matching, reduce efficiency and the accuracy of calculating.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, a kind of carbon-bearing particulate matter nanostructured based on image processing techniques is provided to analyze method, it has realization and automatically eliminates crystallite cross point and to the crystallite feature to Auto-matching, it is possible to be effectively improved computational efficiency and the accuracy in computation of particulate matter nano structure parameter
Invention provides a kind of method that carbon-bearing particulate matter nanostructured based on image processing techniques is analyzed, including: the step of the nano structure parameter calculating of the step of the particulate matter microscopic appearance Image Acquisition carried out successively, the step that the image obtained is carried out Digital Image Processing and particulate matter；
The step of described particulate matter microscopic appearance Image Acquisition is:
A1, grain of being peeked by particulate matter are placed in dehydrated alcohol, vibrate certain time, make that particulate matter is fully dispersed in dehydrated alcohol to be opened in supersonic oscillations machine；Take a mixed liquor and drop in high-resolution field transmission Electronic Speculum copper mesh ultrathin carbon films, wait that ethanol freely volatilizees；
A2, the use high-resolution field transmission Electronic Speculum aerosol sample to preparing carry out microscopic appearance observation, obtain particulate matter feature image；
A3, acquired particulate matter microscopic appearance image is carried out manual segmentation, obtain region interested, and the image after segmentation is preserved.
Preferably, the step that the described image to obtaining carries out Digital Image Processing is:
B1, the pending image acquired in step one is normalized；
B2, image is filtered；
B3, image is carried out equalization enhancing；
B4, image is carried out binary conversion treatment；
B5, image is carried out morphology open and close operator；
B6, image is refined.
Preferably, described step B1, the pending image acquired in step one is normalized for: set the maximum gradation value of all pixels of pending image as Gmax, minimum gradation value is Gmin, the gray value of pixel P is GP, NPIt is the gray value of pixel P after normalization, normalization formula such as formula (1):
Preferably, described step B2, be filtered comprising the steps: to image
C1, take window size be n × n, n be odd number.Being moved on image by window, the pixel P making window center pending with image overlaps；
C2, write down the gray value of each pixel in window, arrange according to order from big to small: N1, N2..., Nn × n；
C3, the gray value N in calculating centre position((n × n)+1)/2, and this value is assigned to pixel P；
All pixels in C4, traversing graph picture.
Preferably, described step B3, image carrying out equalization enhancing, use histogram equalization to strengthen, if the gray level of image after step B2 processes is L, in image, the sum of pixel is n, and i-stage gray scale is ri, niIt is that in image, gray level is riNumber of pixels, comprise the steps:
D1, the normalization histogram of calculating image, such as formula (2):
D2, set the gray level that r is to normalize to the image in interval [01], definition change f so that interval [0,1] any r in, transformed f (r) has a s the most corresponding, it is clear that s is the gray level that transformed f (r) exports image afterwards.F (r) meets following condition in interval 0≤r≤1:
(1) f (r) monotonic increase；
(2)0≤f(r)≤1；
(3) s, r have relation one to one；
D3, by conversion f by gray level r of input pictureiIt is mapped to gray level s of output imagei, such as formula (3):
Preferably, described step B4, process that image carries out binary conversion treatment are: set after step B3 strengthens the grey level range of image as (rmin, rmax), T is rminAnd rmaxA middle number, then binary map gPSuch as formula (4):
Wherein: " 1 " represents object (object, target), uses white；" 0 " represents background, uses black.
In formula (4), threshold value T uses iteration method to determine, comprises the steps:
E1, minimum and maximum gray level r obtained in imageminAnd rmax, and Initial Hurdle is set, such as formula (5):
E2, according to threshold value TkImage is divided into target and background two parts by (k=0,1,2...), obtains two-part average gray value, such as formula (6) and formula (7):
Wherein, S gray level is riThe sum of pixel.
E3, obtain new threshold value, such as formula (8):
If E4 is Tk+1=Tk, then threshold value T=T is madek, otherwise make k=k+1, turn to step E2.
Preferably, step B5, process that image carries out morphology open and close operator are: set input picture as Iinput, structural element is SE, image carries out opening operation and closed operation processes, such as formula (9) and (10):
SE is the square of m × m size,For morphological erosion,For morphological dilations.Can eliminate scatterplot by opening operation, cut off elongated overlap joint and play the effect of separation, closed operation can overlap short interruption and play the effect of connection.
Preferably, image is refined by step B6, uses PSCP thinning algorithm, and its eight connectivity schematic diagram is as follows:
| P1 | P2 | P3 |
| P8 | P | P4 |
| P7 | P6 | P5 |
The steps include:
F1, for all of crystallite pixel P, if meeting following condition, then judge that it is can to delete a little:
(1) crystallite pixel number 2≤E (P)≤6 in its 8 connected domain；
(2) 8 connected domains of P comprise and only comprise one 4 connection crystallite pixel；
F2, traveling through all of deletion a little, if meeting one of following condition, then retaining；Otherwise delete:
(1)P2, P6For crystallite pixel, P4For deleting a little；
(2)P4, P8For crystallite pixel, P6For deleting a little；
(3)P4, P5, P6For deleting a little.
Preferably, the step that the nano structure parameter of described particulate matter calculates is:
G1, particulate matter carbon-coating crystallite go to cross point；
G2, particulate matter carbon-coating crystallite linear search matching, calculate the spacing distance of adjacent two crystallites；
G3, definition according to relevant feature parameters, calculate crystallite length, crystallite curvature and interlamellar spacing；
Described step G1, particulate matter carbon-coating crystallite go the step in cross point to be:
After H1, image binaryzation, the pixel value of crystallite pixel is 1 (white), and the pixel value of background is 0 (black)；
The number of white point in the eight connected region of H2, search pixel point P, is set to n:
(1) if n >=3, then P is cross point, and albomaculatus pixel value in P and eight connected region thereof is set to 0；
(2) if n ＜ 3, then P is not cross point；
All crystallite pixels in H3, traversing graph；
Described step G2, the step of particulate matter carbon-coating crystallite linear search matching be:
I1, set P0For the starting point of current fitting a straight line section, PcFor current search point, P time initialc=P0；
I2, search PcEight connected region in whether there is white point；Without abutment points, then PcIt is set to terminal, P0For starting point, obtaining straight line section with least square fitting Line Algorithm, search terminates；
If I3 is PcEight connected region in there is white point, according to set rule obtain next trace point Pn, it is judged that PnWhether in the range of current straightway extended line (being judged by straight slope).If, P is setc=Pn, by PnPosition join in an array, continue search for subsequent point.If PnNot on current straightway extended line, then with PcFor terminal, P0For starting point, read various point locations, again matching straight line section in some chained list, terminate currently to follow the tracks of straightway.
Preferably, the least square line fitting algorithm that described step I2 uses, comprise the steps:
J1, the rectilinear point coordinate obtained with searching algorithm are (xi, yi) (i=1,2 ..., m), m is the number of rectilinear point, if the equation of least square fitting straight line is y=Ax+B；
J2, point (xi, yi) to the quadratic sum of vertical dimension of straight line be formula (11):
J3, because E (A, B) take minima, its local derviation is zero, such as formula (12):
J4, formula (12) uses summation distributive law obtain formula (13):
Wherein
J5, above-mentioned equation group is solved, obtains formula (14):
The invention has the beneficial effects as follows: granule nano structure parameter can be calculated quickly and accurately.Automatically cut off the carbon-coating crystallite intersected, carbon-coating crystallite is carried out linear search matching, it is achieved the crystallite automatic calculating to spacing.
Below in conjunction with drawings and Examples, the present invention is described in further detail；But the method for a kind of based on image processing techniques the carbon-bearing particulate matter nanostructured analysis of the present invention is not limited to embodiment.
Accompanying drawing explanation
Fig. 1 is the HR-TEM original image of white carbon black；
Fig. 2 is pending image after segmentation；
Fig. 3 is image after normalization；
Fig. 4 is image after binaryzation；
Fig. 5 is image behind cross point；
Fig. 6 (a) white carbon black crystallite length scattergram；
Fig. 6 (b) white carbon black crystallite curvature distribution figure；
Fig. 6 (c) white carbon black crystallite is to spacing scattergram.
Detailed description of the invention
Embodiment 1
See shown in Fig. 1 to Fig. 6, the method that a kind of based on image processing techniques the carbon-bearing particulate matter nanostructured of the present invention is analyzed, including: the step of the nano structure parameter calculating of the step of the particulate matter microscopic appearance Image Acquisition carried out successively, the step that the image obtained is carried out Digital Image Processing and particulate matter；
The step of described particulate matter microscopic appearance Image Acquisition is:
A1, grain of being peeked by particulate matter are placed in dehydrated alcohol, vibrate certain time, make that particulate matter is fully dispersed in dehydrated alcohol to be opened in supersonic oscillations machine；Take a mixed liquor and drop in high-resolution field transmission Electronic Speculum copper mesh ultrathin carbon films, wait that ethanol freely volatilizees；
A2, the use high-resolution field transmission Electronic Speculum aerosol sample to preparing carry out microscopic appearance observation, obtain particulate matter feature image；
A3, acquired particulate matter microscopic appearance image is carried out manual segmentation, obtain region interested, and the image after segmentation is preserved.
Further, the step that the described image to obtaining carries out Digital Image Processing is:
B1, the pending image acquired in step one is normalized；
B2, image is filtered；
B3, image is carried out equalization enhancing；
B4, image is carried out binary conversion treatment；
B5, image is carried out morphology open and close operator；
B6, image is refined.
Further, described step B1, the pending image acquired in step one is normalized for: set the maximum gradation value of all pixels of pending image as Gmax, minimum gradation value is Gmin, the gray value of pixel P is GP, NPIt is the gray value of pixel P after normalization, normalization formula such as formula (1):
Further, described step B2, be filtered comprising the steps: to image
C1, take window size be n × n, n be odd number.Being moved on image by window, the pixel P making window center pending with image overlaps；
C2, write down the gray value of each pixel in window, arrange according to order from big to small: N1, N2..., Nn × n；
C3, the gray value N in calculating centre position((n × n)+1)/2, and this value is assigned to pixel P；
All pixels in C4, traversing graph picture.
Further, described step B3, image carrying out equalization enhancing, use histogram equalization to strengthen, if the gray level of image after step B2 processes is L, in image, the sum of pixel is n, and i-stage gray scale is ri, niIt is that in image, gray level is riNumber of pixels, comprise the steps:
D1, the normalization histogram of calculating image, such as formula (2):
D2, set the gray level that r is to normalize to the image in interval [01], definition change f so that interval [0,1] any r in, transformed f (r) has a s the most corresponding, it is clear that s is the gray level that transformed f (r) exports image afterwards.F (r) meets following condition in interval 0≤r≤1:
(1) f (r) monotonic increase；
(2)0≤f(r)≤1；
(3) s, r have relation one to one；
D3, by conversion f by gray level r of input pictureiIt is mapped to gray level s of output imagei, such as formula (3):
Further, described step B4, process that image carries out binary conversion treatment are: set after step B3 strengthens the grey level range of image as (rmin, rmax), T is rminAnd rmaxA middle number, then binary map gPSuch as formula (4):
Wherein: " 1 " represents object (object, target), uses white；" 0 " represents background, uses black.
In formula (4), threshold value T uses iteration method to determine, comprises the steps:
E1, minimum and maximum gray level r obtained in imageminAnd rmax, and Initial Hurdle is set, such as formula (5):
E2, according to threshold value TkImage is divided into target and background two parts by (k=0,1,2...), obtains two-part average gray value, such as formula (6) and formula (7):
Wherein, S gray level is riThe sum of pixel.
E3, obtain new threshold value, such as formula (8):
If E4 is Tk+1=Tk, then threshold value T=T is madek, otherwise make k=k+1, turn to step E2.
Further, step B5, process that image carries out morphology open and close operator are: set input picture as Iinput, structural element is SE, image carries out opening operation and closed operation processes, such as formula (9) and (10):
SE is the square of m × m size,For morphological erosion,For morphological dilations.Can eliminate scatterplot by opening operation, cut off elongated overlap joint and play the effect of separation, closed operation can overlap short interruption and play the effect of connection.
Further, image is refined by step B6, uses PSCP thinning algorithm, and its eight connectivity schematic diagram is as follows:
| P1 | P2 | P3 |
| P8 | P | P4 |
| P7 | P6 | P5 |
The steps include:
F1, for all of crystallite pixel P, if meeting following condition, then judge that it is can to delete a little:
(1) crystallite pixel number 2≤E (P)≤6 in its 8 connected domain；
(2) 8 connected domains of P comprise and only comprise one 4 connection crystallite pixel；
F2, traveling through all of deletion a little, if meeting one of following condition, then retaining；Otherwise delete:
(1)P2, P6For crystallite pixel, P4For deleting a little；
(2)P4, P8For crystallite pixel, P6For deleting a little；
(3)P4, P5, P6For deleting a little.
Further, the step that the nano structure parameter of described particulate matter calculates is:
G1, particulate matter carbon-coating crystallite go to cross point；
G2, particulate matter carbon-coating crystallite linear search matching, calculate the spacing distance of adjacent two crystallites；
G3, definition according to relevant feature parameters, calculate crystallite length, crystallite curvature and interlamellar spacing；
Described step G1, particulate matter carbon-coating crystallite go the step in cross point to be:
After H1, image binaryzation, the pixel value of crystallite pixel is 1 (white), and the pixel value of background is 0 (black)；
The number of white point in the eight connected region of H2, search pixel point P, is set to n:
(1) if n >=3, then P is cross point, and albomaculatus pixel value in P and eight connected region thereof is set to 0；
(2) if n ＜ 3, then P is not cross point；
All crystallite pixels in H3, traversing graph；
Described step G2, the step of particulate matter carbon-coating crystallite linear search matching be:
I1, set P0For the starting point of current fitting a straight line section, PcFor current search point, P time initialc=P0；
I2, search PcEight connected region in whether there is white point；Without abutment points, then PcIt is set to terminal, P0For starting point, obtaining straight line section with least square fitting Line Algorithm, search terminates；
If I3 is PcEight connected region in there is white point, according to set rule obtain next trace point Pn, it is judged that PnWhether in the range of current straightway extended line (being judged by straight slope).If, P is setc=Pn, by PnPosition join in an array, continue search for subsequent point.If PnNot on current straightway extended line, then with PcFor terminal, P0For starting point, read various point locations, again matching straight line section in some chained list, terminate currently to follow the tracks of straightway.
Further, the least square line fitting algorithm that described step I2 uses, comprise the steps:
J1, the rectilinear point coordinate obtained with searching algorithm are (xi, yi) (i=1,2 ..., m), m is the number of rectilinear point, if the equation of least square fitting straight line is y=Ax+B；
J2, point (xi, yi) to the quadratic sum of vertical dimension of straight line be formula (11):
J3, because E (A, B) take minima, its local derviation is zero, such as formula (12):
J4, formula (12) uses summation distributive law obtain formula (13):
Wherein
J5, above-mentioned equation group is solved, obtains formula (14):
Embodiment 2
The present invention provides a kind of carbon-bearing particulate matter nanostructured based on image processing techniques to analyze method, comprises the steps:
K1, white carbon black is taken 5-10 grain it is positioned in the dehydrated alcohol of 5ml, use supersonic oscillations 30 minutes, make that carbon black pellet is fully dispersed in dehydrated alcohol to be opened.Take a mixed liquor with glass dropper to drop to, on TEM ultra-thin copper mesh carbon film, treat that ethanol volatilizees completely；
K2, utilize HR-TEM that white carbon black microscopic appearance is observed, obtain white carbon black microscopic appearance picture, as shown in Figure 1.The part selecting microscopic feature obvious in picture carries out cutting, it is thus achieved that size is the pending image of 468 × 488 pixels, as shown in Figure 2；
K3, being normalized Fig. 2, normalization result is as shown in Figure 3；
K4, image filtering
Window size takes 3 × 3, then according to pixels all in image are filtered processing by setting procedure；
K5, according to formula (2) and (3), filtered image is carried out histogram equalization enhancement process；
K6, enhanced image carry out binary conversion treatment, and as shown in Figure 4, the pixel value of crystallite pixel is 1, and the pixel value of background pixel point is 0；
K7, the morphology open and close operator of image, structural element SE takes the square of 3 × 3 sizes；
K8, to morphology open and close operator process after image carry out micronization processes；
K9, carbon-coating crystallite communicating position is turned off process, remove cross point, as shown in Figure 5；
K10, then carbon-coating crystallite is carried out linear search matching；
K11, white carbon black nanostructured characterisitic parameter calculate
Through calculating, the nanostructured characterisitic parameter of white carbon black is crystallite average length 0.6278nm, crystallite average curvature 1.0401, and crystallite is 0.397 to average headway.Fig. 6 (a) is crystallite length scattergram, and Fig. 6 (b) is crystallite curvature distribution figure, and Fig. 6 (c) is that crystallite is to spacing scattergram.
Above-described embodiment is only used for further illustrating the method that a kind of based on image processing techniques the carbon-bearing particulate matter nanostructured of the present invention is analyzed; but the invention is not limited in embodiment; every any simple modification, equivalent variations and modification made above example according to the technical spirit of the present invention, each falls within the protection domain of technical solution of the present invention.
Claims (10)
1. the method that a carbon-bearing particulate matter nanostructured based on image processing techniques is analyzed, it is characterized in that, including: the step of the nano structure parameter calculating of the step of the particulate matter microscopic appearance Image Acquisition carried out successively, the step that the image obtained is carried out Digital Image Processing and particulate matter；
The step of described particulate matter microscopic appearance Image Acquisition is:
A1, grain of being peeked by particulate matter are placed in dehydrated alcohol, vibrate certain time, make that particulate matter is fully dispersed in dehydrated alcohol to be opened in supersonic oscillations machine；Take a mixed liquor and drop in high-resolution field transmission Electronic Speculum copper mesh ultrathin carbon films, wait that ethanol freely volatilizees；
A2, the use high-resolution field transmission Electronic Speculum aerosol sample to preparing carry out microscopic appearance observation, obtain particulate matter feature image；
A3, acquired particulate matter microscopic appearance image is carried out manual segmentation, obtain region interested, and the image after segmentation is preserved.
The method that a kind of carbon-bearing particulate matter nanostructured based on image processing techniques the most according to claim 1 is analyzed, it is characterised in that: the step that the described image to obtaining carries out Digital Image Processing is:
B1, the pending image acquired in step one is normalized；
B2, image is filtered；
B3, image is carried out equalization enhancing；
B4, image is carried out binary conversion treatment；
B5, image is carried out morphology open and close operator；
B6, image is refined.
The method that a kind of carbon-bearing particulate matter nanostructured based on image processing techniques the most according to claim 2 is analyzed, it is characterised in that: described step B1, the pending image acquired in step one is normalized for: set the maximum gradation value of all pixels of pending image as Gmax, minimum gradation value is Gmin, the gray value of pixel P is GP, NPIt is the gray value of pixel P after normalization, normalization formula such as formula (1):
。
The method that a kind of carbon-bearing particulate matter nanostructured based on image processing techniques the most according to claim 2 is analyzed, it is characterised in that: described step B2, it is filtered comprising the steps: to image
C1, take window size be n × n, n be odd number.Being moved on image by window, the pixel P making window center pending with image overlaps；
C2, write down the gray value of each pixel in window, arrange according to order from big to small: N1, N2..., Nn × n；
C3, the gray value N in calculating centre position((n × n)+1)/2, and this value is assigned to pixel P；
All pixels in C4, traversing graph picture.
The method that a kind of carbon-bearing particulate matter nanostructured based on image processing techniques the most according to claim 2 is analyzed, it is characterized in that: described step B3, image is carried out equalization enhancing, employing histogram equalization strengthens, if the gray level of the image after the process of step B2 is L, in image, the sum of pixel is n, and i-stage gray scale is ri, niIt is that in image, gray level is riNumber of pixels, comprise the steps:
D1, the normalization histogram of calculating image, such as formula (2):
D2, set the gray level that r is to normalize to the image in interval [01], definition change f so that interval [0,1] any r in, transformed f (r) has a s the most corresponding, it is clear that s is the gray level that transformed f (r) exports image afterwards.F (r) meets following condition in interval 0≤r≤1:
(1) f (r) monotonic increase；
(2)0≤f(r)≤1；
(3) s, r have relation one to one；
D3, by conversion f by gray level r of input pictureiIt is mapped to gray level s of output imagei, such as formula (3):
The method that a kind of carbon-bearing particulate matter nanostructured based on image processing techniques the most according to claim 2 is analyzed, it is characterised in that: described step B4, the process that image carries out binary conversion treatment be: sets after step B3 strengthens the grey level range of image as (rmin, rmax), T is rminAnd rmaxA middle number, then binary map gPSuch as formula (4):
Wherein: " 1 " represents object (object, target), uses white；" 0 " represents background, uses black.
In formula (4), threshold value T uses iteration method to determine, comprises the steps:
E1, minimum and maximum gray level r obtained in imageminAnd rmax, and Initial Hurdle is set, such as formula (5):
E2, according to threshold value TkImage is divided into target and background two parts by (k=0,1,2...), obtains two-part average gray value, such as formula (6) and formula (7):
Wherein, S gray level is riThe sum of pixel.
E3, obtain new threshold value, such as formula (8):
If E4 is Tk+1=Tk, then threshold value T=T is madek, otherwise make k=k+1, turn to step E2.
The method that a kind of carbon-bearing particulate matter nanostructured based on image processing techniques the most according to claim 2 is analyzed, it is characterised in that: step B5, the process that image carries out morphology open and close operator be: sets input picture as Iinput, structural element is SE, image carries out opening operation and closed operation processes, such as formula (9) and (10):
SE is the square of m × m size,For morphological erosion,For morphological dilations.Can eliminate scatterplot by opening operation, cut off elongated overlap joint and play the effect of separation, closed operation can overlap short interruption and play the effect of connection.
The method that a kind of carbon-bearing particulate matter nanostructured based on image processing techniques the most according to claim 2 is analyzed, it is characterised in that: image is refined by step B6, uses PSCP thinning algorithm, and its eight connectivity schematic diagram is as follows:
The steps include:
F1, for all of crystallite pixel P, if meeting following condition, then judge that it is can to delete a little:
(1) crystallite pixel number 2≤E (P)≤6 in its 8 connected domain；
(2) 8 connected domains of P comprise and only comprise one 4 connection crystallite pixel；
F2, traveling through all of deletion a little, if meeting one of following condition, then retaining；Otherwise delete:
(1)P2, P6For crystallite pixel, P4For deleting a little；
(2)P4, P8For crystallite pixel, P6For deleting a little；
(3)P4, P5, P6For deleting a little.
The method that a kind of carbon-bearing particulate matter nanostructured based on image processing techniques the most according to claim 1 is analyzed, it is characterised in that: the step that the nano structure parameter of described particulate matter calculates is:
G1, particulate matter carbon-coating crystallite go to cross point；
G2, particulate matter carbon-coating crystallite linear search matching, calculate the spacing distance of adjacent two crystallites；
G3, definition according to relevant feature parameters, calculate crystallite length, crystallite curvature and interlamellar spacing；
Described step G1, particulate matter carbon-coating crystallite go the step in cross point to be:
After H1, image binaryzation, the pixel value of crystallite pixel is 1 (white), and the pixel value of background is 0 (black)；
The number of white point in the eight connected region of H2, search pixel point P, is set to n:
(1) if n >=3, then P is cross point, and albomaculatus pixel value in P and eight connected region thereof is set to 0；
(2) if n ＜ 3, then P is not cross point；
All crystallite pixels in H3, traversing graph；
Described step G2, the step of particulate matter carbon-coating crystallite linear search matching be:
I1, set P0For the starting point of current fitting a straight line section, PcFor current search point, P time initialc=P0；
I2, search PcEight connected region in whether there is white point；Without abutment points, then PcIt is set to terminal, P0For starting point, obtaining straight line section with least square fitting Line Algorithm, search terminates；
If I3 is PcEight connected region in there is white point, according to set rule obtain next trace point Pn, it is judged that PnWhether in the range of current straightway extended line (being judged by straight slope).If, P is setc=Pn, by PnPosition join in an array, continue search for subsequent point.If PnNot on current straightway extended line, then with PcFor terminal, P0For starting point, read various point locations, again matching straight line section in some chained list, terminate currently to follow the tracks of straightway.
The method that a kind of carbon-bearing particulate matter nanostructured based on image processing techniques the most according to claim 10 is analyzed, it is characterised in that: the least square line fitting algorithm that described step I2 uses, comprise the steps:
J1, the rectilinear point coordinate obtained with searching algorithm are (xi, yi) (i=1,2 ..., m), m is the number of rectilinear point, if the equation of least square fitting straight line is y=Ax+B；
J2, point (xi, yi) to the quadratic sum of vertical dimension of straight line be formula (11):
J3, because E (A, B) take minima, its local derviation is zero, such as formula (12):
J4, formula (12) uses summation distributive law obtain formula (13):
Wherein
J5, above-mentioned equation group is solved, obtains formula (14):
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