CN104198325A - Method for measuring ratio of cut stem to cut tobacco based on computer vision - Google Patents

Method for measuring ratio of cut stem to cut tobacco based on computer vision Download PDF

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CN104198325A
CN104198325A CN201410448180.XA CN201410448180A CN104198325A CN 104198325 A CN104198325 A CN 104198325A CN 201410448180 A CN201410448180 A CN 201410448180A CN 104198325 A CN104198325 A CN 104198325A
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stem
pipe tobacco
image
measured
value
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CN104198325B (en
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董浩
刘锋
王澍
周明珠
张龙
刘勇
周德成
李晓辉
荆熠
王锦平
邢军
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Hefei Institutes of Physical Science of CAS
National Tobacco Quality Supervision and Inspection Center
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Hefei Institutes of Physical Science of CAS
National Tobacco Quality Supervision and Inspection Center
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Abstract

The invention discloses a method for measuring a ratio of cut stem to cut tobacco based on computer vision. The method comprises the following steps: A, acquiring each cut stem image by an image acquisition system; B, preprocessing the multiple acquired cut stem images; C, respectively acquiring image features of the cut stem and calculating the feature data quantity of the cut stem; D, establishing a feature database; E, acquiring images of components of to-be-measured cut stem; F, preprocessing the images of the components of the to-be-measured cut stem; G, calculating feature data quantity of the to-be-measured cut tobacco in the images of the components of the to-be-measured cut stem, performing relevancy calculation, and analyzing and identifying the components of the cut stem according to a relevancy calculation result; H, sorting the cut stem out from the to-be-measured cut tobacco by a sorting system; and I, respectively weighing the cut stem sorted out and residual components and calculating the ratio of the cut stem components to the to-be-measured cut tobacco. According to the method disclosed by the invention, the cut stem components in the cut tobacco can be rapidly, accurately and automatically measured, the measurement efficiency and accuracy are improved, and the labor intensity of operators is reduced.

Description

Stem ratio measuring method in pipe tobacco based on computer vision
Technical field
The present invention relates to a kind of stem ratio measuring method in pipe tobacco, relate in particular to a kind of stem ratio measuring method in pipe tobacco based on computer vision.
Background technology
Cigarette composition design is basis and the core of cigarette enterprise product design, and in cigarette, accurately mix pairing cigarette physical index, flue gas characteristic and the aesthetic quality of the component such as cut tobacco, expansive cut tobacco, stem, reconstituted tobacco exist impact in various degree.Therefore, determine rapidly and accurately the ratios of component in pipe tobacco such as cut tobacco in cigarette, expansive cut tobacco, stem, reconstituted tobacco, to examination formula Design target accuracy, stablize pipe tobacco hybrid technique quality and homogeneity production is significant.
Because the feature of detected object is complicated and relate to correlation technique bottleneck, thus the mensuration of pipe tobacco constituent still to rely on hand-sorting and people be interpretation.At present, normally used stem proportion measurement method is that the stem in cigarette is separated by water with other components, and dry rear weighing, calculates the ratio of stem.Existing detection method operation steps is complicated, detection efficiency is low, along with the increase of workload will produce larger error, be not suitable for a large amount of detections, measure efficiency and precision and be difficult to adapt to the requirement of modern detection demand and high-quality production of cigarettes, and also have larger error between different personnel's testing result.
Due to the difference of job operation and raw material self character, between the different component of pipe tobacco, there is the difference of texture, color, form, edge-smoothing degree, the computer vision means that exist for of these differences are identified each component provides characteristic parameter.Compare other components, the texture of stem and edge feature are very obvious, can distinguish by computer vision technique and other components.
Summary of the invention
The object of this invention is to provide a kind of stem ratio measuring method in pipe tobacco based on computer vision, can to the image of one-component stem, carry out acquisition process by computing machine, obtain the characteristic amount of stem and set up property data base, by the stem in property data base analysis identification polycomponent pipe tobacco, finally realize quick, accurate, the automatic assay of stem component in pipe tobacco, improve determination efficiency and accuracy, reduce intensity of workers.
The present invention adopts following technical proposals:
A stem ratio measuring method in pipe tobacco based on computer vision, comprises the following steps:
A: by many smooth non-overlapping the putting of stem, then utilize image capturing system to gather respectively each stem image;
B: utilize image processing and analyzing system to carry out pre-service to multiple stem images that obtain, remove every interference and noise in stem image;
C: utilize image processing and analyzing system to obtain respectively the characteristics of image of stem in multiple stem images, then according to the characteristic amount of the box counting algorithm stem of stem;
D: utilize image processing and analyzing system to set up property data base according to the characteristic amount of the stem in multiple stem images;
E: by sprawling smooth non-overlapping the putting of piece-rate system, utilize image capturing system to gather each constitutional diagram picture of pipe tobacco to be measured in pipe tobacco to be measured;
F: utilize image processing and analyzing system to look like to carry out pre-service to each constitutional diagram of pipe tobacco to be measured obtaining, remove interference and noise in each constitutional diagram picture of pipe tobacco to be measured;
G: pipe tobacco characteristic amount to be measured in each constitutional diagram picture of image processing and analyzing system-computed pipe tobacco to be measured, and with step D in the characteristic amount of stem in the property data base set up carry out relatedness computation, according to relatedness computation result, the stem component being blended in pipe tobacco is analyzed to identification;
H: image processing and analyzing system is sent to sorting system by analysis recognition result, sorts out the stem in pipe tobacco to be measured by sorting system;
I: weigh respectively the stem quality and the remaining ingredient quality that are sorted out by sorting system, and calculate the ratio of stem component in pipe tobacco to be measured.
In described step B, the scanning window that image processing and analyzing system adopts 5 * 5 pixels scans according to order from top to bottom, from left to right the stem image obtaining, calculate stem image average and variance Var in scanning window, if variance Var is greater than setting threshold T d, to this point, adopt Fast Median Filtering method to carry out smoothing processing, remove interference and noise in stem image.
In described step C, image processing and analyzing system is transformed into hsv color space by the stem image obtaining; In conjunction with Canny and Log edge detection operator, respectively the image of R, G, B, H, these six components of S, V is carried out to rim detection, record respectively the pixel variance yields V in pipe tobacco region in R, G, B, H, S, V component image r, V g, V b, V h, V s, V v; Then use gray level co-occurrence matrixes to calculate contrast, entropy, angle second moment and four textural characteristics values of correlativity in pipe tobacco region in stem image; Wherein, wherein, R component image is illustrated in RGB color space, and the R value of each pixel is constant, and G value and B value are zero; G component image is illustrated in RGB color space, and the G value of each pixel is constant, and R value and B value are zero; B component image is illustrated in RGB color space, and the B value of each pixel is constant, and R value and G value are zero; H component image is illustrated in hsv color space, and the H value of each pixel is constant, and S value and V value are zero; S component image is illustrated in hsv color space, and the S value of each pixel is constant, and H value and V value are zero; V component image is illustrated in hsv color space, and the V value of each pixel is constant, and H value and S value are zero; Characteristic amount described in step C comprises ten eigenwerts, is respectively the V in pipe tobacco region in stem image r, V g, V b, V h, V s, V vpixel variance yields on six components, and contrast, entropy, angle second moment and four textural characteristics values of correlativity in pipe tobacco region in stem image.
In described step D, image processing and analyzing system is calculated respectively the characteristic amount of stem in every stem image, and adds up the distribution range C of each eigenwert i(i=1,2 ..., 10), then the value of each scope is multiplied by corresponding scale-up factor e i(i=1,2 ..., 10), finally set up property data base T i=C ie i(i=1,2 ..., 10), wherein, inverse for dispersion degree.
In described step F, the scanning window that image processing and analyzing system adopts 5 * 5 pixels is to scanning according to order from top to bottom, from left to right in each constitutional diagram picture of the pipe tobacco to be measured obtaining, calculate average and variance Var in interior each constitutional diagram picture of pipe tobacco to be measured of scanning window, if variance Var is greater than setting threshold T d, to this point, adopt Fast Median Filtering method to carry out smoothing processing, remove interference and noise in each constitutional diagram picture of pipe tobacco to be measured.
In described step G, image processing and analyzing system is calculated respectively ten eigenwerts in pipe tobacco characteristic amount to be measured, and these ten eigenwerts are imported respectively in property data base, then image processing and analyzing system-computed pipe tobacco to be measured and degree of correlation stem, pipe tobacco to be measured with the computing formula of degree of correlation R stem be R = n &CenterDot; &Pi; i = 1 n S i 10 , Wherein S i = x i V &OverBar; i x i < V &OverBar; i V &OverBar; i x i x i &GreaterEqual; V &OverBar; i , n &Element; [ 1,10 ] , For the quantity in property data base critical field in ten eigenwerts of pipe tobacco to be measured; x ifor character pair value, average for this eigenwert in property data base; If degree of correlation R is more than or equal to degree of correlation threshold value T, judge that current pipe tobacco to be measured is stem; If degree of correlation R is less than degree of correlation threshold value T, judge that current pipe tobacco to be measured is not stem, wherein, degree of correlation threshold value T is the dispersion degree of character pair database t ∈ [0.25,0.75],
The present invention is based on computer vision technique, by the stem image acquisition and processing to one-component, obtain the characteristic amount of stem and set up property data base, by property data base analysis, identify the stem in polycomponent pipe tobacco and sort again, can avoid the impact of manual measurement on test result in existing method, eliminate personal error; The present invention is by gathering stem feature input database, finally by pipe tobacco to be measured one by one with database in the calculating of comparing of stem feature, finally sort out the stem component in pipe tobacco, test speed is fast, and other test datas such as area ratio, geomery parameter can be provided; Measuring process full automation is processed, and can improve efficiency, accuracy and the measuring accuracy of measurement, significantly reduces labor capacity.
Accompanying drawing explanation
Fig. 1 is schematic flow sheet of the present invention.
Embodiment
As shown in Figure 1, in the pipe tobacco based on computer vision of the present invention, stem ratio measuring method, is characterized in that, comprises the following steps:
A: by many smooth non-overlapping the putting of stem, then utilize image capturing system to gather respectively each stem image;
B: utilize image processing and analyzing system to carry out pre-service to multiple stem images that obtain, remove every interference and noise in stem image;
When carrying out pre-service and eliminate harmonic and white noise, the scanning window that image processing and analyzing system adopts 5 * 5 pixels scans according to order from top to bottom, from left to right the stem image obtaining, then calculate stem image average and variance Var in scanning window, if variance Var is greater than setting threshold T d, show that this place's pixel value has larger variation, to this point, adopt Fast Median Filtering method to carry out smoothing processing, remove interference and noise in stem image.Calculating to image average and variance Var belongs to the state of the art, carries out smoothing processing eliminate harmonic and white noise also belong to the state of the art by Fast Median Filtering method, does not repeat them here.
C: utilize image processing and analyzing system to obtain respectively the characteristics of image of stem in multiple stem images, then according to the characteristic amount of the box counting algorithm stem of stem;
When carrying out step C, first image processing and analyzing system is transformed into hsv color space by the stem image obtaining; In conjunction with Canny and Log edge detection operator, respectively the image of R, G, B, H, these six components of S, V is carried out to rim detection, record respectively the pixel variance yields V in pipe tobacco region in each component image r, V g, V b, V h, V s, V v; Then use gray level co-occurrence matrixes to calculate contrast, entropy, angle second moment and four textural characteristics values of correlativity in pipe tobacco region in stem image; Wherein, R component image is illustrated in RGB color space, and the R value of each pixel is constant, and G value and B value are zero; H component image is illustrated in hsv color space, and the H value of each pixel is constant, and S value and V value are zero; Other component image by that analogy.
Characteristic amount described in step C comprises ten eigenwerts, is respectively the pixel variance yields V in stem region in R, G, B, H, S, V component image r, V g, V b, V h, V s, V v, and contrast, entropy, angle second moment and four textural characteristics values of correlativity in pipe tobacco region in stem image.In step C, image is transformed into hsv color space, utilizes Canny and Log edge detection operator to carry out rim detection, use gray level co-occurrence matrixes calculating contrast, entropy, angle second moment and correlativity to be the state of the art image, do not repeat them here.
D: utilize image processing and analyzing system to set up property data base according to the characteristic amount of the stem in multiple stem images;
Image processing and analyzing system is calculated respectively the characteristic amount of stem in every stem image, and adds up the distribution range C of each eigenwert i(i=1,2 ..., 10), then the value of each scope is multiplied by corresponding scale-up factor e i(i=1,2 ..., 10), finally set up property data base T i=C ie i(i=1,2 ..., 10).Wherein, inverse for dispersion degree.
E: by sprawling smooth non-overlapping the putting of piece-rate system, utilize image capturing system to gather each constitutional diagram picture of pipe tobacco to be measured in pipe tobacco to be measured;
F: utilize method described in B to carry out pre-service to the image collecting, remove interference and noise in each constitutional diagram picture of pipe tobacco to be measured, detailed process repeats no more;
G: pipe tobacco characteristic amount to be measured in each constitutional diagram picture of image processing and analyzing system-computed pipe tobacco to be measured, and with step D in the characteristic amount of stem in the property data base set up carry out relatedness computation, according to relatedness computation result, the stem component being blended in pipe tobacco is analyzed to identification.
In step G, image processing and analyzing system is calculated respectively ten eigenwerts of pipe tobacco to be measured, i.e. the pixel variance yields V in pipe tobacco region in R, G, B, H, S, V component image r, V g, V b, V h, V s, V v, and contrast, entropy, angle second moment and the correlativity in pipe tobacco region in pipe tobacco image.And above-mentioned ten eigenwerts are imported respectively in property data base, then, by the degree of correlation of image processing and analyzing system-computed pipe tobacco to be measured and stem, the computing formula of the degree of correlation R of pipe tobacco to be measured and stem is:
wherein S i = x i V &OverBar; i x i < V &OverBar; i V &OverBar; i x i x i &GreaterEqual; V &OverBar; i . Wherein, n ∈ [1,10], represents the quantity (V in property data base in property data base critical field in 10 eigenwerts of pipe tobacco to be measured r, V g, V b, V h, V s, V v, contrast, entropy, this each self-corresponding distribution range of ten values of angle second moment be the critical field of this value); x irepresent characteristic of correspondence value, average for this eigenwert in property data base;
If degree of correlation R is more than or equal to degree of correlation threshold value T, judge that current pipe tobacco to be measured is stem; If degree of correlation R < is T, judge that current pipe tobacco to be measured is not stem.Wherein, in reality, detect in identifying the dispersion degree of degree of correlation threshold value T character pair database t ∈ [0.25,0.75], e icomputing method in step D, providing.Dispersion degree is larger, and the critical field of this property data base is larger, and corresponding T is less; Otherwise T is larger.
H: image processing and analyzing system is sent to sorting system by analysis recognition result, sorts out the stem in pipe tobacco to be measured by sorting system;
I: weigh respectively the stem quality and the remaining ingredient quality that are sorted out by sorting system, and calculate the ratio of stem component in pipe tobacco to be measured.
In the present invention, image capturing system comprises lighting device, imaging device and image capture software, and the effect of lighting device is to provide suitable illumination for stem and pipe tobacco to be measured, so that obtain clear real image; Lighting device can adopt the light-source systems such as the planar light source of even floodlighting, annular light source, emitting led array, backlight can be provided; Imaging device mainly comprises camera lens and camera two parts, and the effect of imaging device is to coordinate image capture software to obtain the image of stem and pipe tobacco to be measured; Image capture software can adopt existing various software on the market, as Motic2.0 image capture software; Image analysis processing system can adopt host computer, coordinates according to the software of conventional images Treatment Analysis technology establishment and realizes correlation function, as MATLAB image processing and analyzing software; Sprawl piece-rate system comprise feed belt, vibratory screening apparatus, shaking platform etc. can be by the mechanical hook-up of the smooth non-overlapping separated drawout of pipe tobacco to be measured or device combination, sorting system comprises device or the device combination that mechanical sorting machine, mechanical arm, malleation or negative pressure straw etc. can sort out the stem identifying and other pipe tobacco components.Above-mentioned each equipment and corresponding software all belong to existing product, do not repeat them here.
Below in conjunction with embodiment, the present invention will be further elaborated:
Embodiment 1
1) by 2 smooth non-overlapping being placed under high light LED illumination array of stem, the Motic2.0 image capture software by CCD camera and autofocus lens coupled computer end, collects 2 stem images;
2) utilize MATLAB image processing and analyzing software to carry out pre-service to obtain 2 stem images, remove every interference and noise in stem image;
3) computing machine obtains respectively the characteristics of image of stem in 2 stem images, then according to the characteristic amount of the box counting algorithm stem of stem;
4) computing machine is set up property data base according to the characteristic amount of the stem in 2 stem images;
5) by pipe tobacco to be measured by sprawling smooth non-overlapping being placed under high light LED illumination array of piece-rate system, the Motic2.0 image capture software by CCD camera and autofocus lens coupled computer end gathers each constitutional diagram picture of pipe tobacco to be measured;
6) utilize MATLAB image processing and analyzing software to look like to carry out pre-service to each constitutional diagram of pipe tobacco to be measured obtaining, remove interference and noise in each constitutional diagram picture of pipe tobacco to be measured;
7) computing machine calculates pipe tobacco characteristic amount to be measured in each constitutional diagram picture of pipe tobacco to be measured, and with step D in the characteristic amount of stem in the property data base set up carry out relatedness computation, according to relatedness computation result, the stem component being blended in pipe tobacco is analyzed to identification;
8) computing machine is sent to sorting system by analysis recognition result, by sorting system, sorts out the stem in pipe tobacco to be measured;
9) the stem quality sorting out that weighs with scale is 0.6g, remaining ingredient quality 1.4g, and in pipe tobacco, the ratio of stem component is 30%.
Embodiment 2
1) by 20 smooth non-overlapping being placed under planar light source of stem, the Motic2.0 image capture software by CCD camera and microspur tight shot coupled computer end, collects 20 stem images;
2) utilize MATLAB image processing and analyzing software to carry out pre-service to obtain 20 stem images, remove every interference and noise in stem image;
3) computing machine obtains respectively the characteristics of image of stem in 20 stem images, then according to the characteristic amount of the box counting algorithm stem of stem;
4) computing machine is set up property data base according to the characteristic amount of the stem in 20 stem images;
5) by pipe tobacco to be measured by sprawling smooth non-overlapping being placed under planar light source of piece-rate system, the Motic2.0 image capture software by CCD camera and microspur tight shot coupled computer end gathers each constitutional diagram picture of pipe tobacco to be measured;
6) utilize MATLAB image processing and analyzing software to look like to carry out pre-service to each constitutional diagram of pipe tobacco to be measured obtaining, remove interference and noise in each constitutional diagram picture of pipe tobacco to be measured;
7) computing machine calculates pipe tobacco characteristic amount to be measured in each constitutional diagram picture of pipe tobacco to be measured, and with step D in the characteristic amount of stem in the property data base set up carry out relatedness computation, according to relatedness computation result, the stem component being blended in pipe tobacco is analyzed to identification;
8) computing machine is sent to sorting system by analysis recognition result, by sorting system, sorts out the stem in pipe tobacco to be measured;
9) the stem quality 1.0g and the remaining ingredient quality 4.5g that weigh with scale and sort out, in pipe tobacco, the ratio of stem component is 18%.

Claims (6)

1. a stem ratio measuring method in the pipe tobacco based on computer vision, is characterized in that, comprises the following steps:
A: by many smooth non-overlapping the putting of stem, then utilize image capturing system to gather respectively each stem image;
B: utilize image processing and analyzing system to carry out pre-service to multiple stem images that obtain, remove every interference and noise in stem image;
C: utilize image processing and analyzing system to obtain respectively the characteristics of image of stem in multiple stem images, then according to the characteristic amount of the box counting algorithm stem of stem;
D: utilize image processing and analyzing system to set up property data base according to the characteristic amount of the stem in multiple stem images;
E: by sprawling smooth non-overlapping the putting of piece-rate system, utilize image capturing system to gather each constitutional diagram picture of pipe tobacco to be measured in pipe tobacco to be measured;
F: utilize image processing and analyzing system to look like to carry out pre-service to each constitutional diagram of pipe tobacco to be measured obtaining, remove interference and noise in each constitutional diagram picture of pipe tobacco to be measured;
G: pipe tobacco characteristic amount to be measured in each constitutional diagram picture of image processing and analyzing system-computed pipe tobacco to be measured, and with step D in the characteristic amount of stem in the property data base set up carry out relatedness computation, according to relatedness computation result, the stem component being blended in pipe tobacco is analyzed to identification;
H: image processing and analyzing system is sent to sorting system by analysis recognition result, sorts out the stem in pipe tobacco to be measured by sorting system;
I: weigh respectively the stem quality and the remaining ingredient quality that are sorted out by sorting system, and calculate the ratio of stem component in pipe tobacco to be measured.
2. stem ratio measuring method in the pipe tobacco based on computer vision according to claim 1, it is characterized in that: in described step B, the scanning window that image processing and analyzing system adopts 5 * 5 pixels scans according to order from top to bottom, from left to right the stem image obtaining, calculate stem image average and variance Var in scanning window, if variance Var is greater than setting threshold T d, to this point, adopt Fast Median Filtering method to carry out smoothing processing, remove interference and noise in stem image.
3. stem ratio measuring method in the pipe tobacco based on computer vision according to claim 2, is characterized in that: in described step C, image processing and analyzing system is transformed into hsv color space by the stem image obtaining; In conjunction with Canny and Log edge detection operator, respectively the image of R, G, B, H, these six components of S, V is carried out to rim detection, record respectively the pixel variance yields V in pipe tobacco region in R, G, B, H, S, V component image r, V g, V b, V h, V s, V v; Then use gray level co-occurrence matrixes to calculate contrast, entropy, angle second moment and four textural characteristics values of correlativity in pipe tobacco region in stem image; Wherein, wherein, R component image is illustrated in RGB color space, and the R value of each pixel is constant, and G value and B value are zero; G component image is illustrated in RGB color space, and the G value of each pixel is constant, and R value and B value are zero; B component image is illustrated in RGB color space, and the B value of each pixel is constant, and R value and G value are zero; H component image is illustrated in hsv color space, and the H value of each pixel is constant, and S value and V value are zero; S component image is illustrated in hsv color space, and the S value of each pixel is constant, and H value and V value are zero; V component image is illustrated in hsv color space, and the V value of each pixel is constant, and H value and S value are zero; Characteristic amount described in step C comprises ten eigenwerts, is respectively the V in pipe tobacco region in stem image r, V g, V b, V h, V s, V vpixel variance yields on six components, and contrast, entropy, angle second moment and four textural characteristics values of correlativity in pipe tobacco region in stem image.
4. stem ratio measuring method in the pipe tobacco based on computer vision according to claim 3, it is characterized in that: in described step D, image processing and analyzing system is calculated respectively the characteristic amount of stem in every stem image, and adds up the distribution range C of each eigenwert i(i=1,2 ..., 10), then the value of each scope is multiplied by corresponding scale-up factor e i(i=1,2 ..., 10), finally set up property data base T i=C ie i(i=1,2 ..., 10), wherein, inverse for dispersion degree.
5. stem ratio measuring method in the pipe tobacco based on computer vision according to claim 4, it is characterized in that: in described step F, the scanning window that image processing and analyzing system adopts 5 * 5 pixels is to scanning according to order from top to bottom, from left to right in each constitutional diagram picture of the pipe tobacco to be measured obtaining, calculate average and variance Var in interior each constitutional diagram picture of pipe tobacco to be measured of scanning window, if variance Var is greater than setting threshold T d, to this point, adopt Fast Median Filtering method to carry out smoothing processing, remove interference and noise in each constitutional diagram picture of pipe tobacco to be measured.
6. stem ratio measuring method in the pipe tobacco based on computer vision according to claim 5, it is characterized in that: in described step G, image processing and analyzing system is calculated respectively ten eigenwerts in pipe tobacco characteristic amount to be measured, and these ten eigenwerts are imported respectively in property data base, then the degree of correlation of image processing and analyzing system-computed pipe tobacco to be measured and stem, the computing formula of the degree of correlation R of pipe tobacco to be measured and stem is R = n &CenterDot; &Pi; i = 1 n S i 10 , Wherein S i = x i V &OverBar; i x i < V &OverBar; i V &OverBar; i x i x i &GreaterEqual; V &OverBar; i , n &Element; [ 1,10 ] , For the quantity in property data base critical field in ten eigenwerts of pipe tobacco to be measured; x ifor character pair value, average for this eigenwert in property data base; If degree of correlation R is more than or equal to degree of correlation threshold value T, judge that current pipe tobacco to be measured is stem; If degree of correlation R is less than degree of correlation threshold value T, judge that current pipe tobacco to be measured is not stem, wherein, degree of correlation threshold value T is the dispersion degree of character pair database t ∈ [0.25,0.75],
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CN109297854A (en) * 2018-09-20 2019-02-01 云南中烟工业有限责任公司 A kind of stem run in item silk mixes the rapid assay methods of ratio in fact
CN109307739A (en) * 2018-09-20 2019-02-05 云南中烟工业有限责任公司 The reconstituted tobacco silk of pipe tobacco mixes the method for ratio in fact in a kind of quick estimation cigarette
CN109307740A (en) * 2018-09-20 2019-02-05 云南中烟工业有限责任公司 The stem of pipe tobacco mixes the method for ratio in fact in a kind of quick estimation cigarette
CN109297854B (en) * 2018-09-20 2020-12-08 云南中烟工业有限责任公司 Method for rapidly measuring real mixing ratio of cut stems in strip running silks
CN109307739B (en) * 2018-09-20 2020-12-08 云南中烟工业有限责任公司 Method for rapidly estimating reconstituted tobacco shred blending proportion of tobacco shreds in cigarettes

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