A method for detecting the distribution of cigar tobacco leaves

Abstract

A method for detecting the distribution of tobacco leaves in cigars is characterized by: digitizing the three-dimensional structure of the cigar through industrial CT scanning; obtaining a two-dimensional image of the internal tobacco leaf distribution using filtered back projection technology; performing noise filtering on the two-dimensional image to remove background noise; and then obtaining a three-dimensional reconstruction model of the cigar through three-dimensional reconstruction and interpolation techniques. This model reflects the internal tobacco leaf distribution state of the cigar. The most significant feature of this invention is its ability to detect the tobacco leaf distribution state of cigars, filling a current technological gap in the detection of cigar tobacco leaf distribution.

Description

Technical Field

[0001] This invention relates to the field of cigar product testing technology, and in particular to a method for detecting the distribution of cigar tobacco leaves. Background Technology

[0002] A cigar is a special tobacco product consisting of three parts: the filler, binder, and wrapper. Traditional cigars are typically handmade cigars, with the entire production process done by hand. Machine-made cigars use machines to make the filler, and the binder and wrapper are then rolled by hand or machine. Regardless of whether it's a traditional or machine-made cigar, the rolling process is the same. First, the filler tobacco leaves are rolled into a specific size and shape using binder tobacco leaves and then shaped. Then, the wrapper tobacco leaves are used to completely wrap the tobacco blank at a specific angle and number of turns to form the cigar. The filler, binder, and wrapper are made of tobacco or materials containing tobacco components. Usually, the grade and chemical composition of the tobacco leaves in these three components are inconsistent, leading to differences in the smoke composition and sensory quality produced after combustion. Therefore, during combustion, different proportions of the three components in the burning cross-section result in different proportions of tobacco grade in the burning cross-section, ultimately producing different sensory experiences. To ensure consistent sensory experience when smoking cigars, the distribution ratio of the three components in the cross-section of the cigar must be stable, meaning the proportion of different types of tobacco leaves in each cross-section must remain constant. However, the field of testing for the distribution of tobacco leaves in cigars is currently lacking. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies and provide a method for detecting the distribution of tobacco leaves in cigars. This invention achieves digitization of the three-dimensional structure of cigars through industrial CT scanning. A two-dimensional image of the tobacco leaf distribution inside the cigar is obtained using filtered back projection technology. This two-dimensional image is then subjected to noise filtering to remove background noise. Finally, a three-dimensional reconstruction model of the cigar is obtained through three-dimensional reconstruction and interpolation techniques. This model reflects the distribution of tobacco leaves inside the cigar. The specific steps are as follows:

[0004] 1. Place the cigarette to be tested on the stage of the industrial CT equipment, keep it vertical with clamps and ensure that the tobacco leaf is within the CT scanning range;

[0005] 2. Start the CT scanning device to scan the cigarette. The digital flat panel detector will transmit and save the received signals to the computer.

[0006] 3. The data processing system in the computer uses the Filtered Back Projection (FDK) algorithm to process the acquired data and obtain a two-dimensional image of the distribution of tobacco leaves inside the cigar;

[0007] 4. The image processing system in the computer performs operations such as image matching, image smoothing, and image enhancement on the 2D CT image sequence to facilitate edge extraction and image segmentation; it interpolates images between adjacent 2D CT images to improve the accuracy of the 3D reconstruction model; finally, the image processing system reconstructs the 3D structural model.

[0008] 5. Using an image processing system, each complete and individual tobacco leaf is identified in the 3D reconstruction model, and...

[0009] Different tobacco leaves are labeled with different colors, the volume of each tobacco leaf is counted, and the total volume of all tobacco leaves in the whole cigar and the percentage of each tobacco leaf volume in the total tobacco leaf volume are calculated. This allows us to evaluate whether the components of the cigar being tested meet our expectations.

[0010] 6. Take n two-dimensional images of cigar cross sections at equal intervals along the axis of the three-dimensional reconstruction model, count the area of ​​different tobacco leaves on the cross section respectively, and calculate the total area of ​​all tobacco leaves on the cross section and the percentage of each tobacco leaf area to the total tobacco leaf area.

[0011] 7. Compare the calculated percentage of different tobacco leaf areas in the n cross-sectional two-dimensional images of cigars. Plot the trend of the percentage of different tobacco leaf areas along the cigar axis, with the number of specific cross-sections of the cigar cross-section as the horizontal axis and the percentage of different tobacco leaf areas in the cross-section to the total tobacco leaf area as the vertical axis, so as to evaluate the distribution of tobacco leaves in cigars.

[0012] The advantages of this invention are: it provides a method for detecting the distribution of cigar tobacco leaves, thereby evaluating the distribution status of cigar tobacco leaves and filling the current gap in the detection technology for the distribution of cigar tobacco leaves. Attached Figure Description

[0013] Figure 1 This is a flowchart of the cigar tobacco leaf distribution detection method provided by the present invention.

[0014] Figure 2 This is a diagram of a cone-beam scanning structure with a planar detector.

[0015] In the diagram: γ is the angle between the ray and the central ray, β is the angle formed by the central ray and the y-axis, called the projection angle, and κ is the cone angle in the Z-axis direction of the cone beam.

[0016] In the figure, the ab coordinate system is the coordinate system on the virtual detector. In this patent, a, b and a(x,y,β) and b(x,y,z,β) are the same, all representing the coordinate information on the virtual detector. Among them, x, y, z and β represent the expression factors of the ab coordinate system in the xyz coordinate system, that is, (a,b) can be converted to (x,y,z) and express the same spatial position information in different coordinate systems.

[0017] Figure 3 This is a schematic diagram showing the distribution of different tobacco leaves in a cross-section of a cigar. (Different lines represent different tobacco leaves.)

[0018] Figure 4 This is a schematic diagram illustrating the distribution trend of different tobacco leaf area proportions along the axial direction of a cigar cross-section (as expected). (This diagram is for illustrative purposes only and does not represent actual cigar data.)

[0019] Figure 5 This is a schematic diagram illustrating the distribution trend of different tobacco leaf area proportions along the axial direction of a cigar cross-section (which does not meet expectations). (This diagram is for illustrative purposes only and does not represent actual cigar data.) Detailed Implementation

[0020] 1. Place the cigar to be tested on the stage of the industrial CT equipment, keep it vertical with clamps and ensure that the tobacco leaves are within the CT scanning range; the cigar sample to be tested needs to be placed in the equilibration chamber for temperature and humidity equilibration before the experiment. The specific values ​​are: temperature 22℃, relative humidity 65%, equilibration time more than 24 hours.

[0021] 2. Start the CT scanning device to scan the cigar. The digital flat panel detector transmits and saves the received signals to the computer. Before scanning the cigarette, the industrial CT equipment needs to be pre-set with parameters, mainly determining the image size (2048x2048) and X-ray source tube voltage (100V) specifically for cigarette structure scanning.

[0022] kV, X-ray source tube current 70uA, scanning thickness 0.004mm, scanning interval 0.004mm, CT scanning mode cone beam scanning, CT scanning mode Normal scanning.

[0023] 3. The data processing system in the computer uses the Filtered Back Projection (FDK) algorithm to process the acquired data, obtaining a two-dimensional image of the distribution of tobacco leaves inside the cigar. The FDK algorithm mainly includes several steps: pre-weighting of the projected data, one-dimensional filtering, and back projection.

[0024] (1) First, the projection data is weighted using a function similar to cosine, and the distance and angle difference between the voxel and the source point are appropriately corrected.

[0025] (2) Then, perform one-dimensional filtering in the horizontal direction on the projection data of different projection angles;

[0026] (3) Perform a weighted back projection of the filtered data using a cone-beam scanner. The weight function in the back projection depends on the distance from the reconstructed point to the focal point. See the attached diagram for a cone-beam scanning structure with a planar detector. Figure 2 .

[0027] The FDK reconstruction algorithm for flat panel detectors can be expressed by the following formula:

[0028]

[0029]

[0030] Where R represents the radius of rotation. This represents the filtered projection data, where g(a) is the filtering function, and U(x,y,β) represents the distance of the reconstructed pixel in the xy plane to the X-ray source. Here, a and b represent the coordinates on the virtual detector.

[0031]

[0032] a(x,y,β) represents the horizontal position of the corresponding ray on the virtual detector, and the corresponding vertical position can be represented as...

[0033]

[0034] The weighted function can be decomposed into the following expression:

[0035]

[0036] Z is the Z-axis coordinate of the point to be reconstructed. Similarly, we can obtain the length of U based on geometric relationships.

[0037] U(x,y,β)=R+xcosβ+ysinβ

[0038] The weighting factor U is similar to the weighting factor in the two-dimensional filtering back projection algorithm. Geometrically, it is related to the projection of the line connecting the ray source point and the reconstructed image onto the intermediate ray.

[0039] 4. The image processing system in the computer performs image matching, image smoothing, and image enhancement on the 2D CT image sequence to facilitate edge extraction and image segmentation; it also interpolates images between adjacent 2D CT images to improve the accuracy of the 3D reconstruction model; finally, the image processing system reconstructs the 3D structural model. In CT scan images, the spacing in the X and Y directions is equal during sampling, while the spacing in the Z direction differs significantly. Therefore, linear interpolation is performed in the Z direction.

[0040] The calculation formula is:

[0041]

[0042] In the formula, P1 and P2 are the CT values ​​of corresponding points in adjacent CT images, a1 and a2 are the Z-axis distances of the two corresponding points from the interpolation point, and P is the CT value of the interpolation point.

[0043] Multifractal spectrum technology is used to extract the internal and contour features of each cigarette CT image. The images are then analyzed using their visual appeal. The range of spectral values ​​in the multifractal spectrum theory is set, and the edges of the cigarette CT images are detected and extracted. The contour of the three-dimensional image is drawn using the edge information extracted from each cigarette CT image. The original cigarette CT image and the contour image extracted by using linear interpolation are then integrated to form a three-dimensional reconstruction model.

[0044] 5. Using an image processing system, each complete and independent tobacco leaf is identified in the 3D reconstruction model, and different tobacco leaves are marked with different colors. The volume of each tobacco leaf is counted, and the total volume of all tobacco leaves in the whole cigar and the percentage of each tobacco leaf volume in the total tobacco leaf volume are calculated. By comparing with the actual situation, it is possible to evaluate whether the composition of the cigar under test meets the expectations.

[0045] 6. Take n two-dimensional images of cigar cross sections at equal intervals along the axis of the three-dimensional reconstruction model, count the area of ​​different tobacco leaves on the cross section respectively, and calculate the total area of ​​all tobacco leaves on the cross section and the percentage of each tobacco leaf area to the total tobacco leaf area.

[0046] 7. Compare the calculated area percentages of different tobacco leaves in the n cross-sectional two-dimensional images of cigars. Plot the percentage of different tobacco leaf areas along the axial direction of the cigar along the x-axis, using the number of specific cross-sections in the cigar cross-section as the horizontal axis and the percentage of different tobacco leaf areas in the cross-sections to the total tobacco leaf area as the vertical axis. This will help evaluate the distribution of tobacco leaves in the cigar. In the curves showing the area percentage of each tobacco leaf along different axial directions, the more parallel the curve is to the x-axis, the more stable the axial tobacco leaf distribution, indicating more stable cigar smoke quality and that the cigar quality meets expectations. (Appendix) Figure 4 The cigar in question is made from four tobacco leaves. Observing the cross-sectional area ratio curves of different tobacco leaves, it was found that the area ratio of each tobacco leaf in different cross-sections is basically consistent, and the curve trend is approximately a straight line parallel to the X-axis. Therefore, the smoke quality of this cigar is stable and meets expectations. (Attached) Figure 5The cigars in the article are also made of four tobacco leaves. Observing the cross-sectional area ratio curves of different tobacco leaves, it was found that the distribution of different tobacco leaves is relatively stable in the middle section of the cigar, but the cross-sectional area ratio of different tobacco leaves changes abruptly at the head and tail of the cigar. This will cause the quality of the smoke during the cigar smoking process to change and not meet expectations.

Claims

1. A method for detecting the distribution of cigar tobacco leaves, characterized in that: The process involves digitizing the three-dimensional structure of cigars using industrial CT scanning. A two-dimensional image of the tobacco leaf distribution inside the cigar is obtained using filtered back projection technology. This image is then subjected to noise filtering to remove background noise. Finally, a three-dimensional reconstruction model of the cigar is obtained using 3D reconstruction and interpolation techniques. This model reflects the distribution of tobacco leaves inside the cigar. The specific steps are as follows: (1) Place the cigar to be tested on the stage of the industrial CT equipment, keep it vertical with clamps and ensure that the tobacco leaf is within the CT scanning range; (2) Start the CT scanning device to scan the cigarette. The digital flat panel detector will transmit and save the received signal to the computer. (3) The data processing system in the computer uses a filtering back projection algorithm to process the acquired data and obtain a two-dimensional image of the distribution of tobacco leaves inside the cigar; (4) The image processing system in the computer performs image matching, image smoothing and image enhancement operations on the tomographic two-dimensional CT image sequence in order to perform edge extraction and image segmentation; interpolation is performed between adjacent two-dimensional CT images to improve the accuracy of the three-dimensional reconstruction model; finally, the three-dimensional structural model is reconstructed using the image processing system. (5) Use an image processing system to identify each complete and independent tobacco leaf in the three-dimensional reconstruction model, and mark different tobacco leaves with different colors. Calculate the volume of each tobacco leaf, and calculate the total volume of all tobacco leaves in the whole cigar and the percentage of each tobacco leaf volume in the total tobacco leaf volume. Then, it can be evaluated whether the components of the cigar to be tested meet the expectations. (6) Take n two-dimensional images of cigar cross sections at equal intervals along the axis of the three-dimensional reconstruction model, count the area of ​​different tobacco leaves on the cross section respectively, and obtain the total area of ​​all tobacco leaves on the cross section and the percentage of each tobacco leaf area to the total tobacco leaf area by calculation; (7) Compare the calculated percentage of different tobacco leaf areas in the two-dimensional images of the cross-section of the cigar. Using the number of specific cross sections of the two-dimensional image of the cross-section of the cigar as the horizontal axis and the percentage of different tobacco leaf areas on the cross section to the total tobacco leaf area as the vertical axis, draw a trend diagram of the change of different tobacco leaf areas on the cross section along the axial direction of the cigar tobacco, so as to evaluate the distribution status of the tobacco leaves in the cigar.

2. The method for detecting the distribution of cigar tobacco leaves according to claim 1, characterized in that: In step (3), the filtering back projection algorithm includes the following steps: (1) First, the projection data is weighted using a function similar to cosine, and the distance and angle difference between the voxels and the source point are appropriately corrected; (2) Then, one-dimensional filtering is performed on the projection data at different projection angles in the horizontal direction; (3) Perform a weighted back projection of the filtered data using a cone beam. The weight function in the back projection depends on the distance from the reconstructed point to the focal point. The FDK reconstruction algorithm for flat panel detectors can be expressed by the following formula: ； ； Where R represents the radius of rotation. This represents the filtered projection data, where g(a) is the filtering function. () represents the distance of the reconstructed pixel in the xy plane to the X-ray source; ； This represents the horizontal position of the corresponding ray on the virtual detector, and the corresponding vertical position can be represented as... ； The weighted function can be decomposed into the following expression: ； Z is the Z-axis coordinate of the point to be reconstructed. Similarly, we can obtain the length of U based on geometric relationships. ； The weighting factor U is similar to the weighting factor in the two-dimensional filtering back projection algorithm. Geometrically, it is related to the projection of the line connecting the ray source point and the reconstructed image onto the intermediate ray.

3. The method for detecting the distribution of cigar tobacco leaves according to claim 1, characterized in that: The specific process of step (4) is as follows: In the CT scan image, the spacing in the X and Y directions is equal during the sampling process, and the spacing in the Z direction has a large difference. Therefore, a linear interpolation is performed in the Z direction, and the calculation formula is: ； In the formula, P1 and P2 are the CT values ​​of corresponding points in adjacent CT images, a1 and a2 are the Z-axis distances of the two corresponding points from the interpolation point, and P is the CT value of the interpolation point.

4. The method for detecting the distribution of cigar tobacco leaves according to claim 1, characterized in that: In step (7), the X-axis is the position coordinate of its cross-section on the axis, and the Y-axis is the area ratio of the tobacco leaf on the cross-section. The distribution curve of the area ratio of different tobacco leaves in the axial direction is obtained to represent the distribution state of cigar tobacco leaves. In the cross-sectional tobacco leaf area ratio curve of different tobacco leaves in the axial direction, the more parallel the tobacco leaf area ratio curve is to the X-axis, the more uniform the tobacco leaf distribution is, and the more stable the cigar smoke quality is.

Images