Segmentation of the cut tobacco strands as an example for the extraction of the whole sample holder field. (a) and (b): the original images of cut tobacco strands and their segmentation results, respectively; (c) and (d): the images of cut tobacco strands and their segmentation results after having been processed with Mean-shift algorithm, respectively.

Segmentation of the cut tobacco strands as an example for the extraction of the whole sample holder field. (a) and (b): the original images of cut tobacco strands and their segmentation results, respectively; (c) and (d): the images of cut tobacco strands and their segmentation results after having been processed with Mean-shift algorithm, respectively.

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The width of cut tobacco strands is an important indicator for physical parameters as well as for the smoking quality. In some countries, cut width helps to distinguish fine-cut tobacco and pipe tobacco and thus differentiates taxation rate. A new method for rapid measurement of the width of cut tobacco strands was developed based on digital image...

Contexts in source publication

Context 1
... strands tested in this work were mainly yellow or brown, the background board was black. The originally collected and rectified image of the cut tobacco strands is shown in Figure 3 (a). Typically, when the strands' images needed to be segmented, the colored images were first converted into grayscale images. ...
Context 2
... there are several similar colors between the strands and the background due to pigmentation of the strands and imaging angles. These lead to relatively poor results of segmentation with noise and breakages as shown in Figure 3 (b). ...
Context 3
... this algorithm is applied, a minimum threshold value of pixels merged needs to be set, which depends on the contrast between targeted area and background area within the image. The processed images after applying the Mean-shift algorithm are shown in Figure 3 (c) and (d). It is clear that the Mean-shift algorithm exhibited better segmentation results and fitted well with the original shape of the tested tobacco strands. ...
Context 4
... addition, images in Figure 5 were compared to the original images of the strands of cut tobacco (Figure 3 (a)) to examine the effect of extraction. The overlapping images are shown in Figure 6. ...

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Citations

... Tong et al. 11 designed an online machine vision measurement device that integrates technologies such as simulating manual grasping, visual image measurement, and automatic material feeding, when compared with traditional offline weighing and manual measurement methods, it showed significant advantages in automation and high precision. Liu et al. 12 established a tobacco strand width measurement method characterized by digital image processing technology, through the mean shift algorithm, the segmentation and extraction of tobacco strand width are realized, offering rapid response and high precision. However, the image-based measurement methods used by Tong and Liu involve converting image detection data into microscopic measurement dimensions of tobacco strands, and the current national detection standard YC/T 178-2003 13 explicitly points out that there is a significant difference between the calculation method that uses tobacco cut mass on different mesh sizes to measure size proportion of cut tobacco. ...
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