You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Mapping without the sun
Zhang, Jixian

six types of foreign fibers was calculated by the following
A 2=7-ZISc-g,| (5)
o , =1
where / = 1,2 6, and g ii g c respectively represented
gray level of six types of foreign fibers and cotton. Table 2
shows Ag value in different images. And it is clearly that the
Ag value of fusion image is maximal.
Ag value
visible region image
405nm wavelength image
850nm wavelength image
Fusion image
Table 2 ^ value in 4 different images
In order to separate these foreign fibers from the background of
cotton, an image segmentation method based on local contrast
variations rather than using a predefined threshold was
developed. Thus, the sorting of foreign fibers could be
conducted using a hierarchical segmentation method. This
would consist first in segmenting all the foreign fibers and then
identifying each type of foreign fibers by means of dedicated
image analysis algorithms. Before the image segmentation
processing, image enhancement techniques were used for the
fusion image so that the image features of foreign fibers are
more obvious. Single-point processing method based on the
intensity of single pixel was applied to enhance the image
contrast of fusion image.
The final step in the image processing was to use a dynamic
thresholding process to set all pixels of foreign fibers to zero,
from then on, only the zero pixel in each image were
considered. After the dynamic thresholding process, all the
foreign fibers were clearly identified in the binary image. Fig.4
shows the enhancement image and binary image of foreign
Fig.4 image segmentation processing: (a) enhancement image, (b) binary image
After the image segmentation processing, the six types of
foreign fibers can be identified as shown in the far right images
in Fig.4. In these binary images, all the foreign fibers are
clearly identified and can be easily separated from the cotton. 6
The study was conducted to determine the feasibility of a novel
multiwavelength imaging system for detecting foreign fibers. A
spectral imaging experiment was set up to select the most
suited wavelength bands to sort foreign fibers. In particular, the
separability of six types foreign fibers including hair, knitting,
jute, plastic, wool, and bristle was studied.
Through the experiment analysis, the OOP including optimal
wavelength and optimal light energy for each specific type of
foreign fibers was determined. Wavelengths were selected by
gray level discrimination analysis of spectral image data. The
wavelength 405nm and 850nm were chosen from the seven
bands for use in the imaging system. An image fusion
algorithm based on wavelet-transform for enhancing the image
feature and acquiring complete information is proposed also in
this paper. Then, image segmentation algorithms based on local
contrast variations was considered when attempting to separate
them from the cotton. This result suggests that use of
multiwavelength imaging technique for detection of foreign
fibers in cotton in a commercial setting may be feasible.
[1] Michael C G, “Agriculture marketing services-the
classification of cotton,” Cotton Ginning Engineering
Conference, Washington, USA, pp.92-96, 1999
[2] H M Strolz,
International Cott
[3] P Tantaswadi, J
automated visual in
using color Isodisc
Processing, vol.27,
[4] Tae Jin Kang ,
the trash and colo
neural network,” Te
[5] B Xu, C Fang a
cotton trash and ci
no. 12, pp.881-890,
[6] D veit, J bergn
tool to improve m
Clothing Sci and Te
[7] J.S Church, J.
contamination in
spectroscopy, vol.2i
[8] Wu RongHi
identification of dif
of Forensic Medicir
[9] Ajay. Pai,
contamination via
IEEE Trans on Im