Presentation 1 Robin

8/8/2019 Presentation 1 Robin

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Presented to: Presented by:Prof. Suneet Tuli Robin das

(2010TTE3677)Dept. of Textile Technology

Indian Institute Of Technology, New Delhi


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Before cotton fiber can be spun, the raw cotton must be sorted to remove any

foreign particle sand fibers. While foreign particles can be clearly distinguishedfrom the raw cotton by color, contrast, and structure.

Foreign fibers such as polypropylene PP or polyethylene PE filmsare often light andtransparent, making them difficult to detect using conventional foreign fiberseparators. Even very low content of foreign fibers in cotton, such contaminants

Leads to

Often appear as a discoloration in the fabric, reducing its value when they endup in finished cotton products.

And this may lead to great economic loss for cotton textile enterprises

1. Introduction


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P rocess

Images of cotton layer are first acquired throughcamera.manipulated to enhance contrast.Images are segmented to distinguish foreign

fibers.Processed images are transmitted to the solenoidvalves,Which switch the high-pressure compressed air onor off to blow the foreign fibers off the cotton tufts.


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Image segme tati meth

M a se e tati et s a e ee e el e s c as , se e tati ase

F zz C ea s a its aria ts.M

ea s ift filters.A li ear iff si .


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DisadvantagesT his device suffers from fundamental limitation of P such as entral processing units PU- long-time overload, and it will frequently lead toundetected foreign fibers in real-time inspection.


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A modern machine-vision-based

foreign fiber detection systemM ainly consist of

A specialized lens with lateral chromatic aberrationcorrection.Ultraviolet light illumination for transparent foreignfiber detection.High-performance digital signal processor .

field-programmable gate array was to perform allthe complex computations of image acquisition andprocessing.


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The System1.sensors

Photo sensors are relatively cheap sensors thatare arranged in-line and detect differences inbrightness in the passing flow of fibrous tufts.

Color sensors, or 1-CCD charged coupledevice cameras, are line-scan cameras witha single CCD chip.

Much more effective, although moreexpensive, are 3-CCD cameras,with threeCCD chips


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In CCD, t e electric a l field a t different p a rts of t e surf a ce is controlled by a na rra y or a trix of electrodes; c a lled t e a tes.

W en li t or p otons of i enou

ener y strike t e surf a ce, electrons

a re usu a lly liber a ted fro t e surf a ce.


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In a CCD for c a pturin i ag es, t ere is a p oto a ctive re g ion a nd a tra ns ission re g ion a de out of a s ift re g ister.

I ag e is projected t rou gh a lens onto t h e

ca p a citor a rr a y c a usin g ea ch ca p a citor to

a ccu ul a te a n electric c ha rg e proportion a l

to t h e li gh t intensity a t tha t loc a tion.

A control circuit c a uses e a ch ca p a citor to

tra nsfer its contents to its nei gh bor a s s h ift

re g ister.

Th e l a st c a p a citor in t h e a rr a y du ps its c ha rg e into a cha rg e am plifier, w h ich converts t h e c ha rg e into a volt ag e.


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Inside t h e di g ita l c am er a , a t first a n IR blockin g filter is t h ere, followed by a color sep a ra tion filter c a lled B a yer Ma sk.

And fin a lly it f a lls on t h e sensor w h ich

turns it into electric a l pulses by

electron to volt ag e conversion.


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2. Illu mination

Polarized transmitted light is theideal system for detectingtransparent and Semitransparentobjects.

Making the foreign plastic fibersA ppear colored.

Polarized reflected light and the

corresponding camera filters,differences In surface luster of foreign objects


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Image Acquisition and Processing Controller

H igh-performance embedded

controller, which features aT MS320DM648 DSP

A nd an X 2S300E-7PQ208PG A .With its 8800 MIPS processor

ive configurable video ports.A nd 1GBps total system

bandwidth.

The controller performs all thecomplex computations of image

acquisition and processing in veryshort period of time


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Adv a nt ag es

M aterial Presentation

The presentation of the fibrous material to the sensors alsoaffects the performance of foreign fiber separators.

Tuft flow is monitor on the rectangular chute

The gentle treatment of cotton fibers, which are notmechanically stressed.

The minimal loss of good fibers during removal.

Accurately detecting the position of foreign objects .

Dis a dv a nt ag e

The downstream separation nozzles must be activatedFor a longer period of time


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Foreign fiber blowing

T he designed air scavenging system and aseparation device provided with at leasteight compressed air nozzles which are

perpendicular to pneumatic cotton tufts

conveying In a direction of material flow.

Compressed air tank.

Solenoid valves.


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F oreign F iber Detection M ethods

Image Segmentation Algorithm Based on Fast Wavelet Transform

In the original image, cotton can be treated as background, while foreignfibers are expressed as foreground.

When digital images are to be viewed or processed at multiple resolutions, thediscrete wavelet transform DWT is the mathematical tool of choice.

The fast wavelet transform FWT is adopted to achieve the edge featureextraction.

It is defined as

where h( ) and h( ) the expansion coefficients are called scaling and wavelet vectors,


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Through the 2D wavelet decomposition, symlets wavelet in this paper, the original cottonimage w (j+ 1 ,m, n)is transformed to 4 parts, w D ( j, m, n) denotes the detail component indiagonal orientation , w v ( j , m, n) denotes the detail component in vertical orientation,w H ( j, m, n) denotes the detail component in horizontal orientation, w ( j, m, n) denotes themorphology component. In three detail components, high frequency features are enhancedand the contrast is indicated by the wavelet coefficients.

The 2D fast wavelet transform FWT filter bank. Each pass generates one DWT scale. In thefirst iteration w (j+ 1 ,m, n)= f (x , y)


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Im ages seg m entation:

(a) Original image,& b,c,d,e,f are the segmented image at different time


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O rig in a l imag es obt a ined by t he I mag e AcquisitionSyste m . ( a) Color i mag e of pl a stic fil m , (b )color i mag eof clot h piece, (c ) color i mag e of ha ir, (d )g ra y i mag eof polypropylene twine wit h bri gh tener

Bina ry i mag es output fro m th e inspection a lg orit hm .

(a) P la stic fil m , (b ) clot h , (c ) ha ir (d )polypropylene twine

Processed images using color image segmentation

algorithm:(a) original image;& b,c is processed image


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Images segmentation: a original image; b ; c proposedalgorithm

Example of the labeling algorithm (The corearea is shown with the sparse dotted linebox; the expansion area is shown with the densedotted line box.)

Example of the equivalent length


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References

1 F. M. Shofner and G. F. Williams, Evolution of the microdust and trash monitor forcottonclassification, Te xtile Researc h Jou r na l, vol. 56 , no. 2 , pp . 150156 , 1986 .2 TW, Optimizing Yarn quality, Special Report, B illian Publishing, 2009,http://www.textileworld.com/Articles/2009/December/Features/Optimizing Yarn Quality.html.3 K. Kuratani, T. Fujii, M. Tanaka, A. Daito, and T. Murosaki, Foreign materialevaluation equipment:collecting method, in Proceed ing s of the SICE Annu a l Conf ere nce , vol. 1 , pp . 376 379 , 2003 .4 A. Daito, T. Murosaki, H. Ito, K. Kuratani, and T. Fujii, Foreign material evaluationequipment:measurement method, in Pr oceed ing s of the SICE Annu a l Conf ere nce , vol. 1 , pp . 372375 , 2003 .

5 Z. Jiao, L. Song, and X. Wang, Realization of foreign fiber detecting algorithmbased on ADSP BF533,in Proceed ing s of the 9 th Int er na tion a l Conf ere nce on Elec t ron ic Meas ure me nt a nd Ins t r ume nt s( ICEMI 09) , pp . 1-9391-942 , Be ijing , China , Augu st 2009 .


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6 W. He, L. Han, and X. Zhang, Study on characteristics analysis and recognition for infraredabsorption spectrum of foreign fibers in cotton, in Pr oceed ing s of the IEEE Int er na tion a l Conf ere nceon Au t oma tion a nd Logistics ( ICAL 08) , pp . 397400 , Se pt e mb er 2008 .7 J.-S. Kwon, J.-M. Lee, and W.-Y. Kim, Real-time detection of foreign objects using X-rayimagingfor dry food manufacturing line, in Pr oceed ing s of the 12 th IEEE Int er na tion a l Sy mpo s ium onCon sumerElec t ronics ( ISCE 08) , pp . 14 , Ap r il 2008 .8 J. C. Bezdek, A convergence theorem for the fuzzy ISODARA clustering algorithms,IEEETra nsac tion s on Pa tt er n Ana ly sis a nd Mac hine Int e llig e nce , vol. 2 , no. 1 , pp . 18 , 1980 .9 S. R. Kannan, A new segmentation system for brain MR images based on fuzzy techniques,App lied Sof t Co mpu ting Jou r na l, vol. 8 , no. 4 , pp . 15991606 , 2008 .10 L. Ma and R. C. Staunton, A modified fuzzy C-means image segmentation algorithm foruse with

uneven illumination patterns, Pa tt er n Rec ogn ition , vol. 40 , no. 11 , pp . 30053011 , 2007 .11 D. Comaniciu and P. Meet, Mean shift analysis and applications, in Proceed ing s of the 7 th IEEEInt er na tion a l Conf ere nce on Co mpu t er V is ion ( ICCV 99) , vol. 2 , pp . 11971203 , Kerk y ra , Greece ,September 1999.


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Presentation 1 Robin